A study on optimal motion for a robot manipulator amid obstacles

International Nuclear Information System (INIS)

Park, Jong Keun

1997-01-01

Optimal motion for a robot manipulator is obtained by nonlinear programming. The objective of optimal motion is minimizing energy consumption of manipulator arm with fixed traveling time in the presence of obstacles. The geometric path is not predetermined. The total trajectory is described in terms of cubic B-spline polynomials and the coefficients of them are obtained to minimize a specific performance index. Obstacle avoidance is performed by the method that the square sum of penetration growth distances between every obstacles and robot links is included in the performance index with appropriate weighting coefficient. In all examples tested here, the solutions were converged to unique optimal trajectories from different initial ones. The optimal geometric path obtained in this research can be used in minimum time trajectory planning. (author)

Helicopter trajectory planning using optimal control theory

Science.gov (United States)

Menon, P. K. A.; Cheng, V. H. L.; Kim, E.

1988-01-01

A methodology for optimal trajectory planning, useful in the nap-of-the-earth guidance of helicopters, is presented. This approach uses an adjoint-control transformation along with a one-dimensional search scheme for generating the optimal trajectories. In addition to being useful for helicopter nap-of-the-earth guidance, the trajectory planning solution is of interest in several other contexts, such as robotic vehicle guidance and terrain-following guidance for cruise missiles and aircraft. A distinguishing feature of the present research is that the terrain constraint and the threat envelopes are incorporated in the equations of motion. Second-order necessary conditions are examined.

Hierarchical Motion Planning for Autonomous Aerial and Terrestrial Vehicles

Science.gov (United States)

Cowlagi, Raghvendra V.

Autonomous mobile robots---both aerial and terrestrial vehicles---have gained immense importance due to the broad spectrum of their potential military and civilian applications. One of the indispensable requirements for the autonomy of a mobile vehicle is the vehicle's capability of planning and executing its motion, that is, finding appropriate control inputs for the vehicle such that the resulting vehicle motion satisfies the requirements of the vehicular task. The motion planning and control problem is inherently complex because it involves two disparate sub-problems: (1) satisfaction of the vehicular task requirements, which requires tools from combinatorics and/or formal methods, and (2) design of the vehicle control laws, which requires tools from dynamical systems and control theory. Accordingly, this problem is usually decomposed and solved over two levels of hierarchy. The higher level, called the geometric path planning level, finds a geometric path that satisfies the vehicular task requirements, e.g., obstacle avoidance. The lower level, called the trajectory planning level, involves sufficient smoothening of this geometric path followed by a suitable time parametrization to obtain a reference trajectory for the vehicle. Although simple and efficient, such hierarchical decomposition suffers a serious drawback: the geometric path planner has no information of the kinematical and dynamical constraints of the vehicle. Consequently, the geometric planner may produce paths that the trajectory planner cannot transform into a feasible reference trajectory. Two main ideas appear in the literature to remedy this problem: (a) randomized sampling-based planning, which eliminates the geometric planner altogether by planning in the vehicle state space, and (b) geometric planning supported by feedback control laws. The former class of methods suffer from a lack of optimality of the resultant trajectory, while the latter class of methods makes a restrictive assumption

TU-AB-BRB-02: Stochastic Programming Methods for Handling Uncertainty and Motion in IMRT Planning

Energy Technology Data Exchange (ETDEWEB)

Unkelbach, J. [Massachusetts General Hospital (United States)

2015-06-15

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV-to-CTV and CTV-to-PTV margins during the planning process to design a PTV-conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter- and intra-fractional positional variations, tissue motions, and initial contouring uncertainties. Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage-based planning objectives. The treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP-evaluation for the underlying CTVs and OARs to design an optimal treated volume. This symposium will describe CP-evaluation methods as well as various robust planning techniques including use of probability-weighted dose distributions, probability-weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter-fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra-fractional organ motion. Clinical examples will be used to inter-compare robust and margin-based planning, highlighting advantages of robust-plans in terms of target and normal tissue coverage. Robust-planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning. Learning Objectives: To understand

SU-F-J-133: Adaptive Radiation Therapy with a Four-Dimensional Dose Calculation Algorithm That Optimizes Dose Distribution Considering Breathing Motion

Energy Technology Data Exchange (ETDEWEB)

Ali, I; Algan, O; Ahmad, S [University of Oklahoma Health Sciences, Oklahoma City, OK (United States); Alsbou, N [University of Central Oklahoma, Edmond, OK (United States)

2016-06-15

Purpose: To model patient motion and produce four-dimensional (4D) optimized dose distributions that consider motion-artifacts in the dose calculation during the treatment planning process. Methods: An algorithm for dose calculation is developed where patient motion is considered in dose calculation at the stage of the treatment planning. First, optimal dose distributions are calculated for the stationary target volume where the dose distributions are optimized considering intensity-modulated radiation therapy (IMRT). Second, a convolution-kernel is produced from the best-fitting curve which matches the motion trajectory of the patient. Third, the motion kernel is deconvolved with the initial dose distribution optimized for the stationary target to produce a dose distribution that is optimized in four-dimensions. This algorithm is tested with measured doses using a mobile phantom that moves with controlled motion patterns. Results: A motion-optimized dose distribution is obtained from the initial dose distribution of the stationary target by deconvolution with the motion-kernel of the mobile target. This motion-optimized dose distribution is equivalent to that optimized for the stationary target using IMRT. The motion-optimized and measured dose distributions are tested with the gamma index with a passing rate of >95% considering 3% dose-difference and 3mm distance-to-agreement. If the dose delivery per beam takes place over several respiratory cycles, then the spread-out of the dose distributions is only dependent on the motion amplitude and not affected by motion frequency and phase. This algorithm is limited to motion amplitudes that are smaller than the length of the target along the direction of motion. Conclusion: An algorithm is developed to optimize dose in 4D. Besides IMRT that provides optimal dose coverage for a stationary target, it extends dose optimization to 4D considering target motion. This algorithm provides alternative to motion management

Two-Step System Identification and Primitive-Based Motion Planning for Control of Small Unmanned Aerial Vehicles

Science.gov (United States)

Grymin, David J.

This dissertation addresses motion planning, modeling, and feedback control for autonomous vehicle systems. A hierarchical approach for motion planning and control of nonlinear systems operating in obstacle environments is presented. To reduce computation time during the motion planning process, dynamically feasible trajectories are generated in real-time through concatenation of pre-specified motion primitives. The motion planning task is posed as a search over a directed graph, and the applicability of informed graph search techniques is investigated. Specifically, a locally greedy algorithm with effective backtracking ability is developed and compared to weighted A* search. The greedy algorithm shows an advantage with respect to solution cost and computation time when larger motion primitive libraries that do not operate on a regular state lattice are utilized. Linearization of the nonlinear system equations about the motion primitive library results in a hybrid linear time-varying model, and an optimal control algorithm using the l 2-induced norm as the performance measure is applied to ensure that the system tracks the desired trajectory. The ability of the resulting controller to closely track the trajectory obtained from the motion planner, despite various disturbances and uncertainties, is demonstrated through simulation. Additionally, an approach for obtaining dynamically feasible reference trajectories and feedback controllers for a small unmanned aerial vehicle (UAV) based on an aerodynamic model derived from flight tests is presented. The modeling approach utilizes the two step method (TSM) with stepwise multiple regression to determine relevant explanatory terms for the aerodynamic models. Dynamically feasible trajectories are then obtained through the solution of an optimal control problem using pseudospectral optimal control software. Discretetime feedback controllers are then obtained to regulate the vehicle along the desired reference trajectory

A Motion Planning Method for Omnidirectional Mobile Robot Based on the Anisotropic Characteristics

Directory of Open Access Journals (Sweden)

Chuntao Leng

2008-11-01

Full Text Available A more suitable motion planning method for an omni-directional mobile robot (OMR, an improved APF method (iAPF, is proposed in this paper by introducing the revolving factor into the artificial potential field (APF. Accordingly, the motion direction derived from traditional artificial potential field (tAPF is regulated. The maximum velocity, maximum acceleration and energy consumption of the OMR moving in different directions are analyzed, based on the kinematic and dynamic constraints of an OMR, and the anisotropy of OMR is presented in this paper. Then the novel concept of an Anisotropic-Function is proposed to indicate the quality of motion in different directions, which can make a very favorable trade-off between time-optimality, stability and efficacy-optimality. In order to obtain the optimal motion, the path that the robot can take in order to avoid the obstacle safely and reach the goal in a shorter path is deduced. Finally, simulations and experiments are carried out to demonstrate that the motion resulting from the iAPF is high-speed, highly stable and highly efficient when compared to the tAPF.

The Motion Planning of Overhead Crane Based on Suppressing Payload Residual Swing

Directory of Open Access Journals (Sweden)

Liu Hua-sen

2016-01-01

Full Text Available Since the overhead crane system is subject to under actuation system due to that overhead crane and payload are connected by flexibility wire rope. The payload generates residual swing when the overhead crane is accelerating/ decelerating the motions. This may cause trouble for the payload precise positioning and motion planning. Hence, an optimization input shaping control method is presented to reduce the under actuated overhead crane’s payload swing caused via the inertia force. The dynamic model of the overhead crane is proposed according to the physics structure of the crane. The input shaper based on the motion planning of the crane is used as the feed forward input to suppress payload residual swing. Simulation and experiment results indicate that the ZV input shaper and ZVD input shaper can reduce the payload swing of the overhead crane.

Layered Safe Motion Planning for Autonomous Vehicles.

Science.gov (United States)

1995-09-01

The major problem addressed by this research is how to plan a safe motion for autonomous vehicles in a two dimensional, rectilinear world. With given start and goal configurations, the planner performs motion planning which

Fuzzy Logic Unmanned Air Vehicle Motion Planning

Directory of Open Access Journals (Sweden)

Chelsea Sabo

2012-01-01

Full Text Available There are a variety of scenarios in which the mission objectives rely on an unmanned aerial vehicle (UAV being capable of maneuvering in an environment containing obstacles in which there is little prior knowledge of the surroundings. With an appropriate dynamic motion planning algorithm, UAVs would be able to maneuver in any unknown environment towards a target in real time. This paper presents a methodology for two-dimensional motion planning of a UAV using fuzzy logic. The fuzzy inference system takes information in real time about obstacles (if within the agent's sensing range and target location and outputs a change in heading angle and speed. The FL controller was validated, and Monte Carlo testing was completed to evaluate the performance. Not only was the path traversed by the UAV often the exact path computed using an optimal method, the low failure rate makes the fuzzy logic controller (FLC feasible for exploration. The FLC showed only a total of 3% failure rate, whereas an artificial potential field (APF solution, a commonly used intelligent control method, had an average of 18% failure rate. These results highlighted one of the advantages of the FLC method: its adaptability to complex scenarios while maintaining low control effort.

Software for Project-Based Learning of Robot Motion Planning

Science.gov (United States)

Moll, Mark; Bordeaux, Janice; Kavraki, Lydia E.

2013-01-01

Motion planning is a core problem in robotics concerned with finding feasible paths for a given robot. Motion planning algorithms perform a search in the high-dimensional continuous space of robot configurations and exemplify many of the core algorithmic concepts of search algorithms and associated data structures. Motion planning algorithms can…

Semantic Mapping and Motion Planning with Turtlebot Roomba

International Nuclear Information System (INIS)

Butt, Rizwan Aslam; Ali, Syed M Usman

2013-01-01

In this paper, we have successfully demonstrated the semantic mapping and motion planning experiments on Turtlebot Robot using Microsoft Kinect in ROS environment. Moreover, we have also performed the comparative studies on various sampling based motion planning algorithms with Turtlebot in Open Motion Planning Library. Our comparative analysis revealed that Expansive Space Trees (EST) surmounted all other approaches with respect to memory occupation and processing time. We have also tried to summarize the related concepts of autonomous robotics which we hope would be helpful for beginners

The anatomy of a distributed motion planning roadmap

KAUST Repository

Jacobs, Sam Ade

2014-09-01

© 2014 IEEE. In this paper, we evaluate and compare the quality and structure of roadmaps constructed from parallelizing sampling-based motion planning algorithms against that of roadmaps constructed using sequential planner. Also, we make an argument and provide experimental results that show that motion planning problems involving heterogenous environments (common in most realistic and large-scale motion planning) is a natural fit for spatial subdivision-based parallel processing. Spatial subdivision-based parallel processing approach is suited for heterogeneous environments because it allows for local adaption in solving a global problem while taking advantage of scalability that is possible with parallel processing.

The anatomy of a distributed motion planning roadmap

KAUST Repository

Jacobs, Sam Ade; Amato, Nancy M.

2014-01-01

© 2014 IEEE. In this paper, we evaluate and compare the quality and structure of roadmaps constructed from parallelizing sampling-based motion planning algorithms against that of roadmaps constructed using sequential planner. Also, we make an argument and provide experimental results that show that motion planning problems involving heterogenous environments (common in most realistic and large-scale motion planning) is a natural fit for spatial subdivision-based parallel processing. Spatial subdivision-based parallel processing approach is suited for heterogeneous environments because it allows for local adaption in solving a global problem while taking advantage of scalability that is possible with parallel processing.

A 3D motion planning framework for snake robots

OpenAIRE

Liljebäck, Pål; Pettersen, Kristin Ytterstad; Stavdahl, Øyvind; Gravdahl, Jan Tommy

2014-01-01

- Author's postprint This paper presents a motion planning framework for three-dimensional body shape control of snake robots. Whereas conventional motion planning approaches define the body shape of snake robots in terms of their individual joint angles, the proposed framework allows the body shape to be specified in terms of Cartesian coordinates in the environment of the robot. This approach simplifies motion planning since Cartesian coordinates are more intuitively mapped to the overal...

Motion planning for gantry mounted manipulators

DEFF Research Database (Denmark)

Olsen, Anders Lau; Petersen, Henrik Gordon

2007-01-01

We present a roadmap based planner for finding robot motions for gantry mounted manipulators for a line welding application at Odense Steel Shipyard (OSS). The robot motions are planned subject to constraints on when the gantry may be moved. We show that random sampling of gantry configurations...

An efficient inverse radiotherapy planning method for VMAT using quadratic programming optimization.

Science.gov (United States)

Hoegele, W; Loeschel, R; Merkle, N; Zygmanski, P

2012-01-01

The purpose of this study is to investigate the feasibility of an inverse planning optimization approach for the Volumetric Modulated Arc Therapy (VMAT) based on quadratic programming and the projection method. The performance of this method is evaluated against a reference commercial planning system (eclipse(TM) for rapidarc(TM)) for clinically relevant cases. The inverse problem is posed in terms of a linear combination of basis functions representing arclet dose contributions and their respective linear coefficients as degrees of freedom. MLC motion is decomposed into basic motion patterns in an intuitive manner leading to a system of equations with a relatively small number of equations and unknowns. These equations are solved using quadratic programming under certain limiting physical conditions for the solution, such as the avoidance of negative dose during optimization and Monitor Unit reduction. The modeling by the projection method assures a unique treatment plan with beneficial properties, such as the explicit relation between organ weightings and the final dose distribution. Clinical cases studied include prostate and spine treatments. The optimized plans are evaluated by comparing isodose lines, DVH profiles for target and normal organs, and Monitor Units to those obtained by the clinical treatment planning system eclipse(TM). The resulting dose distributions for a prostate (with rectum and bladder as organs at risk), and for a spine case (with kidneys, liver, lung and heart as organs at risk) are presented. Overall, the results indicate that similar plan qualities for quadratic programming (QP) and rapidarc(TM) could be achieved at significantly more efficient computational and planning effort using QP. Additionally, results for the quasimodo phantom [Bohsung et al., "IMRT treatment planning: A comparative inter-system and inter-centre planning exercise of the estro quasimodo group," Radiother. Oncol. 76(3), 354-361 (2005)] are presented as an example

Kinodynamic Motion Planning for Autonomous Vehicles

Directory of Open Access Journals (Sweden)

Jiwung Choi

2014-06-01

Full Text Available This article proposes a computationally effective motion planning algorithm for autonomous ground vehicles operating in a semi-structured environment with a mission specified by waypoints, corridor widths and obstacles. The algorithm switches between two kinds of planners, (i static planners and (ii moving obstacle avoidance manoeuvre planners, depending on the mobility of any detected obstacles. While the first is broken down into a path planner and a controller, the second generates a sequence of controls without global path planning. Each subsystem is implemented as follows. The path planner produces an optimal piecewise linear path by applying a variant of cell decomposition and dynamic programming. The piecewise linear path is smoothed by Bézier curves such that the maximum curvatures of the curves are minimized. The controller calculates the highest allowable velocity profile along the path, consistent with the limits on both tangential and radial acceleration and the steering command for the vehicle to track the trajectory using a pure pursuit method. The moving obstacle avoidance manoeuvre produces a sequence of time-optimal local velocities, by minimizing the cost as determined by the safety of the current velocity against obstacles in the velocity obstacle paradigm and the deviation of the current velocity relative to the desired velocity, to satisfy the waypoint constraint. The algorithms are shown to be robust and computationally efficient, and to demonstrate a viable methodology for autonomous vehicle control in the presence of unknown obstacles.

Planning Study Comparison of Real-Time Target Tracking and Four-Dimensional Inverse Planning for Managing Patient Respiratory Motion

International Nuclear Information System (INIS)

Zhang Peng; Hugo, Geoffrey D.; Yan Di

2008-01-01

Purpose: Real-time target tracking (RT-TT) and four-dimensional inverse planning (4D-IP) are two potential methods to manage respiratory target motion. In this study, we evaluated each method using the cumulative dose-volume criteria in lung cancer radiotherapy. Methods and Materials: Respiration-correlated computed tomography scans were acquired for 4 patients. Deformable image registration was applied to generate a displacement mapping for each phase image of the respiration-correlated computed tomography images. First, the dose distribution for the organs of interest obtained from an idealized RT-TT technique was evaluated, assuming perfect knowledge of organ motion and beam tracking. Inverse planning was performed on each phase image separately. The treatment dose to the organs of interest was then accumulated from the optimized plans. Second, 4D-IP was performed using the probability density function of respiratory motion. The beam arrangement, prescription dose, and objectives were consistent in both planning methods. The dose-volume and equivalent uniform dose in the target volume, lung, heart, and spinal cord were used for the evaluation. Results: The cumulative dose in the target was similar for both techniques. The equivalent uniform dose of the lung, heart, and spinal cord was 4.6 ± 2.2, 11 ± 4.4, and 11 ± 6.6 Gy for RT-TT with a 0-mm target margin, 5.2 ± 3.1, 12 ± 5.9, and 12 ± 7.8 Gy for RT-TT with a 2-mm target margin, and 5.3 ± 2.3, 11.9 ± 5.0, and 12 ± 5.6 Gy for 4D-IP, respectively. Conclusion: The results of our study have shown that 4D-IP can achieve plans similar to those achieved by RT-TT. Considering clinical implementation, 4D-IP could be a more reliable and practical method to manage patient respiration-induced motion

Planning of motion strategy for hexapod robot using biogeography based optimization

Directory of Open Access Journals (Sweden)

Hayder Mahdi Abdulridha

2017-08-01

Full Text Available The necessity to utilize the usage of the robot cannot be denied since there are a lot of natural disasters occur around the world, the robot can reach places where humans cannot reach. Hexapod robotic is one of the robots utilized in this case due to its balance and versatility at some stage in the movement on any kind of floor. In this project the explanation of using software and hardware Arduino microcontroller is used to control of such a hexapod. The output signal from Arduino for controlling leg's joint angular position such as a pulse called Pulse Width Modulation (PWM. Also Arduino programmed to create the sequence of motion for six legs. The second part of project is about controlling hexapod to avoid hitches and tracking the wall by using PID controller. Tuning of the PID processes based on Biogeography Based Optimization(BBO need to keep the connection between PC and hexapod, because the BBO was written by Matlab. The experimental results using BBO to optimize the PID controller parameters of hexapod robot show the efficiency of this technique in the adaptation of controller.

SU-F-T-337: Accounting for Patient Motion During Volumetric Modulated Ac Therapy (VMAT) Planning for Post Mastectomy Chest Wall Irradiation

Energy Technology Data Exchange (ETDEWEB)

Hernandez, M; Fontenot, J [Mary Bird Perkins Cancer Center, Baton Rouge, LA (United States); Heins, D [Louisiana State University, Baton Rouge, LA (United States)

2016-06-15

Purpose: To evaluate two dose optimization strategies for maintaining target volume coverage of inversely-planned post mastectomy radiotherapy (PMRT) plans during patient motion. Methods: Five patients previously treated with VMAT for PMRT at our clinical were randomly selected for this study. For each patient, two plan optimization strategies were compared. Plan 1 was optimized to a volume that included the physician’s planning target volume (PTV) plus an expansion up to 0.3 cm from the bolus surface. Plan 2 was optimized to the PTV plus an expansion up to 0.3 cm from the patient surface (i.e., not extending into the bolus). VMAT plans were optimized to deliver 95% of the prescription to 95% of the PTV while sparing organs at risk based on clinical dose limits. PTV coverage was then evaluated following the simulation of patient shifts by 1.0 cm in the anterior and posterior directions using the treatment planning system. Results: Posterior patient shifts produced a difference in D95% of around 11% in both planning approaches from the non-shifted dose distributions. Coverage of the medial and lateral borders of the evaluation volume was reduced in both the posteriorly shifted plans (Plan 1 and Plan 2). Anterior patient shifts affected Plan 2 more than Plan 1 with a difference in D95% of 1% for Plan 1 versus 6% for Plan 2 from the non-shifted dose distributions. The least variation in PTV dose homogeneity for both shifts was obtained with Plan 1. However, all posteriorly shifted plans failed to deliver 95% of the prescription to 95% of the PTV. Whereas, only a few anteriorly shifted plans failed this criteria. Conclusion: The results of this study suggest both planning volume methods are sensitive to patient motion, but that a PTV extended into a bolus volume is slightly more robust for anterior patient shifts.

SU-F-T-337: Accounting for Patient Motion During Volumetric Modulated Ac Therapy (VMAT) Planning for Post Mastectomy Chest Wall Irradiation

International Nuclear Information System (INIS)

Hernandez, M; Fontenot, J; Heins, D

2016-01-01

Purpose: To evaluate two dose optimization strategies for maintaining target volume coverage of inversely-planned post mastectomy radiotherapy (PMRT) plans during patient motion. Methods: Five patients previously treated with VMAT for PMRT at our clinical were randomly selected for this study. For each patient, two plan optimization strategies were compared. Plan 1 was optimized to a volume that included the physician’s planning target volume (PTV) plus an expansion up to 0.3 cm from the bolus surface. Plan 2 was optimized to the PTV plus an expansion up to 0.3 cm from the patient surface (i.e., not extending into the bolus). VMAT plans were optimized to deliver 95% of the prescription to 95% of the PTV while sparing organs at risk based on clinical dose limits. PTV coverage was then evaluated following the simulation of patient shifts by 1.0 cm in the anterior and posterior directions using the treatment planning system. Results: Posterior patient shifts produced a difference in D95% of around 11% in both planning approaches from the non-shifted dose distributions. Coverage of the medial and lateral borders of the evaluation volume was reduced in both the posteriorly shifted plans (Plan 1 and Plan 2). Anterior patient shifts affected Plan 2 more than Plan 1 with a difference in D95% of 1% for Plan 1 versus 6% for Plan 2 from the non-shifted dose distributions. The least variation in PTV dose homogeneity for both shifts was obtained with Plan 1. However, all posteriorly shifted plans failed to deliver 95% of the prescription to 95% of the PTV. Whereas, only a few anteriorly shifted plans failed this criteria. Conclusion: The results of this study suggest both planning volume methods are sensitive to patient motion, but that a PTV extended into a bolus volume is slightly more robust for anterior patient shifts.

Multi-agent System for Off-line Coordinated Motion Planning of Multiple Industrial Robots

Directory of Open Access Journals (Sweden)

Shital S. Chiddarwar

2011-03-01

Full Text Available This article presents an agent based framework for coordinated motion planning of multiple robots. The emerging paradigm of agent based systems is implemented to address various issues related to safe and fast task execution when multiple robots share a common workspace. In the proposed agent based framework, each issue vital for coordinated motion planning of multiple robots and every robot participating in coordinated task is considered as an agent. The identified agents are interfaced with each other in order to incorporate the desired flexibility in the developed framework. This framework gives a complete strategy for determination of optimal trajectories of robots working in coordination with due consideration to their kinematic, dynamic and payload constraint. The complete architecture of the proposed framework and the detailed discussion on various modules are covered in this paper.

Motion Planning in Multi-robot Systems using Timed Automata

DEFF Research Database (Denmark)

Andersen, Michael. S.; Jensen, Rune S.; Bak, Thomas

This paper dscribes how interacting timed automata can be used to model, analyze, and verify motion planning problems for systems with multiple mobile robots. The method assumes an infra-structure of simple unicycle type robots, moving om a planar grid. The motion of the robots, including simple...... kinematics, is captured in an automata formalism that allows formal composition and symbolic reasoning. The verification software UppAal is used to verify specification requirements formulated in computational tree logic (CTL), generating all feasible trajectories that satisfy specifications. The results...... of the planning are demonstrateted in a testbed that allows execution of the planned paths and motion primitives by synchronizing the planning results from UppAal with actual robotic vehicles. The planning problem may be modified online by moving obstacles in the physical environment, which causes a re...

The Application of Euler-Lagrange Method of Optimization for Electromechanical Motion Control

Directory of Open Access Journals (Sweden)

Cristian VASILACHE

2000-12-01

Full Text Available Industrial and non-industrial processes such as production plans, robots, pumps, compressors, home applications, transportation of people and goods etc., require some kinds of motion control. The main functions of electromechanical drives are to adjust these processes by controlling the torque, speed or position. The objective of this paper is to perform the control of motion while minimizing power losses, that is ∫Ri2dt, in process conversion of electrical energy to mechanical energy. The optimal control laws for our problem is find using the Euler - Lagrange principle. We consider three types of controlled drives: torque, speed and position. Each of them has different control laws. By implementation of these controls with Borland C++ and Matlab environment, substantial energy savings are obtained.

Software for project-based learning of robot motion planning

Science.gov (United States)

Moll, Mark; Bordeaux, Janice; Kavraki, Lydia E.

2013-12-01

Motion planning is a core problem in robotics concerned with finding feasible paths for a given robot. Motion planning algorithms perform a search in the high-dimensional continuous space of robot configurations and exemplify many of the core algorithmic concepts of search algorithms and associated data structures. Motion planning algorithms can be explained in a simplified two-dimensional setting, but this masks many of the subtleties and complexities of the underlying problem. We have developed software for project-based learning of motion planning that enables deep learning. The projects that we have developed allow advanced undergraduate students and graduate students to reflect on the performance of existing textbook algorithms and their own variations on such algorithms. Formative assessment has been conducted at three institutions. The core of the software used for this teaching module is also used within the Robot Operating System, a widely adopted platform by the robotics research community. This allows for transfer of knowledge and skills to robotics research projects involving a large variety robot hardware platforms.

Comparison of various online IGRT strategies: The benefits of online treatment plan re-optimization

International Nuclear Information System (INIS)

Schulze, Derek; Liang, Jian; Yan, Di; Zhang Tiezhi

2009-01-01

Purpose: To compare the dosimetric differences of various online IGRT strategies and to predict potential benefits of online re-optimization techniques in prostate cancer radiation treatments. Materials and methods: Nine prostate patients were recruited in this study. Each patient has one treatment planning CT images and 10-treatment day CT images. Five different online IGRT strategies were evaluated which include 3D conformal with bone alignment, 3D conformal re-planning via aperture changes, intensity modulated radiation treatment (IMRT) with bone alignment, IMRT with target alignment and IMRT daily re-optimization. Treatment planning and virtual treatment delivery were performed. The delivered doses were obtained using in-house deformable dose mapping software. The results were analyzed using equivalent uniform dose (EUD). Results: With the same margin, rectum and bladder doses in IMRT plans were about 10% and 5% less than those in CRT plans, respectively. Rectum and bladder doses were reduced as much as 20% if motion margin is reduced by 1 cm. IMRT is more sensitive to organ motion. Large discrepancies of bladder and rectum doses were observed compared to the actual delivered dose with treatment plan predication. The therapeutic ratio can be improved by 14% and 25% for rectum and bladder, respectively, if IMRT online re-planning is employed compared to the IMRT bone alignment approach. The improvement of target alignment approach is similar with 11% and 21% dose reduction to rectum and bladder, respectively. However, underdosing in seminal vesicles was observed on certain patients. Conclusions: Online treatment plan re-optimization may significantly improve therapeutic ratio in prostate cancer treatments mostly due to the reduction of PTV margin. However, for low risk patient with only prostate involved, online target alignment IMRT treatment would achieve similar results as online re-planning. For all IGRT approaches, the delivered organ-at-risk doses may be

Onboard Risk-Aware Real-Time Motion Planning Algorithms for Spacecraft Maneuvering

Data.gov (United States)

National Aeronautics and Space Administration — Unlocking the next generation of complex missions for autonomous spacecraft will require significant advances in robust motion planning. The aim of motion planning...

Motion Planning for a Direct Metal Deposition Rapid Prototyping System

Energy Technology Data Exchange (ETDEWEB)

AMES,ARLO L.; HENSINGER,DAVID M.; KUHLMANN,JOEL L.

1999-10-18

A motion planning strategy was developed and implemented to generate motion control instructions from solid model data for controlling a robotically driven solid free-form fabrication process. The planning strategy was tested using a PUMA type robot arm integrated into a LENS{trademark} (Laser Engineered Net Shape) system. Previous systems relied on a series of x, y, and z stages, to provide a minimal coordinated motion control capability. This limited the complexity of geometries that could be constructed. With the coordinated motion provided by a robotic arm, the system can produce three dimensional parts by ''writing'' material onto any face of existing material. The motion planning strategy relied on solid model geometry evaluation and exploited robotic positioning flexibility to allow the construction of geometrically complex parts. The integration of the robotic manipulator into the LENS{trademark} system was tested by producing metal parts directly from CAD models.

Motion and operation planning of robotic systems background and practical approaches

CERN Document Server

Gomez-Barvo, Fernando

2015-01-01

This book addresses the broad multi-disciplinary topic of robotics, and presents the basic techniques for motion and operation planning in robotics systems. Gathering contributions from experts in diverse and wide ranging fields, it offers an overview of the most recent and cutting-edge practical applications of these methodologies. It covers both theoretical and practical approaches, and elucidates the transition from theory to implementation. An extensive analysis is provided, including humanoids, manipulators, aerial robots and ground mobile robots. ‘Motion and Operation Planning of Robotic Systems’ addresses the following topics: *The theoretical background of robotics. *Application of motion planning techniques to manipulators, such as serial and parallel manipulators. *Mobile robots planning, including robotic applications related to aerial robots, large scale robots and traditional wheeled robots. *Motion planning for humanoid robots. An invaluable reference text for graduate students and researche...

Motion planning for multiple robots

NARCIS (Netherlands)

Aronov, B.; Berg, de M.; van der Stappen, A.F.; Svestka, P.; Vleugels, J.M.

1999-01-01

We study the motion-planning problem for pairs and triples of robots operating in a shared workspace containing n obstacles. A standard way to solve such problems is to view the collection of robots as one composite robot, whose number of degrees of freedom is d , the sum of the numbers of degrees

Multicriteria optimization informed VMAT planning

Energy Technology Data Exchange (ETDEWEB)

Chen, Huixiao; Craft, David L.; Gierga, David P., E-mail: dgierga@partners.org

2014-04-01

We developed a patient-specific volumetric-modulated arc therapy (VMAT) optimization procedure using dose-volume histogram (DVH) information from multicriteria optimization (MCO) of intensity-modulated radiotherapy (IMRT) plans. The study included 10 patients with prostate cancer undergoing standard fractionation treatment, 10 patients with prostate cancer undergoing hypofractionation treatment, and 5 patients with head/neck cancer. MCO-IMRT plans using 20 and 7 treatment fields were generated for each patient on the RayStation treatment planning system (clinical version 2.5, RaySearch Laboratories, Stockholm, Sweden). The resulting DVH of the 20-field MCO-IMRT plan for each patient was used as the reference DVH, and the extracted point values of the resulting DVH of the MCO-IMRT plan were used as objectives and constraints for VMAT optimization. Weights of objectives or constraints of VMAT optimization or both were further tuned to generate the best match with the reference DVH of the MCO-IMRT plan. The final optimal VMAT plan quality was evaluated by comparison with MCO-IMRT plans based on homogeneity index, conformity number of planning target volume, and organ at risk sparing. The influence of gantry spacing, arc number, and delivery time on VMAT plan quality for different tumor sites was also evaluated. The resulting VMAT plan quality essentially matched the 20-field MCO-IMRT plan but with a shorter delivery time and less monitor units. VMAT plan quality of head/neck cancer cases improved using dual arcs whereas prostate cases did not. VMAT plan quality was improved by fine gantry spacing of 2 for the head/neck cancer cases and the hypofractionation-treated prostate cancer cases but not for the standard fractionation–treated prostate cancer cases. MCO-informed VMAT optimization is a useful and valuable way to generate patient-specific optimal VMAT plans, though modification of the weights of objectives or constraints extracted from resulting DVH of MCO

Simultaneous optimization of sequential IMRT plans

International Nuclear Information System (INIS)

Popple, Richard A.; Prellop, Perri B.; Spencer, Sharon A.; Santos, Jennifer F. de los; Duan, Jun; Fiveash, John B.; Brezovich, Ivan A.

2005-01-01

Radiotherapy often comprises two phases, in which irradiation of a volume at risk for microscopic disease is followed by a sequential dose escalation to a smaller volume either at a higher risk for microscopic disease or containing only gross disease. This technique is difficult to implement with intensity modulated radiotherapy, as the tolerance doses of critical structures must be respected over the sum of the two plans. Techniques that include an integrated boost have been proposed to address this problem. However, clinical experience with such techniques is limited, and many clinicians are uncomfortable prescribing nonconventional fractionation schemes. To solve this problem, we developed an optimization technique that simultaneously generates sequential initial and boost IMRT plans. We have developed an optimization tool that uses a commercial treatment planning system (TPS) and a high level programming language for technical computing. The tool uses the TPS to calculate the dose deposition coefficients (DDCs) for optimization. The DDCs were imported into external software and the treatment ports duplicated to create the boost plan. The initial, boost, and tolerance doses were specified and used to construct cost functions. The initial and boost plans were optimized simultaneously using a gradient search technique. Following optimization, the fluence maps were exported to the TPS for dose calculation. Seven patients treated using sequential techniques were selected from our clinical database. The initial and boost plans used to treat these patients were developed independently of each other by dividing the tolerance doses proportionally between the initial and boost plans and then iteratively optimizing the plans until a summation that met the treatment goals was obtained. We used the simultaneous optimization technique to generate plans that met the original planning goals. The coverage of the initial and boost target volumes in the simultaneously optimized

Optimization of motion control laws for tether crawler or elevator systems

Science.gov (United States)

Swenson, Frank R.; Von Tiesenhausen, Georg

1988-01-01

Based on the proposal of a motion control law by Lorenzini (1987), a method is developed for optimizing motion control laws for tether crawler or elevator systems in terms of the performance measures of travel time, the smoothness of acceleration and deceleration, and the maximum values of velocity and acceleration. The Lorenzini motion control law, based on powers of the hyperbolic tangent function, is modified by the addition of a constant-velocity section, and this modified function is then optimized by parameter selections to minimize the peak acceleration value for a selected travel time or to minimize travel time for the selected peak values of velocity and acceleration. It is shown that the addition of a constant-velocity segment permits further optimization of the motion control law performance.

The dosimetric impact of inversely optimized arc radiotherapy plan modulation for real-time dynamic MLC tracking delivery

International Nuclear Information System (INIS)

Falk, Marianne; Larsson, Tobias; Keall, Paul; Chul Cho, Byung; Aznar, Marianne; Korreman, Stine; Poulsen, Per; Munck af Rosenschoeld, Per

2012-01-01

Purpose: Real-time dynamic multileaf collimator (MLC) tracking for management of intrafraction tumor motion can be challenging for highly modulated beams, as the leaves need to travel far to adjust for target motion perpendicular to the leaf travel direction. The plan modulation can be reduced by using a leaf position constraint (LPC) that reduces the difference in the position of adjacent MLC leaves in the plan. The purpose of this study was to investigate the impact of the LPC on the quality of inversely optimized arc radiotherapy plans and the effect of the MLC motion pattern on the dosimetric accuracy of MLC tracking delivery. Specifically, the possibility of predicting the accuracy of MLC tracking delivery based on the plan modulation was investigated. Methods: Inversely optimized arc radiotherapy plans were created on CT-data of three lung cancer patients. For each case, five plans with a single 358 deg. arc were generated with LPC priorities of 0 (no LPC), 0.25, 0.5, 0.75, and 1 (highest possible LPC), respectively. All the plans had a prescribed dose of 2 Gy x 30, used 6 MV, a maximum dose rate of 600 MU/min and a collimator angle of 45 deg. or 315 deg. To quantify the plan modulation, an average adjacent leaf distance (ALD) was calculated by averaging the mean adjacent leaf distance for each control point. The linear relationship between the plan quality [i.e., the calculated dose distributions and the number of monitor units (MU)] and the LPC was investigated, and the linear regression coefficient as well as a two tailed confidence level of 95% was used in the evaluation. The effect of the plan modulation on the performance of MLC tracking was tested by delivering the plans to a cylindrical diode array phantom moving with sinusoidal motion in the superior-inferior direction with a peak-to-peak displacement of 2 cm and a cycle time of 6 s. The delivery was adjusted to the target motion using MLC tracking, guided in real-time by an infrared optical system

Marker optimization for facial motion acquisition and deformation.

Science.gov (United States)

Le, Binh H; Zhu, Mingyang; Deng, Zhigang

2013-11-01

A long-standing problem in marker-based facial motion capture is what are the optimal facial mocap marker layouts. Despite its wide range of potential applications, this problem has not yet been systematically explored to date. This paper describes an approach to compute optimized marker layouts for facial motion acquisition as optimization of characteristic control points from a set of high-resolution, ground-truth facial mesh sequences. Specifically, the thin-shell linear deformation model is imposed onto the example pose reconstruction process via optional hard constraints such as symmetry and multiresolution constraints. Through our experiments and comparisons, we validate the effectiveness, robustness, and accuracy of our approach. Besides guiding minimal yet effective placement of facial mocap markers, we also describe and demonstrate its two selected applications: marker-based facial mesh skinning and multiresolution facial performance capture.

Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms

KAUST Repository

Fidel, Adam; Jacobs, Sam Ade; Sharma, Shishir; Amato, Nancy M.; Rauchwerger, Lawrence

2014-01-01

Motion planning, which is the problem of computing feasible paths in an environment for a movable object, has applications in many domains ranging from robotics, to intelligent CAD, to protein folding. The best methods for solving this PSPACE-hard problem are so-called sampling-based planners. Recent work introduced uniform spatial subdivision techniques for parallelizing sampling-based motion planning algorithms that scaled well. However, such methods are prone to load imbalance, as planning time depends on region characteristics and, for most problems, the heterogeneity of the sub problems increases as the number of processors increases. In this work, we introduce two techniques to address load imbalance in the parallelization of sampling-based motion planning algorithms: an adaptive work stealing approach and bulk-synchronous redistribution. We show that applying these techniques to representatives of the two major classes of parallel sampling-based motion planning algorithms, probabilistic roadmaps and rapidly-exploring random trees, results in a more scalable and load-balanced computation on more than 3,000 cores. © 2014 IEEE.

Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms

KAUST Repository

Fidel, Adam

2014-05-01

Motion planning, which is the problem of computing feasible paths in an environment for a movable object, has applications in many domains ranging from robotics, to intelligent CAD, to protein folding. The best methods for solving this PSPACE-hard problem are so-called sampling-based planners. Recent work introduced uniform spatial subdivision techniques for parallelizing sampling-based motion planning algorithms that scaled well. However, such methods are prone to load imbalance, as planning time depends on region characteristics and, for most problems, the heterogeneity of the sub problems increases as the number of processors increases. In this work, we introduce two techniques to address load imbalance in the parallelization of sampling-based motion planning algorithms: an adaptive work stealing approach and bulk-synchronous redistribution. We show that applying these techniques to representatives of the two major classes of parallel sampling-based motion planning algorithms, probabilistic roadmaps and rapidly-exploring random trees, results in a more scalable and load-balanced computation on more than 3,000 cores. © 2014 IEEE.

MO-B-BRB-00: Optimizing the Treatment Planning Process

International Nuclear Information System (INIS)

2015-01-01

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequential events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi

MO-B-BRB-00: Optimizing the Treatment Planning Process

Energy Technology Data Exchange (ETDEWEB)

NONE

2015-06-15

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequential events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi

Interactively exploring optimized treatment plans

International Nuclear Information System (INIS)

Rosen, Isaac; Liu, H. Helen; Childress, Nathan; Liao Zhongxing

2005-01-01

Purpose: A new paradigm for treatment planning is proposed that embodies the concept of interactively exploring the space of optimized plans. In this approach, treatment planning ignores the details of individual plans and instead presents the physician with clinical summaries of sets of solutions to well-defined clinical goals in which every solution has been optimized in advance by computer algorithms. Methods and materials: Before interactive planning, sets of optimized plans are created for a variety of treatment delivery options and critical structure dose-volume constraints. Then, the dose-volume parameters of the optimized plans are fit to linear functions. These linear functions are used to show in real time how the target dose-volume histogram (DVH) changes as the DVHs of the critical structures are changed interactively. A bitmap of the space of optimized plans is used to restrict the feasible solutions. The physician selects the critical structure dose-volume constraints that give the desired dose to the planning target volume (PTV) and then those constraints are used to create the corresponding optimized plan. Results: The method is demonstrated using prototype software, Treatment Plan Explorer (TPEx), and a clinical example of a patient with a tumor in the right lung. For this example, the delivery options included 4 open beams, 12 open beams, 4 wedged beams, and 12 wedged beams. Beam directions and relative weights were optimized for a range of critical structure dose-volume constraints for the lungs and esophagus. Cord dose was restricted to 45 Gy. Using the interactive interface, the physician explored how the tumor dose changed as critical structure dose-volume constraints were tightened or relaxed and selected the best compromise for each delivery option. The corresponding treatment plans were calculated and compared with the linear parameterization presented to the physician in TPEx. The linear fits were best for the maximum PTV dose and worst

Optimization of FES-assisted rising motion in individuals with paraplegia

Directory of Open Access Journals (Sweden)

Charles Fattal

2011-12-01

Full Text Available The objective of this paper is to investigate what would be the optimal strategy for voluntary trunk movement, which would minimize hip, knee and ankle torques demanding as well minimal upper limb participation during Functional Electrical Stimulation (FES-assisted sit to stand motion in person suffering from Spinal cord injury. Our results suggest that paraplegic patients should bend their body forward in order to use linear momentum of the trunk in sit off phase, i.e. they should generate the motion similar to the one of healthy subjects. Those results have been experimentally tested using a closed-loop controller for FES-assisted standing-up. The controller should automatically trigger leg stimulation in optimal moment with respect to the trunk motion in order to decrease arm participation during rising phase of the motion.

Automated IMRT planning with regional optimization using planning scripts.

Science.gov (United States)

Xhaferllari, Ilma; Wong, Eugene; Bzdusek, Karl; Lock, Michael; Chen, Jeff

2013-01-07

Intensity-modulated radiation therapy (IMRT) has become a standard technique in radiation therapy for treating different types of cancers. Various class solutions have been developed for simple cases (e.g., localized prostate, whole breast) to generate IMRT plans efficiently. However, for more complex cases (e.g., head and neck, pelvic nodes), it can be time-consuming for a planner to generate optimized IMRT plans. To generate optimal plans in these more complex cases which generally have multiple target volumes and organs at risk, it is often required to have additional IMRT optimization structures such as dose limiting ring structures, adjust beam geometry, select inverse planning objectives and associated weights, and additional IMRT objectives to reduce cold and hot spots in the dose distribution. These parameters are generally manually adjusted with a repeated trial and error approach during the optimization process. To improve IMRT planning efficiency in these more complex cases, an iterative method that incorporates some of these adjustment processes automatically in a planning script is designed, implemented, and validated. In particular, regional optimization has been implemented in an iterative way to reduce various hot or cold spots during the optimization process that begins with defining and automatic segmentation of hot and cold spots, introducing new objectives and their relative weights into inverse planning, and turn this into an iterative process with termination criteria. The method has been applied to three clinical sites: prostate with pelvic nodes, head and neck, and anal canal cancers, and has shown to reduce IMRT planning time significantly for clinical applications with improved plan quality. The IMRT planning scripts have been used for more than 500 clinical cases.

The effects of tumor motion on planning and delivery of respiratory-gated IMRT

International Nuclear Information System (INIS)

Hugo, Geoffrey D.; Agazaryan, Nzhde; Solberg, Timothy D.

2003-01-01

The purpose of this study is to investigate the effects of object motion on the planning and delivery of IMRT. Two phantoms containing objects were imaged using CT under a variety of motion conditions. The effects of object motion on axial CT acquisition with and without gating were assessed qualitatively and quantitatively. Measurements of effective slice width and position for the CT scans were made. Mutual information image fusion was adapted for use as a quantitative measure of object deformation in CT images. IMRT plans were generated on the CT scans of the moving and gated object images. These plans were delivered with motion, with and without gating, and the delivery error between the moving deliveries and a nonmoving delivery was assessed using a scalable vector-based index. Motion during CT acquisition produces motion artifact, object deformation, and object mispositioning, which can be substantially reduced with gating. Objects that vary in cross section in the direction of motion exhibit the most deformation in CT images. Mutual information provides a useful quantitative estimate of object deformation. The delivery of IMRT in the presence of target motion significantly alters the delivered dose distribution in relation to the planned distribution. The utilization of gating for IMRT treatment, including imaging, planning, and delivery, significantly reduces the errors introduced by object motion

Integrated Task and Motion Planning with Verification via Formal Methods

Data.gov (United States)

National Aeronautics and Space Administration — This proposal lays out a research plan to "lift" current state-of-the-art results combining discrete and continuous layers of planning in motion planning to the more...

Optimal trajectory planning of free-floating space manipulator using differential evolution algorithm

Science.gov (United States)

Wang, Mingming; Luo, Jianjun; Fang, Jing; Yuan, Jianping

2018-03-01

The existence of the path dependent dynamic singularities limits the volume of available workspace of free-floating space robot and induces enormous joint velocities when such singularities are met. In order to overcome this demerit, this paper presents an optimal joint trajectory planning method using forward kinematics equations of free-floating space robot, while joint motion laws are delineated with application of the concept of reaction null-space. Bézier curve, in conjunction with the null-space column vectors, are applied to describe the joint trajectories. Considering the forward kinematics equations of the free-floating space robot, the trajectory planning issue is consequently transferred to an optimization issue while the control points to construct the Bézier curve are the design variables. A constrained differential evolution (DE) scheme with premature handling strategy is implemented to find the optimal solution of the design variables while specific objectives and imposed constraints are satisfied. Differ from traditional methods, we synthesize null-space and specialized curve to provide a novel viewpoint for trajectory planning of free-floating space robot. Simulation results are presented for trajectory planning of 7 degree-of-freedom (DOF) kinematically redundant manipulator mounted on a free-floating spacecraft and demonstrate the feasibility and effectiveness of the proposed method.

When does treatment plan optimization require inverse planning?

International Nuclear Information System (INIS)

Sherouse, George W.

1995-01-01

Increasing maturity of image-based computer-aided design of three-dimensional conformal radiotherapy has recently sparked a great deal of work in the area of treatment plan optimization. Optimization of a conformal photon beam treatment plan is that exercise through which a set of intensity-modulated static beams or arcs is specified such that, when the plan is executed, 1) a region of homogeneous dose is produced in the patient with a shape which geometrically conforms (within a specified tolerance) to the three-dimensional shape of a designated target volume and 2) acceptably low incidental dose is delivered to non-target tissues. Interest in conformal radiotherapy arise from a fundamental assumption that there is significant value to be gained from aggressive customization of the treatment for each individual patient In our efforts to design optimal treatments, however, it is important to remember that, given the biological and economic realities of clinical radiotherapy, mathematical optimization of dose distribution metrics with respect to some minimal constraint set is not a necessary or even sufficient condition for design of a clinically optimal treatment. There is wide variation in the complexity of the clinical situations encountered in practice and there are a number of non-physical criteria to be considered in planning. There is also a complementary variety of computational and engineering means for achieving optimization. To date, the scientific dialogue regarding these techniques has concentrated on development of solutions to worst-case scenarios, largely in the absence of consideration of appropriate matching of solution complexity to problem complexity. It is the aim of this presentation to propose a provisional stratification of treatment planning problems, stratified by relative complexity, and to identify a corresponding stratification of necessary treatment planning techniques. It is asserted that the subset of clinical radiotherapy cases for

Utilize target motion to cover clinical target volume (ctv) - a novel and practical treatment planning approach to manage respiratory motion

International Nuclear Information System (INIS)

Jin Jianyue; Ajlouni, Munther; Kong Fengming; Ryu, Samuel; Chetty, Indrin J.; Movsas, Benjamin

2008-01-01

Purpose: To use probability density function (PDF) to model motion effects and incorporate this information into treatment planning for lung cancers. Material and methods: PDFs were calculated from the respiratory motion traces of 10 patients. Motion effects were evaluated by convolving static dose distributions with various PDFs. Based on a differential dose prescription with relatively lower dose to the clinical target volume (CTV) than to the gross tumor volume (GTV), two approaches were proposed to incorporate PDFs into treatment planning. The first approach uses the GTV-based internal target volume (ITV) as the planning target volume (PTV) to ensure full dose to the GTV, and utilizes the motion-induced dose gradient to cover the CTV. The second approach employs an inhomogeneous static dose distribution within a minimized PTV to best match the prescription dose gradient. Results: Motion effects on dose distributions were minimal in the anterior-posterior (AP) and lateral directions: a 10-mm motion only induced about 3% of dose reduction in the peripheral target region. The motion effect was remarkable in the cranial-caudal direction. It varied with the motion amplitude, but tended to be similar for various respiratory patterns. For the first approach, a 10-15 mm motion would adequately cover the CTV (presumed to be 60-70% of the GTV dose) without employing the CTV in planning. For motions 15-mm. An example of inhomogeneous static dose distribution in a reduced PTV was given, and it showed significant dose reduction in the normal tissue without compromising target coverage. Conclusions: Respiratory motion-induced dose gradient can be utilized to cover the CTV and minimize the lung dose without the need for more sophisticated technologies

The Toggle Local Planner for sampling-based motion planning

KAUST Repository

Denny, Jory; Amato, Nancy M.

2012-01-01

Sampling-based solutions to the motion planning problem, such as the probabilistic roadmap method (PRM), have become commonplace in robotics applications. These solutions are the norm as the dimensionality of the planning space grows, i.e., d > 5

Global optimal path planning of an autonomous vehicle for overtaking a moving obstacle

Directory of Open Access Journals (Sweden)

B. Mashadi

Full Text Available In this paper, the global optimal path planning of an autonomous vehicle for overtaking a moving obstacle is proposed. In this study, the autonomous vehicle overtakes a moving vehicle by performing a double lane-change maneuver after detecting it in a proper distance ahead. The optimal path of vehicle for performing the lane-change maneuver is generated by a path planning program in which the sum of lateral deviation of the vehicle from a reference path and the rate of steering angle become minimum while the lateral acceleration of vehicle does not exceed a safe limit value. A nonlinear optimal control theory with the lateral vehicle dynamics equations and inequality constraint of lateral acceleration are used to generate the path. The indirect approach for solving the optimal control problem is used by applying the calculus of variation and the Pontryagin's Minimum Principle to obtain first-order necessary conditions for optimality. The optimal path is generated as a global optimal solution and can be used as the benchmark of the path generated by the local motion planning of autonomous vehicles. A full nonlinear vehicle model in CarSim software is used for path following simulation by importing path data from the MATLAB code. The simulation results show that the generated path for the autonomous vehicle satisfies all vehicle dynamics constraints and hence is a suitable overtaking path for the following vehicle.

Sampling-Based Motion Planning Algorithms for Replanning and Spatial Load Balancing

Energy Technology Data Exchange (ETDEWEB)

Boardman, Beth Leigh [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-10-12

The common theme of this dissertation is sampling-based motion planning with the two key contributions being in the area of replanning and spatial load balancing for robotic systems. Here, we begin by recalling two sampling-based motion planners: the asymptotically optimal rapidly-exploring random tree (RRT*), and the asymptotically optimal probabilistic roadmap (PRM*). We also provide a brief background on collision cones and the Distributed Reactive Collision Avoidance (DRCA) algorithm. The next four chapters detail novel contributions for motion replanning in environments with unexpected static obstacles, for multi-agent collision avoidance, and spatial load balancing. First, we show improved performance of the RRT* when using the proposed Grandparent-Connection (GP) or Focused-Refinement (FR) algorithms. Next, the Goal Tree algorithm for replanning with unexpected static obstacles is detailed and proven to be asymptotically optimal. A multi-agent collision avoidance problem in obstacle environments is approached via the RRT*, leading to the novel Sampling-Based Collision Avoidance (SBCA) algorithm. The SBCA algorithm is proven to guarantee collision free trajectories for all of the agents, even when subject to uncertainties in the knowledge of the other agents’ positions and velocities. Given that a solution exists, we prove that livelocks and deadlock will lead to the cost to the goal being decreased. We introduce a new deconfliction maneuver that decreases the cost-to-come at each step. This new maneuver removes the possibility of livelocks and allows a result to be formed that proves convergence to the goal configurations. Finally, we present a limited range Graph-based Spatial Load Balancing (GSLB) algorithm which fairly divides a non-convex space among multiple agents that are subject to differential constraints and have a limited travel distance. The GSLB is proven to converge to a solution when maximizing the area covered by the agents. The analysis

Limited Impact of Setup and Range Uncertainties, Breathing Motion, and Interplay Effects in Robustly Optimized Intensity Modulated Proton Therapy for Stage III Non-small Cell Lung Cancer

Energy Technology Data Exchange (ETDEWEB)

Inoue, Tatsuya [Department of Radiology, Juntendo University Urayasu Hospital, Chiba (Japan); Widder, Joachim; Dijk, Lisanne V. van [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Takegawa, Hideki [Department of Radiation Oncology, Kansai Medical University Hirakata Hospital, Osaka (Japan); Koizumi, Masahiko; Takashina, Masaaki [Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka (Japan); Usui, Keisuke; Kurokawa, Chie; Sugimoto, Satoru [Department of Radiation Oncology, Juntendo University Graduate School of Medicine, Tokyo (Japan); Saito, Anneyuko I. [Department of Radiology, Juntendo University Urayasu Hospital, Chiba (Japan); Department of Radiation Oncology, Juntendo University Graduate School of Medicine, Tokyo (Japan); Sasai, Keisuke [Department of Radiation Oncology, Juntendo University Graduate School of Medicine, Tokyo (Japan); Veld, Aart A. van' t; Langendijk, Johannes A. [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Korevaar, Erik W., E-mail: e.w.korevaar@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands)

2016-11-01

Purpose: To investigate the impact of setup and range uncertainties, breathing motion, and interplay effects using scanning pencil beams in robustly optimized intensity modulated proton therapy (IMPT) for stage III non-small cell lung cancer (NSCLC). Methods and Materials: Three-field IMPT plans were created using a minimax robust optimization technique for 10 NSCLC patients. The plans accounted for 5- or 7-mm setup errors with ±3% range uncertainties. The robustness of the IMPT nominal plans was evaluated considering (1) isotropic 5-mm setup errors with ±3% range uncertainties; (2) breathing motion; (3) interplay effects; and (4) a combination of items 1 and 2. The plans were calculated using 4-dimensional and average intensity projection computed tomography images. The target coverage (TC, volume receiving 95% of prescribed dose) and homogeneity index (D{sub 2} − D{sub 98}, where D{sub 2} and D{sub 98} are the least doses received by 2% and 98% of the volume) for the internal clinical target volume, and dose indexes for lung, esophagus, heart and spinal cord were compared with that of clinical volumetric modulated arc therapy plans. Results: The TC and homogeneity index for all plans were within clinical limits when considering the breathing motion and interplay effects independently. The setup and range uncertainties had a larger effect when considering their combined effect. The TC decreased to <98% (clinical threshold) in 3 of 10 patients for robust 5-mm evaluations. However, the TC remained >98% for robust 7-mm evaluations for all patients. The organ at risk dose parameters did not significantly vary between the respective robust 5-mm and robust 7-mm evaluations for the 4 error types. Compared with the volumetric modulated arc therapy plans, the IMPT plans showed better target homogeneity and mean lung and heart dose parameters reduced by about 40% and 60%, respectively. Conclusions: In robustly optimized IMPT for stage III NSCLC, the setup and range

Limited Impact of Setup and Range Uncertainties, Breathing Motion, and Interplay Effects in Robustly Optimized Intensity Modulated Proton Therapy for Stage III Non-small Cell Lung Cancer

International Nuclear Information System (INIS)

Inoue, Tatsuya; Widder, Joachim; Dijk, Lisanne V. van; Takegawa, Hideki; Koizumi, Masahiko; Takashina, Masaaki; Usui, Keisuke; Kurokawa, Chie; Sugimoto, Satoru; Saito, Anneyuko I.; Sasai, Keisuke; Veld, Aart A. van't; Langendijk, Johannes A.; Korevaar, Erik W.

2016-01-01

Purpose: To investigate the impact of setup and range uncertainties, breathing motion, and interplay effects using scanning pencil beams in robustly optimized intensity modulated proton therapy (IMPT) for stage III non-small cell lung cancer (NSCLC). Methods and Materials: Three-field IMPT plans were created using a minimax robust optimization technique for 10 NSCLC patients. The plans accounted for 5- or 7-mm setup errors with ±3% range uncertainties. The robustness of the IMPT nominal plans was evaluated considering (1) isotropic 5-mm setup errors with ±3% range uncertainties; (2) breathing motion; (3) interplay effects; and (4) a combination of items 1 and 2. The plans were calculated using 4-dimensional and average intensity projection computed tomography images. The target coverage (TC, volume receiving 95% of prescribed dose) and homogeneity index (D_2 − D_9_8, where D_2 and D_9_8 are the least doses received by 2% and 98% of the volume) for the internal clinical target volume, and dose indexes for lung, esophagus, heart and spinal cord were compared with that of clinical volumetric modulated arc therapy plans. Results: The TC and homogeneity index for all plans were within clinical limits when considering the breathing motion and interplay effects independently. The setup and range uncertainties had a larger effect when considering their combined effect. The TC decreased to 98% for robust 7-mm evaluations for all patients. The organ at risk dose parameters did not significantly vary between the respective robust 5-mm and robust 7-mm evaluations for the 4 error types. Compared with the volumetric modulated arc therapy plans, the IMPT plans showed better target homogeneity and mean lung and heart dose parameters reduced by about 40% and 60%, respectively. Conclusions: In robustly optimized IMPT for stage III NSCLC, the setup and range uncertainties, breathing motion, and interplay effects have limited impact on target coverage, dose homogeneity, and

Radiotherapy Planning Using an Improved Search Strategy in Particle Swarm Optimization.

Science.gov (United States)

Modiri, Arezoo; Gu, Xuejun; Hagan, Aaron M; Sawant, Amit

2017-05-01

Evolutionary stochastic global optimization algorithms are widely used in large-scale, nonconvex problems. However, enhancing the search efficiency and repeatability of these techniques often requires well-customized approaches. This study investigates one such approach. We use particle swarm optimization (PSO) algorithm to solve a 4D radiation therapy (RT) inverse planning problem, where the key idea is to use respiratory motion as an additional degree of freedom in lung cancer RT. The primary goal is to administer a lethal dose to the tumor target while sparing surrounding healthy tissue. Our optimization iteratively adjusts radiation fluence-weights for all beam apertures across all respiratory phases. We implement three PSO-based approaches: conventionally used unconstrained, hard-constrained, and our proposed virtual search. As proof of concept, five lung cancer patient cases are optimized over ten runs using each PSO approach. For comparison, a dynamically penalized likelihood (DPL) algorithm-a popular RT optimization technique is also implemented and used. The proposed technique significantly improves the robustness to random initialization while requiring fewer iteration cycles to converge across all cases. DPL manages to find the global optimum in 2 out of 5 RT cases over significantly more iterations. The proposed virtual search approach boosts the swarm search efficiency, and consequently, improves the optimization convergence rate and robustness for PSO. RT planning is a large-scale, nonconvex optimization problem, where finding optimal solutions in a clinically practical time is critical. Our proposed approach can potentially improve the optimization efficiency in similar time-sensitive problems.

Multi-Robot Motion Planning: A Timed Automata Approach

DEFF Research Database (Denmark)

Quottrup, Michael Melholt; Bak, Thomas; Izadi-Zamanabadi, Roozbeh

2004-01-01

This paper describes how a network of interacting timed automata can be used to model, analyze, and verify motion planning problems in a scenario with multiple robotic vehicles. The method presupposes an infra-structure of robots with feed-back controllers obeying simple restriction on a planar...... grid. The automata formalism merely presents a high-level model of environment, robots and control, but allows composition and formal symbolic reasoning about coordinated solutions. Composition is achieved through synchronization, and the verification software UPPAAL is used for a symbolic verification...... then subsequently be used as a high-level motion plan for the robots. This paper reports on the timed automata framework, results of two verification experiments, promise of the approach, and gives a perspective for future research....

Multi-Robot Motion Planning: A Timed Automata Approach

DEFF Research Database (Denmark)

Quottrup, Michael Melholt; Bak, Thomas; Izadi-Zamanabadi, Roozbeh

This paper describes how a network of interacting timed automata can be used to model, analyze, and verify motion planning problems in a scenario with multiple robotic vehicles. The method presupposes an infra-structure of robots with feed-back controllers obeying simple restriction on a planar...... grid. The automata formalism merely presents a high-level model of environment, robots and control, but allows composition and formal symbolic reasoning about coordinated solutions. Composition is achieved through synchronization, and the verification software UPPAAL is used for a symbolic verification...... then subsequently be used as a high-level motion plan for the robots. This paper reports on the timed automata framework, results of two verification experiments, promise of the approach, and gives a perspective for future research....

Vector-model-supported approach in prostate plan optimization

International Nuclear Information System (INIS)

Liu, Eva Sau Fan; Wu, Vincent Wing Cheung; Harris, Benjamin; Lehman, Margot; Pryor, David; Chan, Lawrence Wing Chi

2017-01-01

Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration

Vector-model-supported approach in prostate plan optimization

Energy Technology Data Exchange (ETDEWEB)

Liu, Eva Sau Fan [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong); Wu, Vincent Wing Cheung [Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong); Harris, Benjamin [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); Lehman, Margot; Pryor, David [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); School of Medicine, University of Queensland (Australia); Chan, Lawrence Wing Chi, E-mail: wing.chi.chan@polyu.edu.hk [Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong)

2017-07-01

Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration

Optimal paths of piston motion of irreversible diesel cycle for minimum entropy generation

Directory of Open Access Journals (Sweden)

Ge Yanlin

2011-01-01

Full Text Available A Diesel cycle heat engine with internal and external irreversibility’s of heat transfer and friction, in which the finite rate of combustion is considered and the heat transfer between the working fluid and the environment obeys Newton’s heat transfer law [q≈ Δ(T], is studied in this paper. Optimal piston motion trajectories for minimizing entropy generation per cycle are derived for the fixed total cycle time and fuel consumed per cycle. Optimal control theory is applied to determine the optimal piston motion trajectories for the cases of with piston acceleration constraint on each stroke and the optimal distribution of the total cycle time among the strokes. The optimal piston motion with acceleration constraint for each stroke consists of three segments, including initial maximum acceleration and final maximum deceleration boundary segments, respectively. Numerical examples for optimal configurations are provided, and the results obtained are compared with those obtained when maximizing the work output with Newton’s heat transfer law. The results also show that optimizing the piston motion trajectories could reduce engine entropy generation by more than 20%. This is primarily due to the decrease in entropy generation caused by heat transfer loss on the initial portion of the power stroke.

A Motion Planning Approach to Studying Molecular Motions

KAUST Repository

Amato, Nancy M.

2010-01-01

While structurally very different, protein and RNA molecules share an important attribute. The motions they undergo are strongly related to the function they perform. For example, many diseases such as Mad Cow disease or Alzheimer\\'s disease are associated with protein misfolding and aggregation. Similarly, RNA folding velocity may regulate the plasmid copy number, and RNA folding kinetics can regulate gene expression at the translational level. Knowledge of the stability, folding, kinetics and detailed mechanics of the folding process may help provide insight into how proteins and RNAs fold. In this paper, we present an overview of our work with a computational method we have adapted from robotic motion planning to study molecular motions. We have validated against experimental data and have demonstrated that our method can capture biological results such as stochastic folding pathways, population kinetics of various conformations, and relative folding rates. Thus, our method provides both a detailed view (e.g., individual pathways) and a global view (e.g., population kinetics, relative folding rates, and reaction coordinates) of energy landscapes of both proteins and RNAs. We have validated these techniques by showing that we observe the same relative folding rates as shown in experiments for structurally similar protein molecules that exhibit different folding behaviors. Our analysis has also been able to predict the same relative gene expression rate for wild-type MS2 phage RNA and three of its mutants.

MRI-based measurements of respiratory motion variability and assessment of imaging strategies for radiotherapy planning

International Nuclear Information System (INIS)

Blackall, J M; Ahmad, S; Miquel, M E; McClelland, J R; Landau, D B; Hawkes, D J

2006-01-01

.4(2.2)-7.7(3.9) mm for volunteers and 10.1(6.1)-12.5(6.3) mm for patients. Errors are generally larger still when using a single breath-hold image at either exhale or inhale to represent the lung. This indicates that account should be taken of intra- and inter-cycle respiratory motion variability and that breath-hold-based methods of obtaining data for RT planning may potentially introduce large errors. This approach to analysis of motion and variability has potential to inform decisions about treatment margins and optimize RT planning

Study on State Transition Method Applied to Motion Planning for a Humanoid Robot

Directory of Open Access Journals (Sweden)

Xuyang Wang

2008-11-01

Full Text Available This paper presents an approach of motion planning for a humanoid robot using a state transition method. In this method, motion planning is simplified by introducing a state-space to describe the whole motion series. And each state in the state-space corresponds to a contact state specified during the motion. The continuous motion is represented by a sequence of discrete states. The concept of the transition between two neighboring states, that is the state transition, can be realized by using some traditional path planning methods. Considering the dynamical stability of the robot, a state transition method based on search strategy is proposed. Different sets of trajectories are generated by using a variable 5th-order polynomial interpolation method. After quantifying the stabilities of these trajectories, the trajectories with the largest stability margin are selected as the final state transition trajectories. Rising motion process is exemplified to validate the method and the simulation results show the proposed method to be feasible and effective.

Lazy Toggle PRM: A single-query approach to motion planning

KAUST Repository

Denny, Jory; Shi, Kensen; Amato, Nancy M.

2013-01-01

Probabilistic RoadMaps (PRMs) are quite suc-cessful in solving complex and high-dimensional motion plan-ning problems. While particularly suited for multiple-query scenarios and expansive spaces, they lack efficiency in both solving single

Energy Optimal Trajectories in Human Arm Motion Aiming for Assistive Robots

Directory of Open Access Journals (Sweden)

Lelai Zhou

2017-01-01

Full Text Available The energy expenditure in human arm has been of great interests for seeking optimal human arm trajectories. This paper presents a new way for calculating metabolic energy consumption of human arm motions. The purpose is to reveal the relationship between the energy consumption and the trajectory of arm motion, and further, the acceleration and arm orientation contributions. Human arm motion in horizontal plane is investigated by virtue of Qualisys motion capture system. The motion data is post-processed by a biomechanical model to obtain the metabolic expenditure. Results on the arm motion kinematics, dynamics and metabolic energy consumption, are included.

Global optimization for motion estimation with applications to ultrasound videos of carotid artery plaques

Science.gov (United States)

Murillo, Sergio; Pattichis, Marios; Soliz, Peter; Barriga, Simon; Loizou, C. P.; Pattichis, C. S.

2010-03-01

Motion estimation from digital video is an ill-posed problem that requires a regularization approach. Regularization introduces a smoothness constraint that can reduce the resolution of the velocity estimates. The problem is further complicated for ultrasound videos (US), where speckle noise levels can be significant. Motion estimation using optical flow models requires the modification of several parameters to satisfy the optical flow constraint as well as the level of imposed smoothness. Furthermore, except in simulations or mostly unrealistic cases, there is no ground truth to use for validating the velocity estimates. This problem is present in all real video sequences that are used as input to motion estimation algorithms. It is also an open problem in biomedical applications like motion analysis of US of carotid artery (CA) plaques. In this paper, we study the problem of obtaining reliable ultrasound video motion estimates for atherosclerotic plaques for use in clinical diagnosis. A global optimization framework for motion parameter optimization is presented. This framework uses actual carotid artery motions to provide optimal parameter values for a variety of motions and is tested on ten different US videos using two different motion estimation techniques.

Trajectory planning of mobile robots using indirect solution of optimal control method in generalized point-to-point task

Science.gov (United States)

Nazemizadeh, M.; Rahimi, H. N.; Amini Khoiy, K.

2012-03-01

This paper presents an optimal control strategy for optimal trajectory planning of mobile robots by considering nonlinear dynamic model and nonholonomic constraints of the system. The nonholonomic constraints of the system are introduced by a nonintegrable set of differential equations which represent kinematic restriction on the motion. The Lagrange's principle is employed to derive the nonlinear equations of the system. Then, the optimal path planning of the mobile robot is formulated as an optimal control problem. To set up the problem, the nonlinear equations of the system are assumed as constraints, and a minimum energy objective function is defined. To solve the problem, an indirect solution of the optimal control method is employed, and conditions of the optimality derived as a set of coupled nonlinear differential equations. The optimality equations are solved numerically, and various simulations are performed for a nonholonomic mobile robot to illustrate effectiveness of the proposed method.

Optimization approaches to volumetric modulated arc therapy planning

Energy Technology Data Exchange (ETDEWEB)

Unkelbach, Jan, E-mail: junkelbach@mgh.harvard.edu; Bortfeld, Thomas; Craft, David [Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114 (United States); Alber, Markus [Department of Medical Physics and Department of Radiation Oncology, Aarhus University Hospital, Aarhus C DK-8000 (Denmark); Bangert, Mark [Department of Medical Physics in Radiation Oncology, German Cancer Research Center, Heidelberg D-69120 (Germany); Bokrantz, Rasmus [RaySearch Laboratories, Stockholm SE-111 34 (Sweden); Chen, Danny [Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana 46556 (United States); Li, Ruijiang; Xing, Lei [Department of Radiation Oncology, Stanford University, Stanford, California 94305 (United States); Men, Chunhua [Department of Research, Elekta, Maryland Heights, Missouri 63043 (United States); Nill, Simeon [Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG (United Kingdom); Papp, Dávid [Department of Mathematics, North Carolina State University, Raleigh, North Carolina 27695 (United States); Romeijn, Edwin [H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332 (United States); Salari, Ehsan [Department of Industrial and Manufacturing Engineering, Wichita State University, Wichita, Kansas 67260 (United States)

2015-03-15

Volumetric modulated arc therapy (VMAT) has found widespread clinical application in recent years. A large number of treatment planning studies have evaluated the potential for VMAT for different disease sites based on the currently available commercial implementations of VMAT planning. In contrast, literature on the underlying mathematical optimization methods used in treatment planning is scarce. VMAT planning represents a challenging large scale optimization problem. In contrast to fluence map optimization in intensity-modulated radiotherapy planning for static beams, VMAT planning represents a nonconvex optimization problem. In this paper, the authors review the state-of-the-art in VMAT planning from an algorithmic perspective. Different approaches to VMAT optimization, including arc sequencing methods, extensions of direct aperture optimization, and direct optimization of leaf trajectories are reviewed. Their advantages and limitations are outlined and recommendations for improvements are discussed.

A test case of computer aided motion planning for nuclear maintenance operation

International Nuclear Information System (INIS)

Schmitzberger, E.; Bouchet, J.L.; Schmitzberger, E.

2001-01-01

Needs for improved tools for nuclear power plant maintenance preparation are expressed by EDF engineering. These are an easier and better management of logistics constraints such as free spaces for motions or handling tasks. The lack of generic or well suited tools and the specificity of nuclear maintenance operation have led EDF R and D to develop its own motion planning tools in collaboration with LAAS-CNRS, Utrecht University and the software publisher CADCENTRE within the framework of the three years Esprit LTR project MOLOG. EDF users needs will be summed up in the first part of the paper under the title ''Motion feasibility studies for maintenance operation'' and then compared to the current industrial offer in the ''Software's background'''s part. The definition and objectives ''Towards motion planning tools'' follows. It explains why maintenance preparation pertains to automatic motion planning and how it makes studies much simpler. The ''MOLOG's Benchmark and first result'''s part describes the test-case used to evaluate the MOLOG project and gives an outlook at the results obtained so far. (author)

Human-like motion planning model for driving in signalized intersections

Directory of Open Access Journals (Sweden)

Yanlei Gu

2017-10-01

Full Text Available Highly automated and fully autonomous vehicles are much more likely to be accepted if they react in the same way as human drivers do, especially in a hybrid traffic situation, which allows autonomous vehicles and human-driven vehicles to share the same road. This paper proposes a human-like motion planning model to represent how human drivers assess environments and operate vehicles in signalized intersections. The developed model consists of a pedestrian intention detection model, gap detection model, and vehicle control model. These three submodels are individually responsible for situation assessment, decision making, and action, and also depend on each other in the process of motion planning. In addition, these submodels are constructed and learned on the basis of human drivers' data collected from real traffic environments. To verify the effectiveness of the proposed motion planning model, we compared the proposed model with actual human driver and pedestrian data. The experimental results showed that our proposed model and actual human driver behaviors are highly similar with respect to gap acceptance in intersections.

MO-B-BRB-01: Optimize Treatment Planning Process in Clinical Environment

International Nuclear Information System (INIS)

Feng, W.

2015-01-01

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequential events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi

MO-B-BRB-01: Optimize Treatment Planning Process in Clinical Environment

Energy Technology Data Exchange (ETDEWEB)

Feng, W. [New York Presbyterian Hospital (United States)

2015-06-15

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequential events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi

Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems.

Science.gov (United States)

Xu, Y; Li, N

2014-09-01

Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator-prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework.

Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems

International Nuclear Information System (INIS)

Xu, Y; Li, N

2014-01-01

Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator–prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework. (paper)

Explicit optimization of plan quality measures in intensity-modulated radiation therapy treatment planning.

Science.gov (United States)

Engberg, Lovisa; Forsgren, Anders; Eriksson, Kjell; Hårdemark, Björn

2017-06-01

To formulate convex planning objectives of treatment plan multicriteria optimization with explicit relationships to the dose-volume histogram (DVH) statistics used in plan quality evaluation. Conventional planning objectives are designed to minimize the violation of DVH statistics thresholds using penalty functions. Although successful in guiding the DVH curve towards these thresholds, conventional planning objectives offer limited control of the individual points on the DVH curve (doses-at-volume) used to evaluate plan quality. In this study, we abandon the usual penalty-function framework and propose planning objectives that more closely relate to DVH statistics. The proposed planning objectives are based on mean-tail-dose, resulting in convex optimization. We also demonstrate how to adapt a standard optimization method to the proposed formulation in order to obtain a substantial reduction in computational cost. We investigated the potential of the proposed planning objectives as tools for optimizing DVH statistics through juxtaposition with the conventional planning objectives on two patient cases. Sets of treatment plans with differently balanced planning objectives were generated using either the proposed or the conventional approach. Dominance in the sense of better distributed doses-at-volume was observed in plans optimized within the proposed framework. The initial computational study indicates that the DVH statistics are better optimized and more efficiently balanced using the proposed planning objectives than using the conventional approach. © 2017 American Association of Physicists in Medicine.

A test case of computer aided motion planning for nuclear maintenance operation

Energy Technology Data Exchange (ETDEWEB)

Schmitzberger, E.; Bouchet, J.L. [Electricite de France (EDF), Dept. Surveillance Diagnostic Maintenance, 78 - Chatou (France); Schmitzberger, E. [Institut National Polytechnique, CRAN, 54 - Vandoeuvre les Nancy (France)

2001-07-01

Needs for improved tools for nuclear power plant maintenance preparation are expressed by EDF engineering. These are an easier and better management of logistics constraints such as free spaces for motions or handling tasks. The lack of generic or well suited tools and the specificity of nuclear maintenance operation have led EDF R and D to develop its own motion planning tools in collaboration with LAAS-CNRS, Utrecht University and the software publisher CADCENTRE within the framework of the three years Esprit LTR project MOLOG. EDF users needs will be summed up in the first part of the paper under the title ''Motion feasibility studies for maintenance operation'' and then compared to the current industrial offer in the ''Software's background'''s part. The definition and objectives ''Towards motion planning tools'' follows. It explains why maintenance preparation pertains to automatic motion planning and how it makes studies much simpler. The ''MOLOG's Benchmark and first result'''s part describes the test-case used to evaluate the MOLOG project and gives an outlook at the results obtained so far. (author)

Dose/volume–response relations for rectal morbidity using planned and simulated motion-inclusive dose distributions

International Nuclear Information System (INIS)

Thor, Maria; Apte, Aditya; Deasy, Joseph O.; Karlsdóttir, Àsa; Moiseenko, Vitali; Liu, Mitchell; Muren, Ludvig Paul

2013-01-01

Background and purpose: Many dose-limiting normal tissues in radiotherapy (RT) display considerable internal motion between fractions over a course of treatment, potentially reducing the appropriateness of using planned dose distributions to predict morbidity. Accounting explicitly for rectal motion could improve the predictive power of modelling rectal morbidity. To test this, we simulated the effect of motion in two cohorts. Materials and methods: The included patients (232 and 159 cases) received RT for prostate cancer to 70 and 74 Gy. Motion-inclusive dose distributions were introduced as simulations of random or systematic motion to the planned dose distributions. Six rectal morbidity endpoints were analysed. A probit model using the QUANTEC recommended parameters was also applied to the cohorts. Results: The differences in associations using the planned over the motion-inclusive dose distributions were modest. Statistically significant associations were obtained with four of the endpoints, mainly at high doses (55–70 Gy), using both the planned and the motion-inclusive dose distributions, primarily when simulating random motion. The strongest associations were observed for GI toxicity and rectal bleeding (Rs = 0.12–0.21; Rs = 0.11–0.20). Applying the probit model, significant associations were found for tenesmus and rectal bleeding (Rs = 0.13, p = 0.02). Conclusion: Equally strong associations with rectal morbidity were observed at high doses (>55 Gy), for the planned and the simulated dose distributions including in particular random rectal motion. Future studies should explore patient-specific descriptions of rectal motion to achieve improved predictive power

A scalable distributed RRT for motion planning

KAUST Repository

Jacobs, Sam Ade

2013-05-01

Rapidly-exploring Random Tree (RRT), like other sampling-based motion planning methods, has been very successful in solving motion planning problems. Even so, sampling-based planners cannot solve all problems of interest efficiently, so attention is increasingly turning to parallelizing them. However, one challenge in parallelizing RRT is the global computation and communication overhead of nearest neighbor search, a key operation in RRTs. This is a critical issue as it limits the scalability of previous algorithms. We present two parallel algorithms to address this problem. The first algorithm extends existing work by introducing a parameter that adjusts how much local computation is done before a global update. The second algorithm radially subdivides the configuration space into regions, constructs a portion of the tree in each region in parallel, and connects the subtrees,i removing cycles if they exist. By subdividing the space, we increase computation locality enabling a scalable result. We show that our approaches are scalable. We present results demonstrating almost linear scaling to hundreds of processors on a Linux cluster and a Cray XE6 machine. © 2013 IEEE.

A scalable distributed RRT for motion planning

KAUST Repository

Jacobs, Sam Ade; Stradford, Nicholas; Rodriguez, Cesar; Thomas, Shawna; Amato, Nancy M.

2013-01-01

Rapidly-exploring Random Tree (RRT), like other sampling-based motion planning methods, has been very successful in solving motion planning problems. Even so, sampling-based planners cannot solve all problems of interest efficiently, so attention is increasingly turning to parallelizing them. However, one challenge in parallelizing RRT is the global computation and communication overhead of nearest neighbor search, a key operation in RRTs. This is a critical issue as it limits the scalability of previous algorithms. We present two parallel algorithms to address this problem. The first algorithm extends existing work by introducing a parameter that adjusts how much local computation is done before a global update. The second algorithm radially subdivides the configuration space into regions, constructs a portion of the tree in each region in parallel, and connects the subtrees,i removing cycles if they exist. By subdividing the space, we increase computation locality enabling a scalable result. We show that our approaches are scalable. We present results demonstrating almost linear scaling to hundreds of processors on a Linux cluster and a Cray XE6 machine. © 2013 IEEE.

Near-optimal integration of facial form and motion.

Science.gov (United States)

Dobs, Katharina; Ma, Wei Ji; Reddy, Leila

2017-09-08

Human perception consists of the continuous integration of sensory cues pertaining to the same object. While it has been fairly well shown that humans use an optimal strategy when integrating low-level cues proportional to their relative reliability, the integration processes underlying high-level perception are much less understood. Here we investigate cue integration in a complex high-level perceptual system, the human face processing system. We tested cue integration of facial form and motion in an identity categorization task and found that an optimal model could successfully predict subjects' identity choices. Our results suggest that optimal cue integration may be implemented across different levels of the visual processing hierarchy.

Inverse planning in the age of digital LINACs: station parameter optimized radiation therapy (SPORT)

Science.gov (United States)

Xing, Lei; Li, Ruijiang

2014-03-01

The last few years have seen a number of technical and clinical advances which give rise to a need for innovations in dose optimization and delivery strategies. Technically, a new generation of digital linac has become available which offers features such as programmable motion between station parameters and high dose-rate Flattening Filter Free (FFF) beams. Current inverse planning methods are designed for traditional machines and cannot accommodate these features of new generation linacs without compromising either dose conformality and/or delivery efficiency. Furthermore, SBRT is becoming increasingly important, which elevates the need for more efficient delivery, improved dose distribution. Here we will give an overview of our recent work in SPORT designed to harness the digital linacs and highlight the essential components of SPORT. We will summarize the pros and cons of traditional beamlet-based optimization (BBO) and direct aperture optimization (DAO) and introduce a new type of algorithm, compressed sensing (CS)-based inverse planning, that is capable of automatically removing the redundant segments during optimization and providing a plan with high deliverability in the presence of a large number of station control points (potentially non-coplanar, non-isocentric, and even multi-isocenters). We show that CS-approach takes the interplay between planning and delivery into account and allows us to balance the dose optimality and delivery efficiency in a controlled way and, providing a viable framework to address various unmet demands of the new generation linacs. A few specific implementation strategies of SPORT in the forms of fixed-gantry and rotational arc delivery are also presented.

Inverse planning in the age of digital LINACs: station parameter optimized radiation therapy (SPORT)

International Nuclear Information System (INIS)

Xing, Lei; Li, Ruijiang

2014-01-01

The last few years have seen a number of technical and clinical advances which give rise to a need for innovations in dose optimization and delivery strategies. Technically, a new generation of digital linac has become available which offers features such as programmable motion between station parameters and high dose-rate Flattening Filter Free (FFF) beams. Current inverse planning methods are designed for traditional machines and cannot accommodate these features of new generation linacs without compromising either dose conformality and/or delivery efficiency. Furthermore, SBRT is becoming increasingly important, which elevates the need for more efficient delivery, improved dose distribution. Here we will give an overview of our recent work in SPORT designed to harness the digital linacs and highlight the essential components of SPORT. We will summarize the pros and cons of traditional beamlet-based optimization (BBO) and direct aperture optimization (DAO) and introduce a new type of algorithm, compressed sensing (CS)-based inverse planning, that is capable of automatically removing the redundant segments during optimization and providing a plan with high deliverability in the presence of a large number of station control points (potentially non-coplanar, non-isocentric, and even multi-isocenters). We show that CS-approach takes the interplay between planning and delivery into account and allows us to balance the dose optimality and delivery efficiency in a controlled way and, providing a viable framework to address various unmet demands of the new generation linacs. A few specific implementation strategies of SPORT in the forms of fixed-gantry and rotational arc delivery are also presented.

Flight plan optimization

Science.gov (United States)

Dharmaseelan, Anoop; Adistambha, Keyne D.

2015-05-01

Fuel cost accounts for 40 percent of the operating cost of an airline. Fuel cost can be minimized by planning a flight on optimized routes. The routes can be optimized by searching best connections based on the cost function defined by the airline. The most common algorithm that used to optimize route search is Dijkstra's. Dijkstra's algorithm produces a static result and the time taken for the search is relatively long. This paper experiments a new algorithm to optimize route search which combines the principle of simulated annealing and genetic algorithm. The experimental results of route search, presented are shown to be computationally fast and accurate compared with timings from generic algorithm. The new algorithm is optimal for random routing feature that is highly sought by many regional operators.

Optimal path planning for single and multiple aircraft using a reduced order formulation

Science.gov (United States)

Twigg, Shannon S.

High-flying unmanned reconnaissance and surveillance systems are now being used extensively in the United States military. Current development programs are producing demonstrations of next-generation unmanned flight systems that are designed to perform combat missions. Their use in first-strike combat operations will dictate operations in densely cluttered environments that include unknown obstacles and threats, and will require the use of terrain for masking. The demand for autonomy of operations in such environments dictates the need for advanced trajectory optimization capabilities. In addition, the ability to coordinate the movements of more than one aircraft in the same area is an emerging challenge. This thesis examines using an analytical reduced order formulation for trajectory generation for minimum time and terrain masking cases. First, pseudo-3D constant velocity equations of motion are used for path planning for a single vehicle. In addition, the inclusion of winds, moving targets and moving threats is considered. Then, this formulation is increased to using 3D equations of motion, both with a constant velocity and with a simplified varying velocity model. Next, the constant velocity equations of motion are expanded to include the simultaneous path planning of an unspecified number of vehicles, for both aircraft avoidance situations and formation flight cases.

Biogeography-based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task-time management

Science.gov (United States)

Zadeh, S. M.; Powers, D. M. W.; Sammut, K.; Yazdani, A. M.

2016-12-01

Autonomous Underwater Vehicles (AUVs) are capable of spending long periods of time for carrying out various underwater missions and marine tasks. In this paper, a novel conflict-free motion planning framework is introduced to enhance underwater vehicle's mission performance by completing maximum number of highest priority tasks in a limited time through a large scale waypoint cluttered operating field, and ensuring safe deployment during the mission. The proposed combinatorial route-path planner model takes the advantages of the Biogeography-Based Optimization (BBO) algorithm toward satisfying objectives of both higher-lower level motion planners and guarantees maximization of the mission productivity for a single vehicle operation. The performance of the model is investigated under different scenarios including the particular cost constraints in time-varying operating fields. To show the reliability of the proposed model, performance of each motion planner assessed separately and then statistical analysis is undertaken to evaluate the total performance of the entire model. The simulation results indicate the stability of the contributed model and its feasible application for real experiments.

Automatic Planning of External Search Engine Optimization

Directory of Open Access Journals (Sweden)

Vita Jasevičiūtė

2015-07-01

Full Text Available This paper describes an investigation of the external search engine optimization (SEO action planning tool, dedicated to automatically extract a small set of most important keywords for each month during whole year period. The keywords in the set are extracted accordingly to external measured parameters, such as average number of searches during the year and for every month individually. Additionally the position of the optimized web site for each keyword is taken into account. The generated optimization plan is similar to the optimization plans prepared manually by the SEO professionals and can be successfully used as a support tool for web site search engine optimization.

Learning by Demonstration for Motion Planning of Upper-Limb Exoskeletons

Science.gov (United States)

Lauretti, Clemente; Cordella, Francesca; Ciancio, Anna Lisa; Trigili, Emilio; Catalan, Jose Maria; Badesa, Francisco Javier; Crea, Simona; Pagliara, Silvio Marcello; Sterzi, Silvia; Vitiello, Nicola; Garcia Aracil, Nicolas; Zollo, Loredana

2018-01-01

The reference joint position of upper-limb exoskeletons is typically obtained by means of Cartesian motion planners and inverse kinematics algorithms with the inverse Jacobian; this approach allows exploiting the available Degrees of Freedom (i.e. DoFs) of the robot kinematic chain to achieve the desired end-effector pose; however, if used to operate non-redundant exoskeletons, it does not ensure that anthropomorphic criteria are satisfied in the whole human-robot workspace. This paper proposes a motion planning system, based on Learning by Demonstration, for upper-limb exoskeletons that allow successfully assisting patients during Activities of Daily Living (ADLs) in unstructured environment, while ensuring that anthropomorphic criteria are satisfied in the whole human-robot workspace. The motion planning system combines Learning by Demonstration with the computation of Dynamic Motion Primitives and machine learning techniques to construct task- and patient-specific joint trajectories based on the learnt trajectories. System validation was carried out in simulation and in a real setting with a 4-DoF upper-limb exoskeleton, a 5-DoF wrist-hand exoskeleton and four patients with Limb Girdle Muscular Dystrophy. Validation was addressed to (i) compare the performance of the proposed motion planning with traditional methods; (ii) assess the generalization capabilities of the proposed method with respect to the environment variability. Three ADLs were chosen to validate the system: drinking, pouring and lifting a light sphere. The achieved results showed a 100% success rate in the task fulfillment, with a high level of generalization with respect to the environment variability. Moreover, an anthropomorphic configuration of the exoskeleton is always ensured. PMID:29527161

Learning by Demonstration for Motion Planning of Upper-Limb Exoskeletons

Directory of Open Access Journals (Sweden)

Clemente Lauretti

2018-02-01

Full Text Available The reference joint position of upper-limb exoskeletons is typically obtained by means of Cartesian motion planners and inverse kinematics algorithms with the inverse Jacobian; this approach allows exploiting the available Degrees of Freedom (i.e. DoFs of the robot kinematic chain to achieve the desired end-effector pose; however, if used to operate non-redundant exoskeletons, it does not ensure that anthropomorphic criteria are satisfied in the whole human-robot workspace. This paper proposes a motion planning system, based on Learning by Demonstration, for upper-limb exoskeletons that allow successfully assisting patients during Activities of Daily Living (ADLs in unstructured environment, while ensuring that anthropomorphic criteria are satisfied in the whole human-robot workspace. The motion planning system combines Learning by Demonstration with the computation of Dynamic Motion Primitives and machine learning techniques to construct task- and patient-specific joint trajectories based on the learnt trajectories. System validation was carried out in simulation and in a real setting with a 4-DoF upper-limb exoskeleton, a 5-DoF wrist-hand exoskeleton and four patients with Limb Girdle Muscular Dystrophy. Validation was addressed to (i compare the performance of the proposed motion planning with traditional methods; (ii assess the generalization capabilities of the proposed method with respect to the environment variability. Three ADLs were chosen to validate the system: drinking, pouring and lifting a light sphere. The achieved results showed a 100% success rate in the task fulfillment, with a high level of generalization with respect to the environment variability. Moreover, an anthropomorphic configuration of the exoskeleton is always ensured.

The availability of the step optimization in Monaco planning system

International Nuclear Information System (INIS)

Kim, Dae Sup

2014-01-01

We present a method to reduce this gap and complete the treatment plan, to be made by the re-optimization is performed in the same conditions as the initial treatment plan different from Monaco treatment planning system. The optimization is carried in two steps when performing the inverse calculation for volumetric modulated radiation therapy or intensity modulated radiation therapy in Monaco treatment planning system. This study was the first plan with a complete optimization in two steps by performing all of the treatment plan, without changing the optimized condition from Step 1 to Step 2, a typical sequential optimization performed. At this time, the experiment was carried out with a pencil beam and Monte Carlo algorithm is applied In step 2. We compared initial plan and re-optimized plan with the same optimized conditions. And then evaluated the planning dose by measurement. When performing a re-optimization for the initial treatment plan, the second plan applied the step optimization. When the common optimization again carried out in the same conditions in the initial treatment plan was completed, the result is not the same. From a comparison of the treatment planning system, similar to the dose-volume the histogram showed a similar trend, but exhibit different values that do not satisfy the conditions best optimized dose, dose homogeneity and dose limits. Also showed more than 20% different in comparison dosimetry. If different dose algorithms, this measure is not the same out. The process of performing a number of trial and error, and you get to the ultimate goal of treatment planning optimization process. If carried out to optimize the completion of the initial trust only the treatment plan, we could be made of another treatment plan. The similar treatment plan could not satisfy to optimization results. When you perform re-optimization process, you will need to apply the step optimized conditions, making sure the dose distribution through the optimization

Optimal control of a programmed motion of a rigid spacecraft using redundant kinematics parameterizations

International Nuclear Information System (INIS)

El-Gohary, Awad

2005-01-01

This paper considers the problem of optimal controlling of a programmed motion of a rigid spacecraft. Given a cost of the spacecraft as a quadratic function of state and control variables we seek for optimal control laws as functions of the state variables and the angle of programmed rotation that minimize this cost and asymptotically stabilize the required programmed motion. The stabilizing properties of the proposed controllers are proved using the optimal Liapunov techniques. Numerical simulation study is presented

Aircraft path planning for optimal imaging using dynamic cost functions

Science.gov (United States)

Christie, Gordon; Chaudhry, Haseeb; Kochersberger, Kevin

2015-05-01

Unmanned aircraft development has accelerated with recent technological improvements in sensing and communications, which has resulted in an "applications lag" for how these aircraft can best be utilized. The aircraft are becoming smaller, more maneuverable and have longer endurance to perform sensing and sampling missions, but operating them aggressively to exploit these capabilities has not been a primary focus in unmanned systems development. This paper addresses a means of aerial vehicle path planning to provide a realistic optimal path in acquiring imagery for structure from motion (SfM) reconstructions and performing radiation surveys. This method will allow SfM reconstructions to occur accurately and with minimal flight time so that the reconstructions can be executed efficiently. An assumption is made that we have 3D point cloud data available prior to the flight. A discrete set of scan lines are proposed for the given area that are scored based on visibility of the scene. Our approach finds a time-efficient path and calculates trajectories between scan lines and over obstacles encountered along those scan lines. Aircraft dynamics are incorporated into the path planning algorithm as dynamic cost functions to create optimal imaging paths in minimum time. Simulations of the path planning algorithm are shown for an urban environment. We also present our approach for image-based terrain mapping, which is able to efficiently perform a 3D reconstruction of a large area without the use of GPS data.

A High-precision Motion Compensation Method for SAR Based on Image Intensity Optimization

Directory of Open Access Journals (Sweden)

Hu Ke-bin

2015-02-01

Full Text Available Owing to the platform instability and precision limitations of motion sensors, motion errors negatively affect the quality of synthetic aperture radar (SAR images. The autofocus Back Projection (BP algorithm based on the optimization of image sharpness compensates for motion errors through phase error estimation. This method can attain relatively good performance, while assuming the same phase error for all pixels, i.e., it ignores the spatial variance of motion errors. To overcome this drawback, a high-precision motion error compensation method is presented in this study. In the proposed method, the Antenna Phase Centers (APC are estimated via optimization using the criterion of maximum image intensity. Then, the estimated APCs are applied for BP imaging. Because the APC estimation equals the range history estimation for each pixel, high-precision phase compensation for every pixel can be achieved. Point-target simulations and processing of experimental data validate the effectiveness of the proposed method.

Conventional treatment planning optimization using simulated annealing

International Nuclear Information System (INIS)

Morrill, S.M.; Langer, M.; Lane, R.G.

1995-01-01

Purpose: Simulated annealing (SA) allows for the implementation of realistic biological and clinical cost functions into treatment plan optimization. However, a drawback to the clinical implementation of SA optimization is that large numbers of beams appear in the final solution, some with insignificant weights, preventing the delivery of these optimized plans using conventional (limited to a few coplanar beams) radiation therapy. A preliminary study suggested two promising algorithms for restricting the number of beam weights. The purpose of this investigation was to compare these two algorithms using our current SA algorithm with the aim of producing a algorithm to allow clinically useful radiation therapy treatment planning optimization. Method: Our current SA algorithm, Variable Stepsize Generalized Simulated Annealing (VSGSA) was modified with two algorithms to restrict the number of beam weights in the final solution. The first algorithm selected combinations of a fixed number of beams from the complete solution space at each iterative step of the optimization process. The second reduced the allowed number of beams by a factor of two at periodic steps during the optimization process until only the specified number of beams remained. Results of optimization of beam weights and angles using these algorithms were compared using a standard cadre of abdominal cases. The solution space was defined as a set of 36 custom-shaped open and wedged-filtered fields at 10 deg. increments with a target constant target volume margin of 1.2 cm. For each case a clinically-accepted cost function, minimum tumor dose was maximized subject to a set of normal tissue binary dose-volume constraints. For this study, the optimized plan was restricted to four (4) fields suitable for delivery with conventional therapy equipment. Results: The table gives the mean value of the minimum target dose obtained for each algorithm averaged over 5 different runs and the comparable manual treatment

Motion-encoded dose calculation through fluence/sinogram modification

International Nuclear Information System (INIS)

Lu, Weiguo; Olivera, Gustavo H.; Mackie, Thomas R.

2005-01-01

Conventional radiotherapy treatment planning systems rely on a static computed tomography (CT) image for planning and evaluation. Intra/inter-fraction patient motions may result in significant differences between the planned and the delivered dose. In this paper, we develop a method to incorporate the knowledge of intra/inter-fraction patient motion directly into the dose calculation. By decomposing the motion into a parallel (to beam direction) component and perpendicular (to beam direction) component, we show that the motion effects can be accounted for by simply modifying the fluence distribution (sinogram). After such modification, dose calculation is the same as those based on a static planning image. This method is superior to the 'dose-convolution' method because it is not based on 'shift invariant' assumption. Therefore, it deals with material heterogeneity and surface curvature very well. We test our method using extensive simulations, which include four phantoms, four motion patterns, and three plan beams. We compare our method with the 'dose-convolution' and the 'stochastic simulation' methods (gold standard). As for the homogeneous flat surface phantom, our method has similar accuracy as the 'dose-convolution' method. As for all other phantoms, our method outperforms the 'dose-convolution'. The maximum motion encoded dose calculation error using our method is within 4% of the gold standard. It is shown that a treatment planning system that is based on 'motion-encoded dose calculation' can incorporate random and systematic motion errors in a very simple fashion. Under this approximation, in principle, a planning target volume definition is not required, since it already accounts for the intra/inter-fraction motion variations and it automatically optimizes the cumulative dose rather than the single fraction dose

MO-C-17A-06: Online Adaptive Re-Planning to Account for Independent Motions Between Multiple Targets During Radiotherapy of Lung Cancer

International Nuclear Information System (INIS)

Liu, F; Tai, A; Ahunbay, E; Gore, E; Johnstone, C; Li, X

2014-01-01

Purpose: To quantify interfractional independent motions between multiple targets in radiotherapy (RT) of lung cancer, and to study the dosimetric benefits of an online adaptive replanning method to account for these variations. Methods: Ninety five diagnostic-quality daily CTs acquired for 9 lung cancer patients treated with IGRT using an in-room CT (CTVision, Siemens) were analyzed. On each daily CT set, contours of the targets (GTV, CTV, or involved nodes) and organs at risk were generated by populating the planning contours using an auto-segmentation tool (ABAS, Elekta) with manual editing. For each patient, an IMRT plan was generated based on the planning CT with a prescription dose of 60 Gy in 2Gy fractions. Three plans were generated and compared for each daily CT set: an IGRT (repositioning) plan by copying the original plan with the required shifts, an online adaptive plan by rapidly modifying the aperture shapes and segment weights of the original plan to conform to the daily anatomy, and a new fully re-optimized plan based on the daily CT using a planning system (Panther, Prowess). Results: The daily deviations of the distance between centers of masses of the targets from the plans varied daily from -10 to 8 mm with an average −0.9±4.1 mm (one standard deviation). The average CTV V100 are 99.0±0.7%, 97.9±2.8%, 99.0±0.6%, and 99.1±0.6%, and the lung V20 Gy 928±332 cc, 944±315 cc, 917±300 cc, and 891±295 cc for the original, repositioning, adaptive, and re-optimized plans, respectively. Wilcoxon signed-rank tests show that the adaptive plans are statistically significantly better than the repositioning plans and comparable with the reoptimized plans. Conclusion: There exist unpredictable, interfractional, relative volume changes and independent motions between multiple targets during lung cancer RT which cannot be accounted for by the current IGRT repositioning but can be corrected by the online adaptive replanning method

MO-C-17A-06: Online Adaptive Re-Planning to Account for Independent Motions Between Multiple Targets During Radiotherapy of Lung Cancer

Energy Technology Data Exchange (ETDEWEB)

Liu, F; Tai, A; Ahunbay, E; Gore, E; Johnstone, C; Li, X [Medical College of Wisconsin, Milwaukee, WI (United States)

2014-06-15

Purpose: To quantify interfractional independent motions between multiple targets in radiotherapy (RT) of lung cancer, and to study the dosimetric benefits of an online adaptive replanning method to account for these variations. Methods: Ninety five diagnostic-quality daily CTs acquired for 9 lung cancer patients treated with IGRT using an in-room CT (CTVision, Siemens) were analyzed. On each daily CT set, contours of the targets (GTV, CTV, or involved nodes) and organs at risk were generated by populating the planning contours using an auto-segmentation tool (ABAS, Elekta) with manual editing. For each patient, an IMRT plan was generated based on the planning CT with a prescription dose of 60 Gy in 2Gy fractions. Three plans were generated and compared for each daily CT set: an IGRT (repositioning) plan by copying the original plan with the required shifts, an online adaptive plan by rapidly modifying the aperture shapes and segment weights of the original plan to conform to the daily anatomy, and a new fully re-optimized plan based on the daily CT using a planning system (Panther, Prowess). Results: The daily deviations of the distance between centers of masses of the targets from the plans varied daily from -10 to 8 mm with an average −0.9±4.1 mm (one standard deviation). The average CTV V100 are 99.0±0.7%, 97.9±2.8%, 99.0±0.6%, and 99.1±0.6%, and the lung V20 Gy 928±332 cc, 944±315 cc, 917±300 cc, and 891±295 cc for the original, repositioning, adaptive, and re-optimized plans, respectively. Wilcoxon signed-rank tests show that the adaptive plans are statistically significantly better than the repositioning plans and comparable with the reoptimized plans. Conclusion: There exist unpredictable, interfractional, relative volume changes and independent motions between multiple targets during lung cancer RT which cannot be accounted for by the current IGRT repositioning but can be corrected by the online adaptive replanning method.

A scalable method for parallelizing sampling-based motion planning algorithms

KAUST Repository

Jacobs, Sam Ade; Manavi, Kasra; Burgos, Juan; Denny, Jory; Thomas, Shawna; Amato, Nancy M.

2012-01-01

This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine. © 2012 IEEE.

A scalable method for parallelizing sampling-based motion planning algorithms

KAUST Repository

Jacobs, Sam Ade

2012-05-01

This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine. © 2012 IEEE.

Limited Impact of Setup and Range Uncertainties, Breathing Motion, and Interplay Effects in Robustly Optimized Intensity Modulated Proton Therapy for Stage III Non-small Cell Lung Cancer

NARCIS (Netherlands)

Inoue, Tatsuya; Widder, Joachim; van Dijk, Lisanne V; Takegawa, Hideki; Koizumi, Masahiko; Takashina, Masaaki; Usui, Keisuke; Kurokawa, Chie; Sugimoto, Satoru; Saito, Anneyuko I; Sasai, Keisuke; Van't Veld, Aart A; Langendijk, Johannes A; Korevaar, Erik W

2016-01-01

Purpose: To investigate the impact of setup and range uncertainties, breathing motion, and interplay effects using scanning pencil beams in robustly optimized intensity modulated proton therapy (IMPT) for stage III non-small cell lung cancer (NSCLC). Methods and Materials: Three-field IMPT plans

Linear Temporal Logic-based Mission Planning

OpenAIRE

Anil Kumar; Rahul Kala

2016-01-01

In this paper, we describe the Linear Temporal Logic-based reactive motion planning. We address the problem of motion planning for mobile robots, wherein the goal specification of planning is given in complex environments. The desired task specification may consist of complex behaviors of the robot, including specifications for environment constraints, need of task optimality, obstacle avoidance, rescue specifications, surveillance specifications, safety specifications, etc. We use Linear Tem...

Optimization of stereotactic body radiotherapy treatment planning using a multicriteria optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Ghandour, Sarah; Cosinschi, Adrien; Mazouni, Zohra; Pachoud, Marc; Matzinger, Oscar [Riviera-Chablais Hospital, Vevey (Switzerland). Cancer Center, Radiotherapy Dept.

2016-07-01

To provide high-quality and efficient dosimetric planning for various types of stereotactic body radiotherapy (SBRT) for tumor treatment using a multicriteria optimization (MCO) technique fine-tuned with direct machine parameter optimization (DMPO). Eighteen patients with lung (n = 11), liver (n = 5) or adrenal cell cancer (n = 2) were treated using SBRT in our clinic between December 2014 and June 2015. Plans were generated using the RayStation trademark Treatment Planning System (TPS) with the VMAT technique. Optimal deliverable SBRT plans were first generated using an MCO algorithm to find a well-balanced tradeoff between tumor control and normal tissue sparing in an efficient treatment planning time. Then, the deliverable plan was post-processed using the MCO solution as the starting point for the DMPO algorithm to improve the dose gradient around the planning target volume (PTV) while maintaining the clinician's priorities. The dosimetric quality of the plans was evaluated using dose-volume histogram (DVH) parameters, which account for target coverage and the sparing of healthy tissue, as well as the CI100 and CI50 conformity indexes. Using a combination of the MCO and DMPO algorithms showed that the treatment plans were clinically optimal and conformed to all organ risk dose volume constraints reported in the literature, with a computation time of approximately one hour. The coverage of the PTV (D99% and D95%) and sparing of organs at risk (OAR) were similar between the MCO and MCO + DMPO plans, with no significant differences (p > 0.05) for all the SBRT plans. The average CI100 and CI50 values using MCO + DMPO were significantly better than those with MCO alone (p < 0.05). The MCO technique allows for convergence on an optimal solution for SBRT within an efficient planning time. The combination of the MCO and DMPO techniques yields a better dose gradient, especially for lung tumors.

Using Optimization to Improve Test Planning

Science.gov (United States)

2017-09-01

OPTIMIZATION TO IMPROVE TEST PLANNING by Arlene M. Payne September 2017 Thesis Advisor: Jeffrey E. Kline Second Reader: Oleg A. Yakimenko THIS... Project (0704-0188) Washington, DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE September 2017 3. REPORT TYPE AND DATES COVERED Master’s...thesis 4. TITLE AND SUBTITLE USING OPTIMIZATION TO IMPROVE TEST PLANNING 5. FUNDING NUMBERS 6. AUTHOR(S) Arlene M. Payne 7. PERFORMING ORGANIZATION

Improving IMRT-plan quality with MLC leaf position refinement post plan optimization

International Nuclear Information System (INIS)

Niu Ying; Zhang Guowei; Berman, Barry L.; Parke, William C.; Yi Byongyong; Yu, Cedric X.

2012-01-01

Purpose: In intensity-modulated radiation therapy (IMRT) planning, reducing the pencil-beam size may lead to a significant improvement in dose conformity, but also increase the time needed for the dose calculation and plan optimization. The authors develop and evaluate a postoptimization refinement (POpR) method, which makes fine adjustments to the multileaf collimator (MLC) leaf positions after plan optimization, enhancing the spatial precision and improving the plan quality without a significant impact on the computational burden. Methods: The authors’ POpR method is implemented using a commercial treatment planning system based on direct aperture optimization. After an IMRT plan is optimized using pencil beams with regular pencil-beam step size, a greedy search is conducted by looping through all of the involved MLC leaves to see if moving the MLC leaf in or out by half of a pencil-beam step size will improve the objective function value. The half-sized pencil beams, which are used for updating dose distribution in the greedy search, are derived from the existing full-sized pencil beams without need for further pencil-beam dose calculations. A benchmark phantom case and a head-and-neck (HN) case are studied for testing the authors’ POpR method. Results: Using a benchmark phantom and a HN case, the authors have verified that their POpR method can be an efficient technique in the IMRT planning process. Effectiveness of POpR is confirmed by noting significant improvements in objective function values. Dosimetric benefits of POpR are comparable to those of using a finer pencil-beam size from the optimization start, but with far less computation and time. Conclusions: The POpR is a feasible and practical method to significantly improve IMRT-plan quality without compromising the planning efficiency.

Design and implementation of motion planning of inspection and maintenance robot for ITER-like vessel

Energy Technology Data Exchange (ETDEWEB)

Wang, Hesheng; Lai, Yinping [Department of Automation, Shanghai Jiao Tong University, Shanghai 200240 (China); Key Laboratory of System Control and Information Processing, Ministry of Education of China (China); Chen, Weidong, E-mail: wdchen@sjtu.edu.cn [Department of Automation, Shanghai Jiao Tong University, Shanghai 200240 (China); Key Laboratory of System Control and Information Processing, Ministry of Education of China (China); Cao, Qixin [Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240 (China)

2015-12-15

Robot motion planning is a fundamental problem to ensure the robot executing the task without clashes, fast and accurately in a special environment. In this paper, a motion planning of a 12 DOFs remote handling robot used for inspecting the working state of the ITER-like vessel and maintaining key device components is proposed and implemented. Firstly, the forward and inverse kinematics are given by analytic method. The work space and posture space of this manipulator are both considered. Then the motion planning is divided into three stages: coming out of the cassette mover, moving along the in-vessel center line, and inspecting the D-shape section. Lastly, the result of experiments verified the performance of the motion design method. In addition, the task of unscrewing/screwing the screw demonstrated the feasibility of system in function.

Adaptive Motion Planning in Bin-Picking with Object Uncertainties

DEFF Research Database (Denmark)

Iversen, Thomas Fridolin; Ellekilde, Lars-Peter; Miró, Jaime Valls

2017-01-01

Doing motion planning for bin-picking with object uncertainties requires either a re-grasp of picked objects or an online sensor system. Using the latter is advantageous in terms of computational time, as no time is wasted doing an extra pick and place action. It does, however, put extra...... requirements on the motion planner, as the target position may change on-the-fly. This paper solves that problem by using a state adjusting Partial Observable Markov Decision Process, where the state space is modified between runs, to better fit earlier solved problems. The approach relies on a set...

An optimal control strategy for two-dimensional motion camouflage with non-holonimic constraints.

Science.gov (United States)

Rañó, Iñaki

2012-07-01

Motion camouflage is a stealth behaviour observed both in hover-flies and in dragonflies. Existing controllers for mimicking motion camouflage generate this behaviour on an empirical basis or without considering the kinematic motion restrictions present in animal trajectories. This study summarises our formal contributions to solve the generation of motion camouflage as a non-linear optimal control problem. The dynamics of the system capture the kinematic restrictions to motion of the agents, while the performance index ensures camouflage trajectories. An extensive set of simulations support the technique, and a novel analysis of the obtained trajectories contributes to our understanding of possible mechanisms to obtain sensor based motion camouflage, for instance, in mobile robots.

The motion planning problem and exponential stabilization of a heavy chain. Part II

OpenAIRE

Piotr Grabowski

2008-01-01

This is the second part of paper [P. Grabowski, The motion planning problem and exponential stabilization of a heavy chain. Part I, to appear in International Journal of Control], where a model of a heavy chain system with a punctual load (tip mass) in the form of a system of partial differential equations was interpreted as an abstract semigroup system and then analysed on a Hilbert state space. In particular, in [P. Grabowski, The motion planning problem and exponential stabilization of a h...

Temporo-spatial IMRT optimization: concepts, implementation and initial results

International Nuclear Information System (INIS)

Trofimov, Alexei; Rietzel, Eike; Lu Hsiaoming; Martin, Benjamin; Jiang, Steve; Chen, George T Y; Bortfeld, Thomas

2005-01-01

With the recent availability of 4D-CT, the accuracy of information on internal organ motion during respiration has improved significantly. We investigate the utility of organ motion information in IMRT treatment planning, using an in-house prototype optimization system. Four approaches are compared: (1) planning with optimized margins, based on motion information; (2) the 'motion kernel' approach, in which a more accurate description of the dose deposit from a pencil beam to a moving target is achieved either through time-weighted averaging of influence matrices, calculated for different instances of anatomy (subsets of 4D-CT data, corresponding to various phases of motion) or through convolution of the pencil beam kernel with the probability density function describing the target motion; (3) optimal gating, or tracking with beam intensity maps optimized independently for each instance of anatomy; and (4) optimal tracking with beam intensity maps optimized simultaneously for all instances of anatomy. The optimization is based on a gradient technique and can handle both physical (dose-volume) and equivalent uniform dose constraints. Optimization requires voxel mapping from phase to phase in order to score the dose in individual voxels as they move. The results show that, compared to the other approaches, margin expansion has a significant disadvantage by substantially increasing the integral dose to patient. While gating or tracking result in the best dose conformation to the target, the former elongates treatment time, and the latter significantly complicates the delivery procedure. The 'motion kernel' approach does not provide a dosimetric advantage, compared to optimal tracking or gating, but might lead to more efficient delivery. A combination of gating with the 'motion kernel' or margin expansion approach will increase the duty cycle and may provide one with the most efficient solution, in terms of complexity of the delivery procedure and dose conformality to

Computational optimization techniques applied to microgrids planning

DEFF Research Database (Denmark)

Gamarra, Carlos; Guerrero, Josep M.

2015-01-01

Microgrids are expected to become part of the next electric power system evolution, not only in rural and remote areas but also in urban communities. Since microgrids are expected to coexist with traditional power grids (such as district heating does with traditional heating systems......), their planning process must be addressed to economic feasibility, as a long-term stability guarantee. Planning a microgrid is a complex process due to existing alternatives, goals, constraints and uncertainties. Usually planning goals conflict each other and, as a consequence, different optimization problems...... appear along the planning process. In this context, technical literature about optimization techniques applied to microgrid planning have been reviewed and the guidelines for innovative planning methodologies focused on economic feasibility can be defined. Finally, some trending techniques and new...

Optimization of importance factors in inverse planning

International Nuclear Information System (INIS)

Xing, L.

1999-01-01

Inverse treatment planning starts with a treatment objective and obtains the solution by optimizing an objective function. The clinical objectives are usually multifaceted and potentially incompatible with one another. A set of importance factors is often incorporated in the objective function to parametrize trade-off strategies and to prioritize the dose conformality in different anatomical structures. Whereas the general formalism remains the same, different sets of importance factors characterize plans of obviously different flavour and thus critically determine the final plan. Up to now, the determination of these parameters has been a 'guessing' game based on empirical knowledge because the final dose distribution depends on the parameters in a complex and implicit way. The influence of these parameters is not known until the plan optimization is completed. In order to compromise properly the conflicting requirements of the target and sensitive structures, the parameters are usually adjusted through a trial-and-error process. In this paper, a method to estimate these parameters computationally is proposed and an iterative computer algorithm is described to determine these parameters numerically. The treatment plan selection is done in two steps. First, a set of importance factors are chosen and the corresponding beam parameters (e.g. beam profiles) are optimized under the guidance of a quadratic objective function using an iterative algorithm reported earlier. The 'optimal' plan is then evaluated by an additional scoring function. The importance factors in the objective function are accordingly adjusted to improve the ranking of the plan. For every change in the importance factors, the beam parameters need to be re-optimized. This process continues in an iterative fashion until the scoring function is saturated. The algorithm was applied to two clinical cases and the results demonstrated that it has the potential to improve significantly the existing method of

Path-Constrained Motion Planning for Robotics Based on Kinematic Constraints

NARCIS (Netherlands)

Dijk, van N.J.M.; Wouw, van de N.; Pancras, W.C.M.; Nijmeijer, H.

2007-01-01

Common robotic tracking tasks consist of motions along predefined paths. The design of time-optimal path-constrained trajectories for robotic applications is discussed in this paper. To increase industrial applicability, the proposed method accounts for robot kinematics together with actuator

Automatic interactive optimization for volumetric modulated arc therapy planning

International Nuclear Information System (INIS)

Tol, Jim P; Dahele, Max; Peltola, Jarkko; Nord, Janne; Slotman, Ben J; Verbakel, Wilko FAR

2015-01-01

Intensity modulated radiotherapy treatment planning for sites with many different organs-at-risk (OAR) is complex and labor-intensive, making it hard to obtain consistent plan quality. With the aim of addressing this, we developed a program (automatic interactive optimizer, AIO) designed to automate the manual interactive process for the Eclipse treatment planning system. We describe AIO and present initial evaluation data. Our current institutional volumetric modulated arc therapy (RapidArc) planning approach for head and neck tumors places 3-4 adjustable OAR optimization objectives along the dose-volume histogram (DVH) curve that is displayed in the optimization window. AIO scans this window and uses color-coding to differentiate between the DVH-lines, allowing it to automatically adjust the location of the optimization objectives frequently and in a more consistent fashion. We compared RapidArc AIO plans (using 9 optimization objectives per OAR) with the clinical plans of 10 patients, and evaluated optimal AIO settings. AIO consistency was tested by replanning a single patient 5 times. Average V95&V107 of the boost planning target volume (PTV) and V95 of the elective PTV differed by ≤0.5%, while average elective PTV V107 improved by 1.5%. Averaged over all patients, AIO reduced mean doses to individual salivary structures by 0.9-1.6Gy and provided mean dose reductions of 5.6Gy and 3.9Gy to the composite swallowing structures and oral cavity, respectively. Re-running AIO five times, resulted in the aforementioned parameters differing by less than 3%. Using the same planning strategy as manually optimized head and neck plans, AIO can automate the interactive Eclipse treatment planning process and deliver dosimetric improvements over existing clinical plans

Optimizing 4-Dimensional Magnetic Resonance Imaging Data Sampling for Respiratory Motion Analysis of Pancreatic Tumors

Energy Technology Data Exchange (ETDEWEB)

Stemkens, Bjorn, E-mail: b.stemkens@umcutrecht.nl [Department of Radiotherapy, University Medical Center Utrecht, Utrecht (Netherlands); Tijssen, Rob H.N. [Department of Radiotherapy, University Medical Center Utrecht, Utrecht (Netherlands); Senneville, Baudouin D. de [Imaging Division, University Medical Center Utrecht, Utrecht (Netherlands); L' Institut de Mathématiques de Bordeaux, Unité Mixte de Recherche 5251, Centre National de la Recherche Scientifique/University of Bordeaux, Bordeaux (France); Heerkens, Hanne D.; Vulpen, Marco van; Lagendijk, Jan J.W.; Berg, Cornelis A.T. van den [Department of Radiotherapy, University Medical Center Utrecht, Utrecht (Netherlands)

2015-03-01

Purpose: To determine the optimum sampling strategy for retrospective reconstruction of 4-dimensional (4D) MR data for nonrigid motion characterization of tumor and organs at risk for radiation therapy purposes. Methods and Materials: For optimization, we compared 2 surrogate signals (external respiratory bellows and internal MRI navigators) and 2 MR sampling strategies (Cartesian and radial) in terms of image quality and robustness. Using the optimized protocol, 6 pancreatic cancer patients were scanned to calculate the 4D motion. Region of interest analysis was performed to characterize the respiratory-induced motion of the tumor and organs at risk simultaneously. Results: The MRI navigator was found to be a more reliable surrogate for pancreatic motion than the respiratory bellows signal. Radial sampling is most benign for undersampling artifacts and intraview motion. Motion characterization revealed interorgan and interpatient variation, as well as heterogeneity within the tumor. Conclusions: A robust 4D-MRI method, based on clinically available protocols, is presented and successfully applied to characterize the abdominal motion in a small number of pancreatic cancer patients.

Motion planning with complete knowledge using a colored SOM.

Science.gov (United States)

Vleugels, J; Kok, J N; Overmars, M

1997-01-01

The motion planning problem requires that a collision-free path be determined for a robot moving amidst a fixed set of obstacles. Most neural network approaches to this problem are for the situation in which only local knowledge about the configuration space is available. The main goal of the paper is to show that neural networks are also suitable tools in situations with complete knowledge of the configuration space. In this paper we present an approach that combines a neural network and deterministic techniques. We define a colored version of Kohonen's self-organizing map that consists of two different classes of nodes. The network is presented with random configurations of the robot and, from this information, it constructs a road map of possible motions in the work space. The map is a growing network, and different nodes are used to approximate boundaries of obstacles and the Voronoi diagram of the obstacles, respectively. In a second phase, the positions of the two kinds of nodes are combined to obtain the road map. In this way a number of typical problems with small obstacles and passages are avoided, and the required number of nodes for a given accuracy is within reasonable limits. This road map is searched to find a motion connecting the given source and goal configurations of the robot. The algorithm is simple and general; the only specific computation that is required is a check for intersection of two polygons. We implemented the algorithm for planar robots allowing both translation and rotation and experiments show that compared to conventional techniques it performs well, even for difficult motion planning scenes.

Knowledge-Oriented Physics-Based Motion Planning for Grasping Under Uncertainty

OpenAIRE

Ud Din, Muhayy; Akbari, Aliakbar; Rosell Gratacòs, Jan

2017-01-01

Grasping an object in unstructured and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exits. High-level knowledge and reasoning processes, as well as the allowing of interaction between objects, can enhance the planning efficiency in such environments. In this direction, this study proposes a knowledge-oriented physics-based motion planning approach for a hand-arm system that uses a high-level knowledge-based reasoning to partition the wor...

Motion planning for autonomous vehicle based on radial basis function neural network in unstructured environment.

Science.gov (United States)

Chen, Jiajia; Zhao, Pan; Liang, Huawei; Mei, Tao

2014-09-18

The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF) neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of the motion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existing well-known Rapidly-exploring Random Tree (RRT) method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality.

Integrated production planning and control: A multi-objective optimization model

Directory of Open Access Journals (Sweden)

Cheng Wang

2013-09-01

Full Text Available Purpose: Production planning and control has crucial impact on the production and business activities of enterprise. Enterprise Resource Planning (ERP is the most popular resources planning and management system, however there are some shortcomings and deficiencies in the production planning and control because its core component is still the Material Requirements Planning (MRP. For the defects of ERP system, many local improvement and optimization schemes have been proposed, and improve the feasibility and practicality of the plan in some extent, but study considering the whole planning system optimization in the multiple performance management objectives and achieving better application performance is less. The purpose of this paper is to propose a multi-objective production planning optimization model Based on the point of view of the integration of production planning and control, in order to achieve optimization and control of enterprise manufacturing management. Design/methodology/approach: On the analysis of ERP planning system’s defects and disadvantages, and related research and literature, a multi-objective production planning optimization model is proposed, in addition to net demand and capacity, multiple performance management objectives, such as on-time delivery, production balance, inventory, overtime production, are considered incorporating into the examination scope of the model, so that the manufacturing process could be management and controlled Optimally between multiple objectives. The validity and practicability of the model will be verified by the instance in the last part of the paper. Findings: The main finding is that production planning management of manufacturing enterprise considers not only the capacity and materials, but also a variety of performance management objectives in the production process, and building a multi-objective optimization model can effectively optimize the management and control of enterprise

Improving best-phase image quality in cardiac CT by motion correction with MAM optimization

Energy Technology Data Exchange (ETDEWEB)

Rohkohl, Christopher; Bruder, Herbert; Stierstorfer, Karl [Siemens AG, Healthcare Sector, Siemensstrasse 1, 91301 Forchheim (Germany); Flohr, Thomas [Siemens AG, Healthcare Sector, Siemensstrasse 1, 91301 Forchheim (Germany); Institute of Diagnostic Radiology, Eberhard Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen (Germany)

2013-03-15

Purpose: Research in image reconstruction for cardiac CT aims at using motion correction algorithms to improve the image quality of the coronary arteries. The key to those algorithms is motion estimation, which is currently based on 3-D/3-D registration to align the structures of interest in images acquired in multiple heart phases. The need for an extended scan data range covering several heart phases is critical in terms of radiation dose to the patient and limits the clinical potential of the method. Furthermore, literature reports only slight quality improvements of the motion corrected images when compared to the most quiet phase (best-phase) that was actually used for motion estimation. In this paper a motion estimation algorithm is proposed which does not require an extended scan range but works with a short scan data interval, and which markedly improves the best-phase image quality. Methods: Motion estimation is based on the definition of motion artifact metrics (MAM) to quantify motion artifacts in a 3-D reconstructed image volume. The authors use two different MAMs, entropy, and positivity. By adjusting the motion field parameters, the MAM of the resulting motion-compensated reconstruction is optimized using a gradient descent procedure. In this way motion artifacts are minimized. For a fast and practical implementation, only analytical methods are used for motion estimation and compensation. Both the MAM-optimization and a 3-D/3-D registration-based motion estimation algorithm were investigated by means of a computer-simulated vessel with a cardiac motion profile. Image quality was evaluated using normalized cross-correlation (NCC) with the ground truth template and root-mean-square deviation (RMSD). Four coronary CT angiography patient cases were reconstructed to evaluate the clinical performance of the proposed method. Results: For the MAM-approach, the best-phase image quality could be improved for all investigated heart phases, with a maximum

Improving best-phase image quality in cardiac CT by motion correction with MAM optimization

International Nuclear Information System (INIS)

Rohkohl, Christopher; Bruder, Herbert; Stierstorfer, Karl; Flohr, Thomas

2013-01-01

Purpose: Research in image reconstruction for cardiac CT aims at using motion correction algorithms to improve the image quality of the coronary arteries. The key to those algorithms is motion estimation, which is currently based on 3-D/3-D registration to align the structures of interest in images acquired in multiple heart phases. The need for an extended scan data range covering several heart phases is critical in terms of radiation dose to the patient and limits the clinical potential of the method. Furthermore, literature reports only slight quality improvements of the motion corrected images when compared to the most quiet phase (best-phase) that was actually used for motion estimation. In this paper a motion estimation algorithm is proposed which does not require an extended scan range but works with a short scan data interval, and which markedly improves the best-phase image quality. Methods: Motion estimation is based on the definition of motion artifact metrics (MAM) to quantify motion artifacts in a 3-D reconstructed image volume. The authors use two different MAMs, entropy, and positivity. By adjusting the motion field parameters, the MAM of the resulting motion-compensated reconstruction is optimized using a gradient descent procedure. In this way motion artifacts are minimized. For a fast and practical implementation, only analytical methods are used for motion estimation and compensation. Both the MAM-optimization and a 3-D/3-D registration-based motion estimation algorithm were investigated by means of a computer-simulated vessel with a cardiac motion profile. Image quality was evaluated using normalized cross-correlation (NCC) with the ground truth template and root-mean-square deviation (RMSD). Four coronary CT angiography patient cases were reconstructed to evaluate the clinical performance of the proposed method. Results: For the MAM-approach, the best-phase image quality could be improved for all investigated heart phases, with a maximum

Integrals of Motion for Discrete-Time Optimal Control Problems

OpenAIRE

Torres, Delfim F. M.

2003-01-01

We obtain a discrete time analog of E. Noether's theorem in Optimal Control, asserting that integrals of motion associated to the discrete time Pontryagin Maximum Principle can be computed from the quasi-invariance properties of the discrete time Lagrangian and discrete time control system. As corollaries, results for first-order and higher-order discrete problems of the calculus of variations are obtained.

Discrepancies between selected Pareto optimal plans and final deliverable plans in radiotherapy multi-criteria optimization.

Science.gov (United States)

Kyroudi, Archonteia; Petersson, Kristoffer; Ghandour, Sarah; Pachoud, Marc; Matzinger, Oscar; Ozsahin, Mahmut; Bourhis, Jean; Bochud, François; Moeckli, Raphaël

2016-08-01

Multi-criteria optimization provides decision makers with a range of clinical choices through Pareto plans that can be explored during real time navigation and then converted into deliverable plans. Our study shows that dosimetric differences can arise between the two steps, which could compromise the clinical choices made during navigation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Optimal partial-arcs in VMAT treatment planning

International Nuclear Information System (INIS)

Wala, Jeremiah; Salari, Ehsan; Chen Wei; Craft, David

2012-01-01

We present a method for improving the delivery efficiency of VMAT by extending the recently published VMAT treatment planning algorithm vmerge to automatically generate optimal partial-arc plans. A high-quality initial plan is created by solving a convex multicriteria optimization problem using 180 equi-spaced beams. This initial plan is used to form a set of dose constraints, and a set of partial-arc plans is created by searching the space of all possible partial-arc plans that satisfy these constraints. For each partial-arc, an iterative fluence map merging and sequencing algorithm (vmerge) is used to improve the delivery efficiency. Merging continues as long as the dose quality is maintained above a user-defined threshold. The final plan is selected as the partial-arc with the lowest treatment time. The complete algorithm is called pmerge. Partial-arc plans are created using pmerge for a lung, liver and prostate case, with final treatment times of 127, 245 and 147 s. Treatment times using full arcs with vmerge are 211, 357 and 178 s. The mean doses to the critical structures for the vmerge and pmerge plans are kept within 5% of those in the initial plan, and the target volume covered by the prescription isodose is maintained above 98% for the pmerge and vmerge plans. Additionally, we find that the angular distribution of fluence in the initial plans is predictive of the start and end angles of the optimal partial-arc. We conclude that VMAT delivery efficiency can be improved by employing partial-arcs without compromising dose quality, and that partial-arcs are most applicable to cases with non-centralized targets. (paper)

Rough terrain motion planning for actively reconfigurable mobile robots

International Nuclear Information System (INIS)

Brunner, Michael

2015-01-01

In the aftermath of the Tohoku earthquake and the nuclear meltdown at the power plant of Fukushima Daiichi in 2011, reconfigurable robots like the iRobot Packbot were deployed. Instead of humans, the robots were used to investigate contaminated areas. Other incidents are the two major earthquakes in Northern Italy in May 2012. Besides many casualties, a large number of historical buildings was severely damaged. Due to the imminent danger of collapse, it was too dangerous for rescue personnel to enter many of the buildings. Therefore, the sites were inspected by reconfigurable robots, which are able to traverse the rubble and debris of the partially destroyed buildings. This thesis develops a navigation system enabling wheeled and tracked robots to safely traverse rough terrain and challenging structures. It consists of a planning mechanism and a controller. The focus of this thesis, however, is on the contribution to motion planning. The planning scheme employs a hierarchical approach to motion planning for actively reconfigurable robots in rough environments. Using a map of the environment the algorithm estimates the traversability under the consideration of uncertainties. Based on this analysis, an initial path search determines an approximate solution with respect to the robot's operating limits.Subsequently, a detailed planning step refines the initial path where it is required. The refinement step considers the robot's actuators and stability in addition to the quantities of the first search. Determining the robot-terrain interaction is very important in rough terrain. This thesis presents two path refinement approaches: a deterministic and a randomized approach. The experimental evaluation investigates the separate components of the planning scheme, the robot-terrain interaction for instance.In simulation as well as in real world experiments the evaluation demonstrates the necessity of such a planning algorithm in rough terrain and it provides

Rough terrain motion planning for actively reconfigurable mobile robots

Energy Technology Data Exchange (ETDEWEB)

Brunner, Michael

2015-02-05

In the aftermath of the Tohoku earthquake and the nuclear meltdown at the power plant of Fukushima Daiichi in 2011, reconfigurable robots like the iRobot Packbot were deployed. Instead of humans, the robots were used to investigate contaminated areas. Other incidents are the two major earthquakes in Northern Italy in May 2012. Besides many casualties, a large number of historical buildings was severely damaged. Due to the imminent danger of collapse, it was too dangerous for rescue personnel to enter many of the buildings. Therefore, the sites were inspected by reconfigurable robots, which are able to traverse the rubble and debris of the partially destroyed buildings. This thesis develops a navigation system enabling wheeled and tracked robots to safely traverse rough terrain and challenging structures. It consists of a planning mechanism and a controller. The focus of this thesis, however, is on the contribution to motion planning. The planning scheme employs a hierarchical approach to motion planning for actively reconfigurable robots in rough environments. Using a map of the environment the algorithm estimates the traversability under the consideration of uncertainties. Based on this analysis, an initial path search determines an approximate solution with respect to the robot's operating limits.Subsequently, a detailed planning step refines the initial path where it is required. The refinement step considers the robot's actuators and stability in addition to the quantities of the first search. Determining the robot-terrain interaction is very important in rough terrain. This thesis presents two path refinement approaches: a deterministic and a randomized approach. The experimental evaluation investigates the separate components of the planning scheme, the robot-terrain interaction for instance.In simulation as well as in real world experiments the evaluation demonstrates the necessity of such a planning algorithm in rough terrain and it provides

Frustration-guided motion planning reveals conformational transitions in proteins.

Science.gov (United States)

Budday, Dominik; Fonseca, Rasmus; Leyendecker, Sigrid; van den Bedem, Henry

2017-10-01

Proteins exist as conformational ensembles, exchanging between substates to perform their function. Advances in experimental techniques yield unprecedented access to structural snapshots of their conformational landscape. However, computationally modeling how proteins use collective motions to transition between substates is challenging owing to a rugged landscape and large energy barriers. Here, we present a new, robotics-inspired motion planning procedure called dCC-RRT that navigates the rugged landscape between substates by introducing dynamic, interatomic constraints to modulate frustration. The constraints balance non-native contacts and flexibility, and instantaneously redirect the motion towards sterically favorable conformations. On a test set of eight proteins determined in two conformations separated by, on average, 7.5 Å root mean square deviation (RMSD), our pathways reduced the Cα atom RMSD to the goal conformation by 78%, outperforming peer methods. We then applied dCC-RRT to examine how collective, small-scale motions of four side-chains in the active site of cyclophilin A propagate through the protein. dCC-RRT uncovered a spatially contiguous network of residues linked by steric interactions and collective motion connecting the active site to a recently proposed, non-canonical capsid binding site 25 Å away, rationalizing NMR and multi-temperature crystallography experiments. In all, dCC-RRT can reveal detailed, all-atom molecular mechanisms for small and large amplitude motions. Source code and binaries are freely available at https://github.com/ExcitedStates/KGS/. © 2017 Wiley Periodicals, Inc.

Analysis of Radiation Treatment Planning by Dose Calculation and Optimization Algorithm

Energy Technology Data Exchange (ETDEWEB)

Kim, Dae Sup; Yoon, In Ha; Lee, Woo Seok; Baek, Geum Mun [Dept. of Radiation Oncology, Asan Medical Center, Seoul (Korea, Republic of)

2012-09-15

Analyze the Effectiveness of Radiation Treatment Planning by dose calculation and optimization algorithm, apply consideration of actual treatment planning, and then suggest the best way to treatment planning protocol. The treatment planning system use Eclipse 10.0. (Varian, USA). PBC (Pencil Beam Convolution) and AAA (Anisotropic Analytical Algorithm) Apply to Dose calculation, DVO (Dose Volume Optimizer 10.0.28) used for optimized algorithm of Intensity Modulated Radiation Therapy (IMRT), PRO II (Progressive Resolution Optimizer V 8.9.17) and PRO III (Progressive Resolution Optimizer V 10.0.28) used for optimized algorithm of VAMT. A phantom for experiment virtually created at treatment planning system, 30x30x30 cm sized, homogeneous density (HU: 0) and heterogeneous density that inserted air assumed material (HU: -1,000). Apply to clinical treatment planning on the basis of general treatment planning feature analyzed with Phantom planning. In homogeneous density phantom, PBC and AAA show 65.2% PDD (6 MV, 10 cm) both, In heterogeneous density phantom, also show similar PDD value before meet with low density material, but they show different dose curve in air territory, PDD 10 cm showed 75%, 73% each after penetrate phantom. 3D treatment plan in same MU, AAA treatment planning shows low dose at Lung included area. 2D POP treatment plan with 15 MV of cervical vertebral region include trachea and lung area, Conformity Index (ICRU 62) is 0.95 in PBC calculation and 0.93 in AAA. DVO DVH and Dose calculation DVH are showed equal value in IMRT treatment plan. But AAA calculation shows lack of dose compared with DVO result which is satisfactory condition. Optimizing VMAT treatment plans using PRO II obtained results were satisfactory, but lower density area showed lack of dose in dose calculations. PRO III, but optimizing the dose calculation results were similar with optimized the same conditions once more. In this study, do not judge the rightness of the dose

Analysis of Radiation Treatment Planning by Dose Calculation and Optimization Algorithm

International Nuclear Information System (INIS)

Kim, Dae Sup; Yoon, In Ha; Lee, Woo Seok; Baek, Geum Mun

2012-01-01

Analyze the Effectiveness of Radiation Treatment Planning by dose calculation and optimization algorithm, apply consideration of actual treatment planning, and then suggest the best way to treatment planning protocol. The treatment planning system use Eclipse 10.0. (Varian, USA). PBC (Pencil Beam Convolution) and AAA (Anisotropic Analytical Algorithm) Apply to Dose calculation, DVO (Dose Volume Optimizer 10.0.28) used for optimized algorithm of Intensity Modulated Radiation Therapy (IMRT), PRO II (Progressive Resolution Optimizer V 8.9.17) and PRO III (Progressive Resolution Optimizer V 10.0.28) used for optimized algorithm of VAMT. A phantom for experiment virtually created at treatment planning system, 30x30x30 cm sized, homogeneous density (HU: 0) and heterogeneous density that inserted air assumed material (HU: -1,000). Apply to clinical treatment planning on the basis of general treatment planning feature analyzed with Phantom planning. In homogeneous density phantom, PBC and AAA show 65.2% PDD (6 MV, 10 cm) both, In heterogeneous density phantom, also show similar PDD value before meet with low density material, but they show different dose curve in air territory, PDD 10 cm showed 75%, 73% each after penetrate phantom. 3D treatment plan in same MU, AAA treatment planning shows low dose at Lung included area. 2D POP treatment plan with 15 MV of cervical vertebral region include trachea and lung area, Conformity Index (ICRU 62) is 0.95 in PBC calculation and 0.93 in AAA. DVO DVH and Dose calculation DVH are showed equal value in IMRT treatment plan. But AAA calculation shows lack of dose compared with DVO result which is satisfactory condition. Optimizing VMAT treatment plans using PRO II obtained results were satisfactory, but lower density area showed lack of dose in dose calculations. PRO III, but optimizing the dose calculation results were similar with optimized the same conditions once more. In this study, do not judge the rightness of the dose

Optimal piston motion for maximum net output work of Daniel cam engines with low heat rejection

International Nuclear Information System (INIS)

Badescu, Viorel

2015-01-01

Highlights: • The piston motion of low heat rejection compression ignition engines is optimized. • A realistic model taking into account the cooling system is developed. • The optimized cam is smaller for cylinders without thermal insulation. • The optimized cam size depends on ignition moment and cooling process intensity. - Abstract: Compression ignition engines based on classical tapper-crank systems cannot provide optimal piston motion. Cam engines are more appropriate for this purpose. In this paper the piston motion of a Daniel cam engine is optimized. Piston acceleration is taken as a control. The objective is to maximize the net output work during the compression and power strokes. A major research effort has been allocated in the last two decades for the development of low heat rejection engines. A thermally insulated cylinder is considered and a realistic model taking into account the cooling system is developed. The sinusoidal approximation of piston motion in the classical tapper-crank system overestimates the engine efficiency. The exact description of the piston motion in tapper-crank system is used here as a reference. The radiation process has negligible effects during the optimization. The approach with no constraint on piston acceleration is a reasonable approximation. The net output work is much larger (by 12–13%) for the optimized system than for the classical tapper-crank system, for similar thickness of cylinder walls and thermal insulation. Low heat rejection measures are not of significant importance for optimized cam engines. The optimized cam is smaller for a cylinder without thermal insulation than for an insulated cylinder (by up to 8%, depending on the local polar radius). The auto-ignition moment is not a parameter of significant importance for optimized cam engines. However, for given cylinder wall and insulation materials there is an optimum auto-ignition moment which maximizes the net output work. The optimum auto

Sci-Fri PM: Radiation Therapy, Planning, Imaging, and Special Techniques - 05: A novel respiratory motion simulation program for VMAT treatment plans: a phantom validation study

Energy Technology Data Exchange (ETDEWEB)

Hubley, Emily; Pierce, Greg; Ploquin, Nicolas [University of Calgary, Tom Baker Cancer Centre, Tom Baker Cancer Centre (Canada)

2016-08-15

Purpose: To develop and validate a computational method to simulate craniocaudal respiratory motion in a VMAT treatment plan. Methods: Three 4DCTs of the QUASAR respiratory motion phantom were acquired with a 2cm water-density spherical tumour embedded in cedar to simulate lung. The phantom was oscillating sinusoidally with an amplitude of 2cm and periods of 3, 4, and 5 seconds. An ITV was contoured and 5mm PTV margin was added. High and a low modulation factor VMAT plans were created for each scan. An in-house program was developed to simulate respiratory motion in the treatment plans by shifting the MLC leaf positions relative to the phantom. Each plan was delivered to the phantom and the dose was measured using Gafchromic film. The measured and calculated plans were compared using an absolute dose gamma analysis (3%/3mm). Results: The average gamma pass rate for the low modulation plan and high modulation plans were 91.1% and 51.4% respectively. The difference between the high and low modulation plans gamma pass rates is likely related to the different sampling frequency of the respiratory curve and the higher MLC leaf speeds in the high modulation plan. A high modulation plan has a slower gantry speed and therefore samples the breathing cycle at a coarser frequency leading to inaccuracies between the measured and planned doses. Conclusion: A simple program, including a novel method for increasing sampling frequency beyond the control point frequency, has been developed to simulate respiratory motion in VMAT plans by shifting the MLC leaf positions.

Sci-Fri PM: Radiation Therapy, Planning, Imaging, and Special Techniques - 05: A novel respiratory motion simulation program for VMAT treatment plans: a phantom validation study

International Nuclear Information System (INIS)

Hubley, Emily; Pierce, Greg; Ploquin, Nicolas

2016-01-01

Purpose: To develop and validate a computational method to simulate craniocaudal respiratory motion in a VMAT treatment plan. Methods: Three 4DCTs of the QUASAR respiratory motion phantom were acquired with a 2cm water-density spherical tumour embedded in cedar to simulate lung. The phantom was oscillating sinusoidally with an amplitude of 2cm and periods of 3, 4, and 5 seconds. An ITV was contoured and 5mm PTV margin was added. High and a low modulation factor VMAT plans were created for each scan. An in-house program was developed to simulate respiratory motion in the treatment plans by shifting the MLC leaf positions relative to the phantom. Each plan was delivered to the phantom and the dose was measured using Gafchromic film. The measured and calculated plans were compared using an absolute dose gamma analysis (3%/3mm). Results: The average gamma pass rate for the low modulation plan and high modulation plans were 91.1% and 51.4% respectively. The difference between the high and low modulation plans gamma pass rates is likely related to the different sampling frequency of the respiratory curve and the higher MLC leaf speeds in the high modulation plan. A high modulation plan has a slower gantry speed and therefore samples the breathing cycle at a coarser frequency leading to inaccuracies between the measured and planned doses. Conclusion: A simple program, including a novel method for increasing sampling frequency beyond the control point frequency, has been developed to simulate respiratory motion in VMAT plans by shifting the MLC leaf positions.

Multi-Robot Motion Planning: A Timed Automata Approach

OpenAIRE

Quottrup, Michael Melholt; Bak, Thomas; Izadi-Zamanabadi, Roozbeh

2004-01-01

This paper describes how a network of interacting timed automata can be used to model, analyze, and verify motion planning problems in a scenario with multiple robotic vehicles. The method presupposes an infra-structure of robots with feed-back controllers obeying simple restriction on a planar grid. The automata formalism merely presents a high-level model of environment, robots and control, but allows composition and formal symbolic reasoning about coordinated solutions. Composition is achi...

MO-B-BRB-03: Systems Engineering Tools for Treatment Planning Process Optimization in Radiation Medicine

International Nuclear Information System (INIS)

Kapur, A.

2015-01-01

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequential events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi

MO-B-BRB-03: Systems Engineering Tools for Treatment Planning Process Optimization in Radiation Medicine

Energy Technology Data Exchange (ETDEWEB)

Kapur, A. [Long Island Jewish Medical Center (United States)

2015-06-15

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequential events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi

Temporal Optimization Planning for Fleet Repositioning

DEFF Research Database (Denmark)

Tierney, Kevin; Jensen, Rune Møller

2011-01-01

Fleet repositioning problems pose a high financial bur- den on shipping firms, but have received little attention in the literature, despite their high importance to the shipping industry. Fleet repositioning problems are characterized by chains of interacting activities, but state-of-the-art pla......Fleet repositioning problems pose a high financial bur- den on shipping firms, but have received little attention in the literature, despite their high importance to the shipping industry. Fleet repositioning problems are characterized by chains of interacting activities, but state......-of-the-art planning and scheduling techniques do not offer cost models that are rich enough to represent essential objectives of these problems. To this end, we introduce a novel framework called Temporal Optimization Planning (TOP). TOP uses partial order planning to build optimization models associated...

On the role of modeling parameters in IMRT plan optimization

International Nuclear Information System (INIS)

Krause, Michael; Scherrer, Alexander; Thieke, Christian

2008-01-01

The formulation of optimization problems in intensity-modulated radiotherapy (IMRT) planning comprises the choice of various values such as function-specific parameters or constraint bounds. In current inverse planning programs that yield a single treatment plan for each optimization, it is often unclear how strongly these modeling parameters affect the resulting plan. This work investigates the mathematical concepts of elasticity and sensitivity to deal with this problem. An artificial planning case with a horse-shoe formed target with different opening angles surrounding a circular risk structure is studied. As evaluation functions the generalized equivalent uniform dose (EUD) and the average underdosage below and average overdosage beyond certain dose thresholds are used. A single IMRT plan is calculated for an exemplary parameter configuration. The elasticity and sensitivity of each parameter are then calculated without re-optimization, and the results are numerically verified. The results show the following. (1) elasticity can quantify the influence of a modeling parameter on the optimization result in terms of how strongly the objective function value varies under modifications of the parameter value. It also can describe how strongly the geometry of the involved planning structures affects the optimization result. (2) Based on the current parameter settings and corresponding treatment plan, sensitivity analysis can predict the optimization result for modified parameter values without re-optimization, and it can estimate the value intervals in which such predictions are valid. In conclusion, elasticity and sensitivity can provide helpful tools in inverse IMRT planning to identify the most critical parameters of an individual planning problem and to modify their values in an appropriate way

Derivative-free generation and interpolation of convex Pareto optimal IMRT plans

Science.gov (United States)

Hoffmann, Aswin L.; Siem, Alex Y. D.; den Hertog, Dick; Kaanders, Johannes H. A. M.; Huizenga, Henk

2006-12-01

In inverse treatment planning for intensity-modulated radiation therapy (IMRT), beamlet intensity levels in fluence maps of high-energy photon beams are optimized. Treatment plan evaluation criteria are used as objective functions to steer the optimization process. Fluence map optimization can be considered a multi-objective optimization problem, for which a set of Pareto optimal solutions exists: the Pareto efficient frontier (PEF). In this paper, a constrained optimization method is pursued to iteratively estimate the PEF up to some predefined error. We use the property that the PEF is convex for a convex optimization problem to construct piecewise-linear upper and lower bounds to approximate the PEF from a small initial set of Pareto optimal plans. A derivative-free Sandwich algorithm is presented in which these bounds are used with three strategies to determine the location of the next Pareto optimal solution such that the uncertainty in the estimated PEF is maximally reduced. We show that an intelligent initial solution for a new Pareto optimal plan can be obtained by interpolation of fluence maps from neighbouring Pareto optimal plans. The method has been applied to a simplified clinical test case using two convex objective functions to map the trade-off between tumour dose heterogeneity and critical organ sparing. All three strategies produce representative estimates of the PEF. The new algorithm is particularly suitable for dynamic generation of Pareto optimal plans in interactive treatment planning.

Derivative-free generation and interpolation of convex Pareto optimal IMRT plans

International Nuclear Information System (INIS)

Hoffmann, Aswin L; Siem, Alex Y D; Hertog, Dick den; Kaanders, Johannes H A M; Huizenga, Henk

2006-01-01

In inverse treatment planning for intensity-modulated radiation therapy (IMRT), beamlet intensity levels in fluence maps of high-energy photon beams are optimized. Treatment plan evaluation criteria are used as objective functions to steer the optimization process. Fluence map optimization can be considered a multi-objective optimization problem, for which a set of Pareto optimal solutions exists: the Pareto efficient frontier (PEF). In this paper, a constrained optimization method is pursued to iteratively estimate the PEF up to some predefined error. We use the property that the PEF is convex for a convex optimization problem to construct piecewise-linear upper and lower bounds to approximate the PEF from a small initial set of Pareto optimal plans. A derivative-free Sandwich algorithm is presented in which these bounds are used with three strategies to determine the location of the next Pareto optimal solution such that the uncertainty in the estimated PEF is maximally reduced. We show that an intelligent initial solution for a new Pareto optimal plan can be obtained by interpolation of fluence maps from neighbouring Pareto optimal plans. The method has been applied to a simplified clinical test case using two convex objective functions to map the trade-off between tumour dose heterogeneity and critical organ sparing. All three strategies produce representative estimates of the PEF. The new algorithm is particularly suitable for dynamic generation of Pareto optimal plans in interactive treatment planning

Optimal transmission planning under the Mexican new electricity market

International Nuclear Information System (INIS)

Zenón, Eric; Rosellón, Juan

2017-01-01

This paper addresses electricity transmission planning under the new industry and institutional structure of the Mexican electricity market, which has engaged in a deep reform process after decades of a state-owned-vertically-integrated-non-competitive-closed industry. Under this new structure, characterized by a nodal pricing system and an independent system operator (ISO), we analyze welfare-optimal network expansion with two modeling strategies. In a first model, we propose the use of an incentive price-cap mechanism to promote the expansion of Mexican networks. In a second model, we study centrally-planned grid expansion in Mexico by an ISO within a power-flow model. We carry out comparisons of these models which provide us with hints to evaluate the actual transmission planning process proposed by Mexican authorities (PRODESEN). We obtain that the PRODESEN plan appears to be a convergent welfare-optimal planning process. - Highlights: • We model transmission planning (PRODESEN) in the Mexican new electricity market. • We propose a first model with a price-cap mechanism to promote network expansion. • In a second power-flow model, we study centrally-planned grid expansions. • The PRODESEN appears to be a convergent welfare-optimal planning process. • Incentive regulation could further help to implement such an optimal process.

On Motion Planning for Point-to-Point Maneuvers for a Class of Sailing Vehicles

DEFF Research Database (Denmark)

Xiao, Lin; Jouffroy, Jerome

2011-01-01

Despite their interesting dynamic and controllability properties, sailing vehicles have not been much studied in the control community. In this paper, we investigate motion planning of such vehicles. Starting from a simple dynamic model of sailing vessels in one dimension, this paper first...... considers their associated controllability issues, with the so-called no-sailing zone as a starting point, and it links them with a motion planning strategy using two-point boundary value problems as the main mathematical tool. This perspective is then expanded to do point-to-point maneuvers of sailing...

Multi-objective optimization of inverse planning for accurate radiotherapy

International Nuclear Information System (INIS)

Cao Ruifen; Pei Xi; Cheng Mengyun; Li Gui; Hu Liqin; Wu Yican; Jing Jia; Li Guoli

2011-01-01

The multi-objective optimization of inverse planning based on the Pareto solution set, according to the multi-objective character of inverse planning in accurate radiotherapy, was studied in this paper. Firstly, the clinical requirements of a treatment plan were transformed into a multi-objective optimization problem with multiple constraints. Then, the fast and elitist multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) was introduced to optimize the problem. A clinical example was tested using this method. The results show that an obtained set of non-dominated solutions were uniformly distributed and the corresponding dose distribution of each solution not only approached the expected dose distribution, but also met the dose-volume constraints. It was indicated that the clinical requirements were better satisfied using the method and the planner could select the optimal treatment plan from the non-dominated solution set. (authors)

Optimal full motion video registration with rigorous error propagation

Science.gov (United States)

Dolloff, John; Hottel, Bryant; Doucette, Peter; Theiss, Henry; Jocher, Glenn

2014-06-01

Optimal full motion video (FMV) registration is a crucial need for the Geospatial community. It is required for subsequent and optimal geopositioning with simultaneous and reliable accuracy prediction. An overall approach being developed for such registration is presented that models relevant error sources in terms of the expected magnitude and correlation of sensor errors. The corresponding estimator is selected based on the level of accuracy of the a priori information of the sensor's trajectory and attitude (pointing) information, in order to best deal with non-linearity effects. Estimator choices include near real-time Kalman Filters and batch Weighted Least Squares. Registration solves for corrections to the sensor a priori information for each frame. It also computes and makes available a posteriori accuracy information, i.e., the expected magnitude and correlation of sensor registration errors. Both the registered sensor data and its a posteriori accuracy information are then made available to "down-stream" Multi-Image Geopositioning (MIG) processes. An object of interest is then measured on the registered frames and a multi-image optimal solution, including reliable predicted solution accuracy, is then performed for the object's 3D coordinates. This paper also describes a robust approach to registration when a priori information of sensor attitude is unavailable. It makes use of structure-from-motion principles, but does not use standard Computer Vision techniques, such as estimation of the Essential Matrix which can be very sensitive to noise. The approach used instead is a novel, robust, direct search-based technique.

Optimism and Planning for Future Care Needs among Older Adults

Science.gov (United States)

Sörensen, Silvia; Hirsch, Jameson K.; Lyness, Jeffrey M.

2015-01-01

Aging is associated with an increase in need for assistance. Preparation for future care (PFC) is related to improved coping ability as well as better mental and physical health outcomes among older adults. We examined the association of optimism with components of PFC among older adults. We also explored race differences in the relationship between optimism and PFC. In Study 1, multiple regression showed that optimism was positively related to concrete planning. In Study 2, optimism was related to gathering information. An exploratory analysis combining the samples yielded a race interaction: For Whites higher optimism, but for Blacks lower optimism was associated with more planning. High optimism may be a barrier to future planning in certain social and cultural contexts. PMID:26045699

Treatment planning optimization for linear accelerator radiosurgery

International Nuclear Information System (INIS)

Meeks, Sanford L.; Buatti, John M.; Bova, Francis J.; Friedman, William A.; Mendenhall, William M.

1998-01-01

Purpose: Linear accelerator radiosurgery uses multiple arcs delivered through circular collimators to produce a nominally spherical dose distribution. Production of dose distributions that conform to irregular lesions or conformally avoid critical neural structures requires a detailed understanding of the available treatment planning parameters. Methods and Materials: Treatment planning parameters that may be manipulated within a single isocenter to provide conformal avoidance and dose conformation to ellipsoidal lesions include differential arc weighting and gantry start/stop angles. More irregular lesions require the use of multiple isocenters. Iterative manipulation of treatment planning variables can be difficult and computationally expensive, especially if the effects of these manipulations are not well defined. Effects of treatment parameter manipulation are explained and illustrated. This is followed by description of the University of Florida Stereotactic Radiosurgery Treatment Planning Algorithm. This algorithm organizes the manipulations into a practical approach for radiosurgery treatment planning. Results: Iterative treatment planning parameters may be efficiently manipulated to achieve optimal treatment plans by following the University of Florida Treatment Planning Algorithm. The ability to produce conformal stereotactic treatment plans using the algorithm is demonstrated for a variety of clinical presentations. Conclusion: The standard dose distribution produced in linear accelerator radiosurgery is spherical, but manipulation of available treatment planning parameters may result in optimal dose conformation. The University of Florida Treatment Planning Algorithm organizes available treatment parameters to efficiently produce conformal radiosurgery treatment plans

The Concept of Collision-Free Motion Planning Using a Dynamic Collision Map

Directory of Open Access Journals (Sweden)

Keum-Bae Cho

2014-09-01

Full Text Available In this paper, we address a new method for the collision-free motion planning of a mobile robot in dynamic environments. The motion planner is based on the concept of a conventional collision map (CCM, represented on the L(travel length-T(time plane. We extend the CCM with dynamic information about obstacles, such as linear acceleration and angular velocity, providing useful information for estimating variation in the collision map. We first analyse the effect of the dynamic motion of an obstacle in the collision region. We then define the measure of collision dispersion (MOCD. The dynamic collision map (DCM is generated by drawing the MOCD on the CCM. To evaluate a collision-free motion planner using the DCM, we extend the DCM with MOCD, then draw the unreachable region and deadlocked regions. Finally, we construct a collision-free motion planner using the information from the extended DCM.

A fast optimization algorithm for multicriteria intensity modulated proton therapy planning

International Nuclear Information System (INIS)

Chen Wei; Craft, David; Madden, Thomas M.; Zhang, Kewu; Kooy, Hanne M.; Herman, Gabor T.

2010-01-01

Purpose: To describe a fast projection algorithm for optimizing intensity modulated proton therapy (IMPT) plans and to describe and demonstrate the use of this algorithm in multicriteria IMPT planning. Methods: The authors develop a projection-based solver for a class of convex optimization problems and apply it to IMPT treatment planning. The speed of the solver permits its use in multicriteria optimization, where several optimizations are performed which span the space of possible treatment plans. The authors describe a plan database generation procedure which is customized to the requirements of the solver. The optimality precision of the solver can be specified by the user. Results: The authors apply the algorithm to three clinical cases: A pancreas case, an esophagus case, and a tumor along the rib cage case. Detailed analysis of the pancreas case shows that the algorithm is orders of magnitude faster than industry-standard general purpose algorithms (MOSEK's interior point optimizer, primal simplex optimizer, and dual simplex optimizer). Additionally, the projection solver has almost no memory overhead. Conclusions: The speed and guaranteed accuracy of the algorithm make it suitable for use in multicriteria treatment planning, which requires the computation of several diverse treatment plans. Additionally, given the low memory overhead of the algorithm, the method can be extended to include multiple geometric instances and proton range possibilities, for robust optimization.

A fast optimization algorithm for multicriteria intensity modulated proton therapy planning.

Science.gov (United States)

Chen, Wei; Craft, David; Madden, Thomas M; Zhang, Kewu; Kooy, Hanne M; Herman, Gabor T

2010-09-01

To describe a fast projection algorithm for optimizing intensity modulated proton therapy (IMPT) plans and to describe and demonstrate the use of this algorithm in multicriteria IMPT planning. The authors develop a projection-based solver for a class of convex optimization problems and apply it to IMPT treatment planning. The speed of the solver permits its use in multicriteria optimization, where several optimizations are performed which span the space of possible treatment plans. The authors describe a plan database generation procedure which is customized to the requirements of the solver. The optimality precision of the solver can be specified by the user. The authors apply the algorithm to three clinical cases: A pancreas case, an esophagus case, and a tumor along the rib cage case. Detailed analysis of the pancreas case shows that the algorithm is orders of magnitude faster than industry-standard general purpose algorithms (MOSEK'S interior point optimizer, primal simplex optimizer, and dual simplex optimizer). Additionally, the projection solver has almost no memory overhead. The speed and guaranteed accuracy of the algorithm make it suitable for use in multicriteria treatment planning, which requires the computation of several diverse treatment plans. Additionally, given the low memory overhead of the algorithm, the method can be extended to include multiple geometric instances and proton range possibilities, for robust optimization.

GPU-Monte Carlo based fast IMRT plan optimization

Directory of Open Access Journals (Sweden)

Yongbao Li

2014-03-01

Full Text Available Purpose: Intensity-modulated radiation treatment (IMRT plan optimization needs pre-calculated beamlet dose distribution. Pencil-beam or superposition/convolution type algorithms are typically used because of high computation speed. However, inaccurate beamlet dose distributions, particularly in cases with high levels of inhomogeneity, may mislead optimization, hindering the resulting plan quality. It is desire to use Monte Carlo (MC methods for beamlet dose calculations. Yet, the long computational time from repeated dose calculations for a number of beamlets prevents this application. It is our objective to integrate a GPU-based MC dose engine in lung IMRT optimization using a novel two-steps workflow.Methods: A GPU-based MC code gDPM is used. Each particle is tagged with an index of a beamlet where the source particle is from. Deposit dose are stored separately for beamlets based on the index. Due to limited GPU memory size, a pyramid space is allocated for each beamlet, and dose outside the space is neglected. A two-steps optimization workflow is proposed for fast MC-based optimization. At first step, a rough dose calculation is conducted with only a few number of particle per beamlet. Plan optimization is followed to get an approximated fluence map. In the second step, more accurate beamlet doses are calculated, where sampled number of particles for a beamlet is proportional to the intensity determined previously. A second-round optimization is conducted, yielding the final result.Results: For a lung case with 5317 beamlets, 105 particles per beamlet in the first round, and 108 particles per beam in the second round are enough to get a good plan quality. The total simulation time is 96.4 sec.Conclusion: A fast GPU-based MC dose calculation method along with a novel two-step optimization workflow are developed. The high efficiency allows the use of MC for IMRT optimizations.--------------------------------Cite this article as: Li Y, Tian Z

PET Motion Compensation for Radiation Therapy Using a CT-Based Mid-Position Motion Model: Methodology and Clinical Evaluation

International Nuclear Information System (INIS)

Kruis, Matthijs F.; Kamer, Jeroen B. van de; Houweling, Antonetta C.; Sonke, Jan-Jakob; Belderbos, José S.A.; Herk, Marcel van

2013-01-01

Purpose: Four-dimensional positron emission tomography (4D PET) imaging of the thorax produces sharper images with reduced motion artifacts. Current radiation therapy planning systems, however, do not facilitate 4D plan optimization. When images are acquired in a 2-minute time slot, the signal-to-noise ratio of each 4D frame is low, compromising image quality. The purpose of this study was to implement and evaluate the construction of mid-position 3D PET scans, with motion compensated using a 4D computed tomography (CT)-derived motion model. Methods and Materials: All voxels of 4D PET were registered to the time-averaged position by using a motion model derived from the 4D CT frames. After the registration the scans were summed, resulting in a motion-compensated 3D mid-position PET scan. The method was tested with a phantom dataset as well as data from 27 lung cancer patients. Results: PET motion compensation using a CT-based motion model improved image quality of both phantoms and patients in terms of increased maximum SUV (SUV max ) values and decreased apparent volumes. In homogenous phantom data, a strong relationship was found between the amplitude-to-diameter ratio and the effects of the method. In heterogeneous patient data, the effect correlated better with the motion amplitude. In case of large amplitudes, motion compensation may increase SUV max up to 25% and reduce the diameter of the 50% SUV max volume by 10%. Conclusions: 4D CT-based motion-compensated mid-position PET scans provide improved quantitative data in terms of uptake values and volumes at the time-averaged position, thereby facilitating more accurate radiation therapy treatment planning of pulmonary lesions

SU-E-T-07: 4DCT Robust Optimization for Esophageal Cancer Using Intensity Modulated Proton Therapy

Energy Technology Data Exchange (ETDEWEB)

Liao, L [Proton Therapy Center, UT MD Anderson Cancer Center, Houston, TX (United States); Department of Industrial Engineering, University of Houston, Houston, TX (United States); Yu, J; Zhu, X; Li, H; Zhang, X [Proton Therapy Center, UT MD Anderson Cancer Center, Houston, TX (United States); Li, Y [Proton Therapy Center, UT MD Anderson Cancer Center, Houston, TX (United States); Varian Medical Systems, Houston, TX (United States); Lim, G [Department of Industrial Engineering, University of Houston, Houston, TX (United States)

2015-06-15

Purpose: To develop a 4DCT robust optimization method to reduce the dosimetric impact from respiratory motion in intensity modulated proton therapy (IMPT) for esophageal cancer. Methods: Four esophageal cancer patients were selected for this study. The different phases of CT from a set of 4DCT were incorporated into the worst-case dose distribution robust optimization algorithm. 4DCT robust treatment plans were designed and compared with the conventional non-robust plans. Result doses were calculated on the average and maximum inhale/exhale phases of 4DCT. Dose volume histogram (DVH) band graphic and ΔD95%, ΔD98%, ΔD5%, ΔD2% of CTV between different phases were used to evaluate the robustness of the plans. Results: Compare to the IMPT plans optimized using conventional methods, the 4DCT robust IMPT plans can achieve the same quality in nominal cases, while yield a better robustness to breathing motion. The mean ΔD95%, ΔD98%, ΔD5% and ΔD2% of CTV are 6%, 3.2%, 0.9% and 1% for the robustly optimized plans vs. 16.2%, 11.8%, 1.6% and 3.3% from the conventional non-robust plans. Conclusion: A 4DCT robust optimization method was proposed for esophageal cancer using IMPT. We demonstrate that the 4DCT robust optimization can mitigate the dose deviation caused by the diaphragm motion.

Effects of Respiratory Motion on Passively Scattered Proton Therapy Versus Intensity Modulated Photon Therapy for Stage III Lung Cancer: Are Proton Plans More Sensitive to Breathing Motion?

International Nuclear Information System (INIS)

Matney, Jason; Park, Peter C.; Bluett, Jaques; Chen, Yi Pei; Liu, Wei; Court, Laurence E.; Liao, Zhongxing; Li, Heng; Mohan, Radhe

2013-01-01

Purpose: To quantify and compare the effects of respiratory motion on paired passively scattered proton therapy (PSPT) and intensity modulated photon therapy (IMRT) plans; and to establish the relationship between the magnitude of tumor motion and the respiratory-induced dose difference for both modalities. Methods and Materials: In a randomized clinical trial comparing PSPT and IMRT, radiation therapy plans have been designed according to common planning protocols. Four-dimensional (4D) dose was computed for PSPT and IMRT plans for a patient cohort with respiratory motion ranging from 3 to 17 mm. Image registration and dose accumulation were performed using grayscale-based deformable image registration algorithms. The dose–volume histogram (DVH) differences (4D-3D [3D = 3-dimensional]) were compared for PSPT and IMRT. Changes in 4D-3D dose were correlated to the magnitude of tumor respiratory motion. Results: The average 4D-3D dose to 95% of the internal target volume was close to zero, with 19 of 20 patients within 1% of prescribed dose for both modalities. The mean 4D-3D between the 2 modalities was not statistically significant (P<.05) for all dose–volume histogram indices (mean ± SD) except the lung V5 (PSPT: +1.1% ± 0.9%; IMRT: +0.4% ± 1.2%) and maximum cord dose (PSPT: +1.5 ± 2.9 Gy; IMRT: 0.0 ± 0.2 Gy). Changes in 4D-3D dose were correlated to tumor motion for only 2 indices: dose to 95% planning target volume, and heterogeneity index. Conclusions: With our current margin formalisms, target coverage was maintained in the presence of respiratory motion up to 17 mm for both PSPT and IMRT. Only 2 of 11 4D-3D indices (lung V5 and spinal cord maximum) were statistically distinguishable between PSPT and IMRT, contrary to the notion that proton therapy will be more susceptible to respiratory motion. Because of the lack of strong correlations with 4D-3D dose differences in PSPT and IMRT, the extent of tumor motion was not an adequate predictor of potential

Effects of Respiratory Motion on Passively Scattered Proton Therapy Versus Intensity Modulated Photon Therapy for Stage III Lung Cancer: Are Proton Plans More Sensitive to Breathing Motion?

Energy Technology Data Exchange (ETDEWEB)

Matney, Jason; Park, Peter C. [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); The University of Texas Graduate School of Biomedical Sciences, Houston, Texas (United States); Bluett, Jaques [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Chen, Yi Pei [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); The University of Texas Graduate School of Biomedical Sciences, Houston, Texas (United States); Liu, Wei; Court, Laurence E. [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Liao, Zhongxing [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Li, Heng [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Mohan, Radhe, E-mail: rmohan@mdanderson.org [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)

2013-11-01

Purpose: To quantify and compare the effects of respiratory motion on paired passively scattered proton therapy (PSPT) and intensity modulated photon therapy (IMRT) plans; and to establish the relationship between the magnitude of tumor motion and the respiratory-induced dose difference for both modalities. Methods and Materials: In a randomized clinical trial comparing PSPT and IMRT, radiation therapy plans have been designed according to common planning protocols. Four-dimensional (4D) dose was computed for PSPT and IMRT plans for a patient cohort with respiratory motion ranging from 3 to 17 mm. Image registration and dose accumulation were performed using grayscale-based deformable image registration algorithms. The dose–volume histogram (DVH) differences (4D-3D [3D = 3-dimensional]) were compared for PSPT and IMRT. Changes in 4D-3D dose were correlated to the magnitude of tumor respiratory motion. Results: The average 4D-3D dose to 95% of the internal target volume was close to zero, with 19 of 20 patients within 1% of prescribed dose for both modalities. The mean 4D-3D between the 2 modalities was not statistically significant (P<.05) for all dose–volume histogram indices (mean ± SD) except the lung V5 (PSPT: +1.1% ± 0.9%; IMRT: +0.4% ± 1.2%) and maximum cord dose (PSPT: +1.5 ± 2.9 Gy; IMRT: 0.0 ± 0.2 Gy). Changes in 4D-3D dose were correlated to tumor motion for only 2 indices: dose to 95% planning target volume, and heterogeneity index. Conclusions: With our current margin formalisms, target coverage was maintained in the presence of respiratory motion up to 17 mm for both PSPT and IMRT. Only 2 of 11 4D-3D indices (lung V5 and spinal cord maximum) were statistically distinguishable between PSPT and IMRT, contrary to the notion that proton therapy will be more susceptible to respiratory motion. Because of the lack of strong correlations with 4D-3D dose differences in PSPT and IMRT, the extent of tumor motion was not an adequate predictor of potential

Plug pattern optimization for gamma knife radiosurgery treatment planning

International Nuclear Information System (INIS)

Zhang Pengpeng; Wu, Jackie; Dean, David; Xing Lei; Xue Jinyue; Maciunas, Robert; Sibata, Claudio

2003-01-01

Purpose: To develop a novel dose optimization algorithm for improving the sparing of critical structures during gamma knife radiosurgery by shaping the plug pattern of each individual shot. Method and Materials: We first use a geometric information (medial axis) aided guided evolutionary simulated annealing (GESA) optimization algorithm to determine the number of shots and isocenter location, size, and weight of each shot. Then we create a plug quality score system that checks the dose contribution to the volume of interest by each plug in the treatment plan. A positive score implies that the corresponding source could be open to improve tumor coverage, whereas a negative score means the source could be blocked for the purpose of sparing normal and critical structures. The plug pattern is then optimized via the GESA algorithm that is integrated with this score system. Weight and position of each shot are also tuned in this procedure. Results: An acoustic tumor case is used to evaluate our algorithm. Compared to the treatment plan generated without plug patterns, adding an optimized plug pattern into the treatment planning process boosts tumor coverage index from 95.1% to 97.2%, reduces RTOG conformity index from 1.279 to 1.167, lowers Paddick's index from 1.34 to 1.20, and trims the critical structure receiving more than 30% maximum dose from 16 mm 3 to 6 mm 3 . Conclusions: Automated GESA-based plug pattern optimization of gamma knife radiosurgery frees the treatment planning team from the manual forward planning procedure and provides an optimal treatment plan

Sample-Based Motion Planning in High-Dimensional and Differentially-Constrained Systems

Science.gov (United States)

2010-02-01

path planning and motion primitives to enable crawling gaits on rough terrain e.g. [Rebula et al., 2007, Kolter et al., 2008,Pongas et al., 2007,Ratliff...demonstrating robust planning and locomotion over quite challenging terrain (e.g., [Rebula et al., 2007, Kolter et al., 2008, Pongas et al., 2007, Zucker, 2009...and Systems. [ Kolter et al., 2008] Kolter , J. Z., Rodgers, M. P., and Ng, A. Y. (2008). A control architecture for quadruped locomotion over rough

Impact of tumour motion compensation and delineation methods on FDG PET-based dose painting plan quality for NSCLC radiation therapy

International Nuclear Information System (INIS)

Thomas, Hannah M.; Kinahan, Paul E.; Samuel, James J.E.; Bowen, Stephen R.

2018-01-01

To quantitatively estimate the impact of different methods for both boost volume delineation and respiratory motion compensation of [18F] FDG PET/CT images on the fidelity of planned non-uniform ‘dose painting’ plans to the prescribed boost dose distribution. Six locally advanced non-small cell lung cancer (NSCLC) patients were retrospectively reviewed. To assess the impact of respiratory motion, time-averaged (3D AVG), respiratory phase-gated (4D GATED) and motion-encompassing (4D MIP) PET images were used. The boost volumes were defined using manual contour (MANUAL), fixed threshold (FIXED) and gradient search algorithm (GRADIENT). The dose painting prescription of 60 Gy base dose to the planning target volume and an integral dose of 14 Gy (total 74 Gy) was discretized into seven treatment planning substructures and linearly redistributed according to the relative SUV at every voxel in the boost volume. Fifty-four dose painting plan combinations were generated and conformity was evaluated using quality index VQ0.95–1.05, which represents the sum of planned dose voxels within 5% deviation from the prescribed dose. Trends in plan quality and magnitude of achievable dose escalation were recorded. Different segmentation techniques produced statistically significant variations in maximum planned dose (P < 0.02), as well as plan quality between segmentation methods for 4D GATED and 4D MIP PET images (P < 0.05). No statistically significant differences in plan quality and maximum dose were observed between motion-compensated PET-based plans (P > 0.75). Low variability in plan quality was observed for FIXED threshold plans, while MANUAL and GRADIENT plans achieved higher dose with lower plan quality indices. The dose painting plans were more sensitive to segmentation of boost volumes than PET motion compensation in this study sample. Careful consideration of boost target delineation and motion compensation strategies should guide the design of NSCLC dose painting

Impact of tumour motion compensation and delineation methods on FDG PET-based dose painting plan quality for NSCLC radiation therapy.

Science.gov (United States)

Thomas, Hannah Mary; Kinahan, Paul E; Samuel, James Jebaseelan E; Bowen, Stephen R

2018-02-01

To quantitatively estimate the impact of different methods for both boost volume delineation and respiratory motion compensation of [18F] FDG PET/CT images on the fidelity of planned non-uniform 'dose painting' plans to the prescribed boost dose distribution. Six locally advanced non-small cell lung cancer (NSCLC) patients were retrospectively reviewed. To assess the impact of respiratory motion, time-averaged (3D AVG), respiratory phase-gated (4D GATED) and motion-encompassing (4D MIP) PET images were used. The boost volumes were defined using manual contour (MANUAL), fixed threshold (FIXED) and gradient search algorithm (GRADIENT). The dose painting prescription of 60 Gy base dose to the planning target volume and an integral dose of 14 Gy (total 74 Gy) was discretized into seven treatment planning substructures and linearly redistributed according to the relative SUV at every voxel in the boost volume. Fifty-four dose painting plan combinations were generated and conformity was evaluated using quality index VQ0.95-1.05, which represents the sum of planned dose voxels within 5% deviation from the prescribed dose. Trends in plan quality and magnitude of achievable dose escalation were recorded. Different segmentation techniques produced statistically significant variations in maximum planned dose (P plan quality between segmentation methods for 4D GATED and 4D MIP PET images (P plan quality and maximum dose were observed between motion-compensated PET-based plans (P > 0.75). Low variability in plan quality was observed for FIXED threshold plans, while MANUAL and GRADIENT plans achieved higher dose with lower plan quality indices. The dose painting plans were more sensitive to segmentation of boost volumes than PET motion compensation in this study sample. Careful consideration of boost target delineation and motion compensation strategies should guide the design of NSCLC dose painting trials. © 2017 The Royal Australian and New Zealand College of

Quality assurance for high dose rate brachytherapy treatment planning optimization: using a simple optimization to verify a complex optimization

International Nuclear Information System (INIS)

Deufel, Christopher L; Furutani, Keith M

2014-01-01

As dose optimization for high dose rate brachytherapy becomes more complex, it becomes increasingly important to have a means of verifying that optimization results are reasonable. A method is presented for using a simple optimization as quality assurance for the more complex optimization algorithms typically found in commercial brachytherapy treatment planning systems. Quality assurance tests may be performed during commissioning, at regular intervals, and/or on a patient specific basis. A simple optimization method is provided that optimizes conformal target coverage using an exact, variance-based, algebraic approach. Metrics such as dose volume histogram, conformality index, and total reference air kerma agree closely between simple and complex optimizations for breast, cervix, prostate, and planar applicators. The simple optimization is shown to be a sensitive measure for identifying failures in a commercial treatment planning system that are possibly due to operator error or weaknesses in planning system optimization algorithms. Results from the simple optimization are surprisingly similar to the results from a more complex, commercial optimization for several clinical applications. This suggests that there are only modest gains to be made from making brachytherapy optimization more complex. The improvements expected from sophisticated linear optimizations, such as PARETO methods, will largely be in making systems more user friendly and efficient, rather than in finding dramatically better source strength distributions. (paper)

A Framework for Multi-Robot Motion Planning from Temporal Logic Specifications

DEFF Research Database (Denmark)

Koo, T. John; Li, Rongqing; Quottrup, Michael Melholt

2012-01-01

-time Temporal Logic, Computation Tree Logic, and -calculus can be preserved. Motion planning can then be performed at a discrete level by considering the parallel composition of discrete abstractions of the robots with a requirement specification given in a suitable temporal logic. The bisimilarity ensures...

Optimal control of stretching process of flexible solar arrays on spacecraft based on a hybrid optimization strategy

Directory of Open Access Journals (Sweden)

Qijia Yao

2017-07-01

Full Text Available The optimal control of multibody spacecraft during the stretching process of solar arrays is investigated, and a hybrid optimization strategy based on Gauss pseudospectral method (GPM and direct shooting method (DSM is presented. First, the elastic deformation of flexible solar arrays was described approximately by the assumed mode method, and a dynamic model was established by the second Lagrangian equation. Then, the nonholonomic motion planning problem is transformed into a nonlinear programming problem by using GPM. By giving fewer LG points, initial values of the state variables and control variables were obtained. A serial optimization framework was adopted to obtain the approximate optimal solution from a feasible solution. Finally, the control variables were discretized at LG points, and the precise optimal control inputs were obtained by DSM. The optimal trajectory of the system can be obtained through numerical integration. Through numerical simulation, the stretching process of solar arrays is stable with no detours, and the control inputs match the various constraints of actual conditions. The results indicate that the method is effective with good robustness. Keywords: Motion planning, Multibody spacecraft, Optimal control, Gauss pseudospectral method, Direct shooting method

UMTS network planning, optimization, and inter-operation with GSM

CERN Document Server

Rahnema, Moe

2008-01-01

UMTS Network Planning, Optimization, and Inter-Operation with GSM is an accessible, one-stop reference to help engineers effectively reduce the time and costs involved in UMTS deployment and optimization. Rahnema includes detailed coverage from both a theoretical and practical perspective on the planning and optimization aspects of UMTS, and a number of other new techniques to help operators get the most out of their networks. Provides an end-to-end perspective, from network design to optimizationIncorporates the hands-on experiences of numerous researchersSingle

Radial polar histogram: obstacle avoidance and path planning for robotic cognition and motion control

Science.gov (United States)

Wang, Po-Jen; Keyawa, Nicholas R.; Euler, Craig

2012-01-01

In order to achieve highly accurate motion control and path planning for a mobile robot, an obstacle avoidance algorithm that provided a desired instantaneous turning radius and velocity was generated. This type of obstacle avoidance algorithm, which has been implemented in California State University Northridge's Intelligent Ground Vehicle (IGV), is known as Radial Polar Histogram (RPH). The RPH algorithm utilizes raw data in the form of a polar histogram that is read from a Laser Range Finder (LRF) and a camera. A desired open block is determined from the raw data utilizing a navigational heading and an elliptical approximation. The left and right most radii are determined from the calculated edges of the open block and provide the range of possible radial paths the IGV can travel through. In addition, the calculated obstacle edge positions allow the IGV to recognize complex obstacle arrangements and to slow down accordingly. A radial path optimization function calculates the best radial path between the left and right most radii and is sent to motion control for speed determination. Overall, the RPH algorithm allows the IGV to autonomously travel at average speeds of 3mph while avoiding all obstacles, with a processing time of approximately 10ms.

Linear Temporal Logic-based Mission Planning

Directory of Open Access Journals (Sweden)

Anil Kumar

2016-06-01

Full Text Available In this paper, we describe the Linear Temporal Logic-based reactive motion planning. We address the problem of motion planning for mobile robots, wherein the goal specification of planning is given in complex environments. The desired task specification may consist of complex behaviors of the robot, including specifications for environment constraints, need of task optimality, obstacle avoidance, rescue specifications, surveillance specifications, safety specifications, etc. We use Linear Temporal Logic to give a representation for such complex task specification and constraints. The specifications are used by a verification engine to judge the feasibility and suitability of plans. The planner gives a motion strategy as output. Finally a controller is used to generate the desired trajectory to achieve such a goal. The approach is tested using simulations on the LTLMoP mission planning tool, operating over the Robot Operating System. Simulation results generated using high level planners and low level controllers work simultaneously for mission planning and controlling the physical behavior of the robot.

Real-Time Motion Planning and Safe Navigation in Dynamic Multi-Robot Environments

National Research Council Canada - National Science Library

Bruce, James R

2006-01-01

.... While motion planning has been used for high level robot navigation, or limited to semi-static or single-robot domains, it has often been dismissed for the real-time low-level control of agents due...

Optimal day-ahead operational planning of microgrids

International Nuclear Information System (INIS)

Hosseinnezhad, Vahid; Rafiee, Mansour; Ahmadian, Mohammad; Siano, Pierluigi

2016-01-01

Highlights: • A new multi-objective model for optimal day-ahead operational planning of microgrids is proposed. • A new concept called seamlessness is introduced to control the sustainability of microgrid. • A new method is developed to manage the load and renewable energy resources estimation errors. • A new solution based on a combination of numerical and evolutionary approaches is proposed. - Abstract: Providing a cost-efficient, eco-friendly and sustainable energy is one of the main issues in modern societies. In response to this demand, new features of microgrid technology have provided huge potentials while distributing electricity more effectively, economically and securely. Accordingly, this paper presents a new multi-objective generation management model for optimal day-ahead operational planning of medium voltage microgrids. The proposed model optimizes both pollutant emission and operating cost of a microgrid by using multi-objective optimization. Besides, a seamlessness-selective algorithm is integrated into the model, which can be adopted to achieve the desired self-sufficiency level for microgrids along a specified planning horizon. Furthermore, the model is characterized by a reserve-assessment strategy developed to handle the load and renewable energy resources estimation errors. The introduced model is solved using a combination of numerical and evolutionary methods of species-based quantum particle swarm optimization to find the optimal scheduling scheme and minos-based optimal power flow to optimize the operating cost and emission. In addition, the suggested solution approach also incorporates an efficient mechanism for considering energy storage systems and coding the candidate solutions in the evolutionary algorithm. The proposed model is implemented on a test microgrid and is investigated through simulations to study the different aspects of the problem. The results show significant improvements and benefits which are obtained by

Determination of an Optimal Control Strategy for a Generic Surface Vehicle

Science.gov (United States)

2014-06-18

TERMS Autonomous Vehicles Boundary Value Problem Dynamic Programming Surface Vehicles Optimal Control Path Planning 16...to follow prescribed motion trajectories. In particular, for autonomous vehicles , this motion trajectory is given by the determination of the

Optimal planning of integrated multi-energy systems

DEFF Research Database (Denmark)

van Beuzekom, I.; Gibescu, M.; Pinson, Pierre

2017-01-01

In this paper, a mathematical approach for the optimal planning of integrated energy systems is proposed. In order to address the challenges of future, RES-dominated energy systems, the model deliberates between the expansion of traditional energy infrastructures, the integration...... and sustainability goals for 2030 and 2045. Optimal green- and brownfield designs for a district's future integrated energy system are compared using a one-step, as well as a two-step planning approach. As expected, the greenfield designs are more cost efficient, as their results are not constrained by the existing...

Automatic treatment plan re-optimization for adaptive radiotherapy guided with the initial plan DVHs

International Nuclear Information System (INIS)

Li, Nan; Zarepisheh, Masoud; Uribe-Sanchez, Andres; Moore, Kevin; Tian, Zhen; Zhen, Xin; Graves, Yan Jiang; Gautier, Quentin; Mell, Loren; Jia, Xun; Jiang, Steve; Zhou, Linghong

2013-01-01

Adaptive radiation therapy (ART) can reduce normal tissue toxicity and/or improve tumor control through treatment adaptations based on the current patient anatomy. Developing an efficient and effective re-planning algorithm is an important step toward the clinical realization of ART. For the re-planning process, manual trial-and-error approach to fine-tune planning parameters is time-consuming and is usually considered unpractical, especially for online ART. It is desirable to automate this step to yield a plan of acceptable quality with minimal interventions. In ART, prior information in the original plan is available, such as dose–volume histogram (DVH), which can be employed to facilitate the automatic re-planning process. The goal of this work is to develop an automatic re-planning algorithm to generate a plan with similar, or possibly better, DVH curves compared with the clinically delivered original plan. Specifically, our algorithm iterates the following two loops. An inner loop is the traditional fluence map optimization, in which we optimize a quadratic objective function penalizing the deviation of the dose received by each voxel from its prescribed or threshold dose with a set of fixed voxel weighting factors. In outer loop, the voxel weighting factors in the objective function are adjusted according to the deviation of the current DVH curves from those in the original plan. The process is repeated until the DVH curves are acceptable or maximum iteration step is reached. The whole algorithm is implemented on GPU for high efficiency. The feasibility of our algorithm has been demonstrated with three head-and-neck cancer IMRT cases, each having an initial planning CT scan and another treatment CT scan acquired in the middle of treatment course. Compared with the DVH curves in the original plan, the DVH curves in the resulting plan using our algorithm with 30 iterations are better for almost all structures. The re-optimization process takes about 30�

Threshold-driven optimization for reference-based auto-planning

Science.gov (United States)

Long, Troy; Chen, Mingli; Jiang, Steve; Lu, Weiguo

2018-02-01

We study threshold-driven optimization methodology for automatically generating a treatment plan that is motivated by a reference DVH for IMRT treatment planning. We present a framework for threshold-driven optimization for reference-based auto-planning (TORA). Commonly used voxel-based quadratic penalties have two components for penalizing under- and over-dosing of voxels: a reference dose threshold and associated penalty weight. Conventional manual- and auto-planning using such a function involves iteratively updating the preference weights while keeping the thresholds constant, an unintuitive and often inconsistent method for planning toward some reference DVH. However, driving a dose distribution by threshold values instead of preference weights can achieve similar plans with less computational effort. The proposed methodology spatially assigns reference DVH information to threshold values, and iteratively improves the quality of that assignment. The methodology effectively handles both sub-optimal and infeasible DVHs. TORA was applied to a prostate case and a liver case as a proof-of-concept. Reference DVHs were generated using a conventional voxel-based objective, then altered to be either infeasible or easy-to-achieve. TORA was able to closely recreate reference DVHs in 5-15 iterations of solving a simple convex sub-problem. TORA has the potential to be effective for auto-planning based on reference DVHs. As dose prediction and knowledge-based planning becomes more prevalent in the clinical setting, incorporating such data into the treatment planning model in a clear, efficient way will be crucial for automated planning. A threshold-focused objective tuning should be explored over conventional methods of updating preference weights for DVH-guided treatment planning.

PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning.

Science.gov (United States)

Fiege, Jason; McCurdy, Boyd; Potrebko, Peter; Champion, Heather; Cull, Andrew

2011-09-01

In radiation therapy treatment planning, the clinical objectives of uniform high dose to the planning target volume (PTV) and low dose to the organs-at-risk (OARs) are invariably in conflict, often requiring compromises to be made between them when selecting the best treatment plan for a particular patient. In this work, the authors introduce Pareto-Aware Radiotherapy Evolutionary Treatment Optimization (pareto), a multiobjective optimization tool to solve for beam angles and fluence patterns in intensity-modulated radiation therapy (IMRT) treatment planning. pareto is built around a powerful multiobjective genetic algorithm (GA), which allows us to treat the problem of IMRT treatment plan optimization as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. We have employed a simple parameterized beam fluence representation with a realistic dose calculation approach, incorporating patient scatter effects, to demonstrate feasibility of the proposed approach on two phantoms. The first phantom is a simple cylindrical phantom containing a target surrounded by three OARs, while the second phantom is more complex and represents a paraspinal patient. pareto results in a large database of Pareto nondominated solutions that represent the necessary trade-offs between objectives. The solution quality was examined for several PTV and OAR fitness functions. The combination of a conformity-based PTV fitness function and a dose-volume histogram (DVH) or equivalent uniform dose (EUD) -based fitness function for the OAR produced relatively uniform and conformal PTV doses, with well-spaced beams. A penalty function added to the fitness functions eliminates hotspots. Comparison of resulting DVHs to those from treatment plans developed with a single-objective fluence optimizer (from a commercial treatment planning system) showed good correlation. Results also indicated that pareto shows promise in optimizing the number

Robust Control and Motion Planning for Nonlinear Underactuated Systems Using H infinity Techniques

National Research Council Canada - National Science Library

Toussaint, Gregory

2000-01-01

This thesis presents new techniques for planning and robustly controlling the motion of nonlinear underactuated vehicles when disturbances are present and only imperfect state measurements are available for feedback...

The equivalence of multi-criteria methods for radiotherapy plan optimization

International Nuclear Information System (INIS)

Breedveld, Sebastiaan; Storchi, Pascal R M; Heijmen, Ben J M

2009-01-01

Several methods can be used to achieve multi-criteria optimization of radiation therapy treatment planning, which strive for Pareto-optimality. The property of the solution being Pareto optimal is desired, because it guarantees that no criteria can be improved without deteriorating another criteria. The most widely used methods are the weighted-sum method, in which the different treatment objectives are weighted, and constrained optimization methods, in which treatment goals are set and the algorithm has to find the best plan fulfilling these goals. The constrained method used in this paper, the 2pεc (2-phase ε-constraint) method is based on the ε-constraint method, which generates Pareto-optimal solutions. Both approaches are uniquely related to each other. In this paper, we will show that it is possible to switch from the constrained method to the weighted-sum method by using the Lagrange multipliers from the constrained optimization problem, and vice versa by setting the appropriate constraints. In general, the theory presented in this paper can be useful in cases where a new situation is slightly different from the original situation, e.g. in online treatment planning, with deformations of the volumes of interest, or in automated treatment planning, where changes to the automated plan have to be made. An example of the latter is given where the planner is not satisfied with the result from the constrained method and wishes to decrease the dose in a structure. By using the Lagrange multipliers, a weighted-sum optimization problem is constructed, which generates a Pareto-optimal solution in the neighbourhood of the original plan, but fulfills the new treatment objectives.

Robust Optimization Model for Production Planning Problem under Uncertainty

Directory of Open Access Journals (Sweden)

Pembe GÜÇLÜ

2017-01-01

Full Text Available Conditions of businesses change very quickly. To take into account the uncertainty engendered by changes has become almost a rule while planning. Robust optimization techniques that are methods of handling uncertainty ensure to produce less sensitive results to changing conditions. Production planning, is to decide from which product, when and how much will be produced, with a most basic definition. Modeling and solution of the Production planning problems changes depending on structure of the production processes, parameters and variables. In this paper, it is aimed to generate and apply scenario based robust optimization model for capacitated two-stage multi-product production planning problem under parameter and demand uncertainty. With this purpose, production planning problem of a textile company that operate in İzmir has been modeled and solved, then deterministic scenarios’ and robust method’s results have been compared. Robust method has provided a production plan that has higher cost but, will result close to feasible and optimal for most of the different scenarios in the future.

Path planning algorithms for assembly sequence planning. [in robot kinematics

Science.gov (United States)

Krishnan, S. S.; Sanderson, Arthur C.

1991-01-01

Planning for manipulation in complex environments often requires reasoning about the geometric and mechanical constraints which are posed by the task. In planning assembly operations, the automatic generation of operations sequences depends on the geometric feasibility of paths which permit parts to be joined into subassemblies. Feasible locations and collision-free paths must be present for part motions, robot and grasping motions, and fixtures. This paper describes an approach to reasoning about the feasibility of straight-line paths among three-dimensional polyhedral parts using an algebra of polyhedral cones. A second method recasts the feasibility conditions as constraints in a nonlinear optimization framework. Both algorithms have been implemented and results are presented.

Analyzing the effects of human-aware motion planning on close-proximity human-robot collaboration.

Science.gov (United States)

Lasota, Przemyslaw A; Shah, Julie A

2015-02-01

The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort. The evaluation of human response to adaptive robotic assistants has been limited, particularly in the realm of motion-level adaptation. The lack of true human-in-the-loop evaluation has made it impossible to determine whether such adaptation would lead to efficient and satisfying human-robot interaction. We conducted an experiment in which participants worked with a robot to perform a collaborative task. Participants worked with an adaptive robot incorporating human-aware motion planning and with a baseline robot using shortest-path motions. Team fluency was evaluated through a set of quantitative metrics, and human satisfaction and perceived safety and comfort were evaluated through questionnaires. When working with the adaptive robot, participants completed the task 5.57% faster, with 19.9% more concurrent motion, 2.96% less human idle time, 17.3% less robot idle time, and a 15.1% greater separation distance. Questionnaire responses indicated that participants felt safer and more comfortable when working with an adaptive robot and were more satisfied with it as a teammate than with the standard robot. People respond well to motion-level robot adaptation, and significant benefits can be achieved from its use in terms of both human-robot team fluency and human worker satisfaction. Our conclusion supports the development of technologies that could be used to implement human-aware motion planning in collaborative robots and the use of this technique for close-proximity human-robot collaboration.

SU-E-T-622: Planning Technique for Passively-Scattered Involved-Node Proton Therapy of Mediastinal Lymphoma with Consideration of Cardiac Motion

Energy Technology Data Exchange (ETDEWEB)

Flampouri, S; Li, Z; Hoppe, B [University of Florida Health Proton Therapy Institute, Jacksonville, FL (United States)

2015-06-15

Purpose: To develop a treatment planning method for passively-scattered involved-node proton therapy of mediastinal lymphoma robust to breathing and cardiac motions. Methods: Beam-specific planning treatment volumes (bsPTV) are calculated for each proton field to incorporate pertinent uncertainties. Geometric margins are added laterally to each beam while margins for range uncertainty due to setup errors, breathing, and calibration curve uncertainties are added along each beam. The calculation of breathing motion and deformation effects on proton range includes all 4DCT phases. The anisotropic water equivalent margins are translated to distances on average 4DCT. Treatment plans are designed so each beam adequately covers the corresponding bsPTV. For targets close to the heart, cardiac motion effects on dosemaps are estimated by using a library of anonymous ECG-gated cardiac CTs (cCT). The cCT, originally contrast-enhanced, are partially overridden to allow meaningful proton dose calculations. Targets similar to the treatment targets are drawn on one or more cCT sets matching the anatomy of the patient. Plans based on the average cCT are calculated on individual phases, then deformed to the average and accumulated. When clinically significant dose discrepancies occur between planned and accumulated doses, the patient plan is modified to reduce the cardiac motion effects. Results: We found that bsPTVs as planning targets create dose distributions similar to the conventional proton planning distributions, while they are a valuable tool for visualization of the uncertainties. For large targets with variability in motion and depth, integral dose was reduced because of the anisotropic margins. In most cases, heart motion has a clinically insignificant effect on target coverage. Conclusion: A treatment planning method was developed and used for proton therapy of mediastinal lymphoma. The technique incorporates bsPTVs compensating for all common sources of uncertainties

Motion analysis systems as optimization training tools in combat sports and martial arts

Directory of Open Access Journals (Sweden)

Ewa Polak

2016-01-01

Full Text Available Introduction: Over the past years, a few review papers about possibilities of using motion analysis systems in sport were published, but there are no articles that discuss this problem in the field of combat sports and martial arts. Aim: This study presents the diversity of contemporary motion analysis systems both, those that are used in scientific research, as well as those that can be applied in daily work of coaches and athletes in combat sports and martial arts. An additional aim is the indication of example applications in scientific research and range of applications in optimizing the training process. It presents a brief description of each type of systems that are currently used in sport, specific examples of systems and the main advantages and disadvantages of using them. The presentation and discussion takes place in the following sections: motion analysis utility for combat sports and martial arts, systems using digital video and systems using markers, sensors or transmitters. Conclusions: Not all types of motion analysis systems used in sport are suitable for combat sports and martial arts. Scientific studies conducted so far showed the usefulness of video-based, optical and electromechanical systems. The use of research results made with complex motion analysis systems, or made with simple systems, local application and immediate visualization is important for the preparation of training and its optimization. It may lead to technical and tactical improvement in athletes as well as the prevention of injuries in combat sports and martial arts.

SU-E-T-527: Is CTV-Based Robust Optimized IMPT in Non-Small-Cell Lung Cancer Robust Against Respiratory Motion?

International Nuclear Information System (INIS)

Anetai, Y; Mizuno, H; Sumida, I; Ogawa, K; Takegawa, H; Inoue, T; Koizumi, M; Veld, A van’t; Korevaar, E

2015-01-01

Purpose: To determine which proton planning technique on average-CT is more vulnerable to respiratory motion induced density changes and interplay effect among (a) IMPT of CTV-based minimax robust optimization with 5mm set-up error considered, (b, c) IMPT/SFUD of 5mm-expanded PTV optimization. Methods: Three planning techniques were optimized in Raystation with a prescription of 60/25 (Gy/fractions) and almost the same OAR constraints/objectives for each of 10 NSCLC patients. 4D dose without/with interplay effect was recalculated on eight 4D-CT phases and accumulated after deforming the dose of each phase to a reference (exhalation phase). The change of D98% of each CTV caused by density changes and interplay was determined. In addition, evaluation of the DVH information vector (D99%, D98%, D95%, Dave, D50%, D2%, D1%) which compares the whole DVH by η score = (cosine similarity × Pearson correlation coefficient − 0.9) × 1000 quantified the degree of DVH change: score below 100 indicates changed DVH. Results: Three 3D plans of each technique satisfied our clinical goals. D98% shift mean±SD (Gy) due to density changes was largest in (c): −0.78±1.1 while (a): −0.11±0.65 and (b): − 0.59±0.93. Also the shift due to interplay effect most was (c): −.54±0.70 whereas (a): −0.25±0.93 and (b): −0.12±0.13. Moreover lowest η score caused by density change was also (c): 69, while (a) and (b) kept around 90. η score also indicated less effect of interplay than density changes. Note that generally the changed DVH were still acceptable clinically. Paired T-tests showed a significantly smaller density change effect in (a) (p<0.05) than in (b) or (c) and no significant difference in interplay effect. Conclusion: CTV-based robust optimized IMPT was more robust against respiratory motion induced density changes than PTV-based IMPT and SFUD. The interplay effect was smaller than the effect of density changes and similar among the three techniques. The JSPS Core

Geometric Reasoning for Automated Planning

Science.gov (United States)

Clement, Bradley J.; Knight, Russell L.; Broderick, Daniel

2012-01-01

An important aspect of mission planning for NASA s operation of the International Space Station is the allocation and management of space for supplies and equipment. The Stowage, Configuration Analysis, and Operations Planning teams collaborate to perform the bulk of that planning. A Geometric Reasoning Engine is developed in a way that can be shared by the teams to optimize item placement in the context of crew planning. The ISS crew spends (at the time of this writing) a third or more of their time moving supplies and equipment around. Better logistical support and optimized packing could make a significant impact on operational efficiency of the ISS. Currently, computational geometry and motion planning do not focus specifically on the optimized orientation and placement of 3D objects based on multiple distance and containment preferences and constraints. The software performs reasoning about the manipulation of 3D solid models in order to maximize an objective function based on distance. It optimizes for 3D orientation and placement. Spatial placement optimization is a general problem and can be applied to object packing or asset relocation.

Multi-objective Design Optimization of a Parallel Schönflies-motion Robot

DEFF Research Database (Denmark)

Wu, Guanglei; Bai, Shaoping; Hjørnet, Preben

2016-01-01

. The dynamic performance is concerned mainly the capability of force transmission in the parallel kinematic chain, for which transmission indices are defined. The Pareto-front is obtained to investigate the influence of the design variables to the robot performance. Dynamic characteristics for three Pareto......This paper introduces a parallel Schoenflies-motion robot with rectangular workspace, which is suitable for pick-and-place operations. A multi-objective optimization problem is formulated to optimize the robot's geometric parameters with consideration of kinematic and dynamic performances...

PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning

International Nuclear Information System (INIS)

Fiege, Jason; McCurdy, Boyd; Potrebko, Peter; Champion, Heather; Cull, Andrew

2011-01-01

Purpose: In radiation therapy treatment planning, the clinical objectives of uniform high dose to the planning target volume (PTV) and low dose to the organs-at-risk (OARs) are invariably in conflict, often requiring compromises to be made between them when selecting the best treatment plan for a particular patient. In this work, the authors introduce Pareto-Aware Radiotherapy Evolutionary Treatment Optimization (pareto), a multiobjective optimization tool to solve for beam angles and fluence patterns in intensity-modulated radiation therapy (IMRT) treatment planning. Methods: pareto is built around a powerful multiobjective genetic algorithm (GA), which allows us to treat the problem of IMRT treatment plan optimization as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. We have employed a simple parameterized beam fluence representation with a realistic dose calculation approach, incorporating patient scatter effects, to demonstrate feasibility of the proposed approach on two phantoms. The first phantom is a simple cylindrical phantom containing a target surrounded by three OARs, while the second phantom is more complex and represents a paraspinal patient. Results: pareto results in a large database of Pareto nondominated solutions that represent the necessary trade-offs between objectives. The solution quality was examined for several PTV and OAR fitness functions. The combination of a conformity-based PTV fitness function and a dose-volume histogram (DVH) or equivalent uniform dose (EUD) -based fitness function for the OAR produced relatively uniform and conformal PTV doses, with well-spaced beams. A penalty function added to the fitness functions eliminates hotspots. Comparison of resulting DVHs to those from treatment plans developed with a single-objective fluence optimizer (from a commercial treatment planning system) showed good correlation. Results also indicated that pareto shows

Spatiotemporal radiotherapy planning using a global optimization approach

Science.gov (United States)

Adibi, Ali; Salari, Ehsan

2018-02-01

This paper aims at quantifying the extent of potential therapeutic gain, measured using biologically effective dose (BED), that can be achieved by altering the radiation dose distribution over treatment sessions in fractionated radiotherapy. To that end, a spatiotemporally integrated planning approach is developed, where the spatial and temporal dose modulations are optimized simultaneously. The concept of equivalent uniform BED (EUBED) is used to quantify and compare the clinical quality of spatiotemporally heterogeneous dose distributions in target and critical structures. This gives rise to a large-scale non-convex treatment-plan optimization problem, which is solved using global optimization techniques. The proposed spatiotemporal planning approach is tested on two stylized cancer cases resembling two different tumor sites and sensitivity analysis is performed for radio-biological and EUBED parameters. Numerical results validate that spatiotemporal plans are capable of delivering a larger BED to the target volume without increasing the BED in critical structures compared to conventional time-invariant plans. In particular, this additional gain is attributed to the irradiation of different regions of the target volume at different treatment sessions. Additionally, the trade-off between the potential therapeutic gain and the number of distinct dose distributions is quantified, which suggests a diminishing marginal gain as the number of dose distributions increases.

Optimal Control of Holding Motion by Nonprehensile Two-Cooperative-Arm Robot

Directory of Open Access Journals (Sweden)

Changan Jiang

2016-01-01

Full Text Available Recently, more researchers have focused on nursing-care assistant robot and placed their hope on it to solve the shortage problem of the caregivers in hospital or nursing home. In this paper, a nonprehensile two-cooperative-arm robot is considered to realize holding motion to keep a two-rigid-link object (regarded as a care-receiver stable on the robot arms. By applying Newton-Euler equations of motion, dynamic model of the object is obtained. In this model, for describing interaction behavior between object and robot arms in the normal direction, a viscoelastic model is employed to represent the normal forces. Considering existence of friction between object and robot arms, LuGre dynamic model is applied to describe the friction. Based on the obtained model, an optimal regulator is designed to control the holding motion of two-cooperative-arm robot. In order to verify the effectiveness of the proposed method, simulation results are shown.

TH-A-9A-02: BEST IN PHYSICS (THERAPY) - 4D IMRT Planning Using Highly- Parallelizable Particle Swarm Optimization

Energy Technology Data Exchange (ETDEWEB)

Modiri, A; Gu, X; Sawant, A [UT Southwestern Medical Center, Dallas, TX (United States)

2014-06-15

Purpose: We present a particle swarm optimization (PSO)-based 4D IMRT planning technique designed for dynamic MLC tracking delivery to lung tumors. The key idea is to utilize the temporal dimension as an additional degree of freedom rather than a constraint in order to achieve improved sparing of organs at risk (OARs). Methods: The target and normal structures were manually contoured on each of the ten phases of a 4DCT scan acquired from a lung SBRT patient who exhibited 1.5cm tumor motion despite the use of abdominal compression. Corresponding ten IMRT plans were generated using the Eclipse treatment planning system. These plans served as initial guess solutions for the PSO algorithm. Fluence weights were optimized over the entire solution space i.e., 10 phases × 12 beams × 166 control points. The size of the solution space motivated our choice of PSO, which is a highly parallelizable stochastic global optimization technique that is well-suited for such large problems. A summed fluence map was created using an in-house B-spline deformable image registration. Each plan was compared with a corresponding, internal target volume (ITV)-based IMRT plan. Results: The PSO 4D IMRT plan yielded comparable PTV coverage and significantly higher dose—sparing for parallel and serial OARs compared to the ITV-based plan. The dose-sparing achieved via PSO-4DIMRT was: lung Dmean = 28%; lung V20 = 90%; spinal cord Dmax = 23%; esophagus Dmax = 31%; heart Dmax = 51%; heart Dmean = 64%. Conclusion: Truly 4D IMRT that uses the temporal dimension as an additional degree of freedom can achieve significant dose sparing of serial and parallel OARs. Given the large solution space, PSO represents an attractive, parallelizable tool to achieve globally optimal solutions for such problems. This work was supported through funding from the National Institutes of Health and Varian Medical Systems. Amit Sawant has research funding from Varian Medical Systems, VisionRT Ltd. and Elekta.

Analyzing the Effects of Human-Aware Motion Planning on Close-Proximity Human–Robot Collaboration

Science.gov (United States)

Shah, Julie A.

2015-01-01

Objective: The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort. Background: The evaluation of human response to adaptive robotic assistants has been limited, particularly in the realm of motion-level adaptation. The lack of true human-in-the-loop evaluation has made it impossible to determine whether such adaptation would lead to efficient and satisfying human–robot interaction. Method: We conducted an experiment in which participants worked with a robot to perform a collaborative task. Participants worked with an adaptive robot incorporating human-aware motion planning and with a baseline robot using shortest-path motions. Team fluency was evaluated through a set of quantitative metrics, and human satisfaction and perceived safety and comfort were evaluated through questionnaires. Results: When working with the adaptive robot, participants completed the task 5.57% faster, with 19.9% more concurrent motion, 2.96% less human idle time, 17.3% less robot idle time, and a 15.1% greater separation distance. Questionnaire responses indicated that participants felt safer and more comfortable when working with an adaptive robot and were more satisfied with it as a teammate than with the standard robot. Conclusion: People respond well to motion-level robot adaptation, and significant benefits can be achieved from its use in terms of both human–robot team fluency and human worker satisfaction. Application: Our conclusion supports the development of technologies that could be used to implement human-aware motion planning in collaborative robots and the use of this technique for close-proximity human–robot collaboration. PMID:25790568

Optimized respiratory-resolved motion-compensated 3D Cartesian coronary MR angiography.

Science.gov (United States)

Correia, Teresa; Ginami, Giulia; Cruz, Gastão; Neji, Radhouene; Rashid, Imran; Botnar, René M; Prieto, Claudia

2018-04-22

To develop a robust and efficient reconstruction framework that provides high-quality motion-compensated respiratory-resolved images from free-breathing 3D whole-heart Cartesian coronary magnetic resonance angiography (CMRA) acquisitions. Recently, XD-GRASP (eXtra-Dimensional Golden-angle RAdial Sparse Parallel MRI) was proposed to achieve 100% scan efficiency and provide respiratory-resolved 3D radial CMRA images by exploiting sparsity in the respiratory dimension. Here, a reconstruction framework for Cartesian CMRA imaging is proposed, which provides respiratory-resolved motion-compensated images by incorporating 2D beat-to-beat translational motion information to increase sparsity in the respiratory dimension. The motion information is extracted from interleaved image navigators and is also used to compensate for 2D translational motion within each respiratory phase. The proposed Optimized Respiratory-resolved Cartesian Coronary MR Angiography (XD-ORCCA) method was tested on 10 healthy subjects and 2 patients with cardiovascular disease, and compared against XD-GRASP. The proposed XD-ORCCA provides high-quality respiratory-resolved images, allowing clear visualization of the right and left coronary arteries, even for irregular breathing patterns. Compared with XD-GRASP, the proposed method improves the visibility and sharpness of both coronaries. Significant differences (p respiratory phases with larger motion amplitudes and subjects with irregular breathing patterns. A robust respiratory-resolved motion-compensated framework for Cartesian CMRA has been proposed and tested in healthy subjects and patients. The proposed XD-ORCCA provides high-quality images for all respiratory phases, independently of the regularity of the breathing pattern. © 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

Biocapacity optimization in regional planning

Science.gov (United States)

Guo, Jianjun; Yue, Dongxia; Li, Kai; Hui, Cang

2017-01-01

Ecological overshoot has been accelerating across the globe. Optimizing biocapacity has become a key to resolve the overshoot of ecological demand in regional sustainable development. However, most literature has focused on reducing ecological footprint but ignores the potential of spatial optimization of biocapacity through regional planning of land use. Here we develop a spatial probability model and present four scenarios for optimizing biocapacity of a river basin in Northwest China. The potential of enhanced biocapacity and its effects on ecological overshoot and water consumption in the region were explored. Two scenarios with no restrictions on croplands and water use reduced the overshoot by 29 to 53%, and another two scenarios which do not allow croplands and water use to increase worsened the overshoot by 11 to 15%. More spatially flexible transition rules of land use led to higher magnitude of change after optimization. However, biocapacity optimization required a large amount of additional water resources, casting considerable pressure on the already water-scarce socio-ecological system. Our results highlight the potential for policy makers to manage/optimize regional land use which addresses ecological overshoot. Investigation on the feasibility of such spatial optimization complies with the forward-looking policies for sustainable development and deserves further attention.

The Toggle Local Planner for sampling-based motion planning

KAUST Repository

Denny, Jory

2012-05-01

Sampling-based solutions to the motion planning problem, such as the probabilistic roadmap method (PRM), have become commonplace in robotics applications. These solutions are the norm as the dimensionality of the planning space grows, i.e., d > 5. An important primitive of these methods is the local planner, which is used for validation of simple paths between two configurations. The most common is the straight-line local planner which interpolates along the straight line between the two configurations. In this paper, we introduce a new local planner, Toggle Local Planner (Toggle LP), which extends local planning to a two-dimensional subspace of the overall planning space. If no path exists between the two configurations in the subspace, then Toggle LP is guaranteed to correctly return false. Intuitively, more connections could be found by Toggle LP than by the straight-line planner, resulting in better connected roadmaps. As shown in our results, this is the case, and additionally, the extra cost, in terms of time or storage, for Toggle LP is minimal. Additionally, our experimental analysis of the planner shows the benefit for a wide array of robots, with DOF as high as 70. © 2012 IEEE.

Multi-optimization Criteria-based Robot Behavioral Adaptability and Motion Planning

International Nuclear Information System (INIS)

Pin, Francois G.

2002-01-01

Robotic tasks are typically defined in Task Space (e.g., the 3-D World), whereas robots are controlled in Joint Space (motors). The transformation from Task Space to Joint Space must consider the task objectives (e.g., high precision, strength optimization, torque optimization), the task constraints (e.g., obstacles, joint limits, non-holonomic constraints, contact or tool task constraints), and the robot kinematics configuration (e.g., tools, type of joints, mobile platform, manipulator, modular additions, locked joints). Commercially available robots are optimized for a specific set of tasks, objectives and constraints and, therefore, their control codes are extremely specific to a particular set of conditions. Thus, there exist a multiplicity of codes, each handling a particular set of conditions, but none suitable for use on robots with widely varying tasks, objectives, constraints, or environments. On the other hand, most DOE missions and tasks are typically ''batches of one''. Attempting to use commercial codes for such work requires significant personnel and schedule costs for re-programming or adding code to the robots whenever a change in task objective, robot configuration, number and type of constraint, etc. occurs. The objective of our project is to develop a ''generic code'' to implement this Task-space to Joint-Space transformation that would allow robot behavior adaptation, in real time (at loop rate), to changes in task objectives, number and type of constraints, modes of controls, kinematics configuration (e.g., new tools, added module). Our specific goal is to develop a single code for the general solution of under-specified systems of algebraic equations that is suitable for solving the inverse kinematics of robots, is useable for all types of robots (mobile robots, manipulators, mobile manipulators, etc.) with no limitation on the number of joints and the number of controlled Task-Space variables, can adapt to real time changes in number and

Liveness-Based RRT Algorithm for Autonomous Underwater Vehicles Motion Planning

Directory of Open Access Journals (Sweden)

Yang Li

2017-01-01

Full Text Available Motion planning is a crucial, basic issue in robotics, which aims at driving vehicles or robots towards to a given destination with various constraints, such as obstacles and limited resource. This paper presents a new version of rapidly exploring random trees (RRT, that is, liveness-based RRT (Li-RRT, to address autonomous underwater vehicles (AUVs motion problem. Different from typical RRT, we define an index of each node in the random searching tree, called “liveness” in this paper, to describe the potential effectiveness during the expanding process. We show that Li-RRT is provably probabilistic completeness as original RRT. In addition, the expected time of returning a valid path with Li-RRT is obviously reduced. To verify the efficiency of our algorithm, numerical experiments are carried out in this paper.

Social Learning and Optimal Advertising in the Motion Picture Industry

OpenAIRE

Ohio University; Department of Economics; Hailey Hayeon Joo

2009-01-01

Social learning is thought to be a key determinant of the demand for movies. This can be a double-edged sword for motion picture distributors, because when a movie is good, social learning can enhance the effectiveness of movie advertising, but when a movie is bad, it can mitigate this effectiveness. This paper develops an equilibrium model of consumers' movie-going choices and movie distributors' advertising decisions. First, we develop a structural model for studios' optimal advertising str...

Feasibility of identification of gamma knife planning strategies by identification of pareto optimal gamma knife plans.

Science.gov (United States)

Giller, C A

2011-12-01

The use of conformity indices to optimize Gamma Knife planning is common, but does not address important tradeoffs between dose to tumor and normal tissue. Pareto analysis has been used for this purpose in other applications, but not for Gamma Knife (GK) planning. The goal of this work is to use computer models to show that Pareto analysis may be feasible for GK planning to identify dosimetric tradeoffs. We define a GK plan A to be Pareto dominant to B if the prescription isodose volume of A covers more tumor but not more normal tissue than B, or if A covers less normal tissue but not less tumor than B. A plan is Pareto optimal if it is not dominated by any other plan. Two different Pareto optimal plans represent different tradeoffs between dose to tumor and normal tissue, because neither plan dominates the other. 'GK simulator' software calculated dose distributions for GK plans, and was called repetitively by a genetic algorithm to calculate Pareto dominant plans. Three irregular tumor shapes were tested in 17 trials using various combinations of shots. The mean number of Pareto dominant plans/trial was 59 ± 17 (sd). Different planning strategies were identified by large differences in shot positions, and 70 of the 153 coordinate plots (46%) showed differences of 5mm or more. The Pareto dominant plans dominated other nearby plans. Pareto dominant plans represent different dosimetric tradeoffs and can be systematically calculated using genetic algorithms. Automatic identification of non-intuitive planning strategies may be feasible with these methods.

A patient-specific planning target volume used in 'plan of the day' adaptation for interfractional motion mitigation

International Nuclear Information System (INIS)

Chen, Wenjing; Gemmel, Alexander; Rietzel, Eike

2013-01-01

We propose a patient-specific planning target volume (PTV) to deal with interfractional variations, and test its feasibility in a retrospective treatment-planning study. Instead of using one planning image only, multiple scans are taken on different days. The target and organs at risk (OARs) are delineated on each images. The proposed PTV is generated from a union of those target contours on the planning images, excluding voxels of the OARs, and is denoted the PTV 'GP-OAR' (global prostate-organs at risk). The study is performed using 'plan of the day' adaptive workflow, which selects a daily plan from a library of plans based on a similarity comparison between the daily scan and planning images. The daily plans optimized for GP-OAR volumes are compared with those optimized for PTVs generated from a single prostate contour (PTV SP). Four CT serials of prostate cancer patient datasets are included in the test, and in total 28 fractions are simulated. The results show that the daily chosen GP-OAR plans provide excellent target coverage, with V95 values of the prostate mostly >95%. In addition, dose delivered to the OARs as calculated from applying daily chosen GP-OAR plans is slightly increased but comparable to that calculated from applying daily SP plans. In general, the PTV GP-OARs are able to cover possible target variations while keeping dose delivered to the OARs at a similar level to that of the PTV SPs. (author)

Motion correction of PET brain images through deconvolution: II. Practical implementation and algorithm optimization

Science.gov (United States)

Raghunath, N.; Faber, T. L.; Suryanarayanan, S.; Votaw, J. R.

2009-02-01

Image quality is significantly degraded even by small amounts of patient motion in very high-resolution PET scanners. When patient motion is known, deconvolution methods can be used to correct the reconstructed image and reduce motion blur. This paper describes the implementation and optimization of an iterative deconvolution method that uses an ordered subset approach to make it practical and clinically viable. We performed ten separate FDG PET scans using the Hoffman brain phantom and simultaneously measured its motion using the Polaris Vicra tracking system (Northern Digital Inc., Ontario, Canada). The feasibility and effectiveness of the technique was studied by performing scans with different motion and deconvolution parameters. Deconvolution resulted in visually better images and significant improvement as quantified by the Universal Quality Index (UQI) and contrast measures. Finally, the technique was applied to human studies to demonstrate marked improvement. Thus, the deconvolution technique presented here appears promising as a valid alternative to existing motion correction methods for PET. It has the potential for deblurring an image from any modality if the causative motion is known and its effect can be represented in a system matrix.

Motion correction of PET brain images through deconvolution: II. Practical implementation and algorithm optimization

International Nuclear Information System (INIS)

Raghunath, N; Faber, T L; Suryanarayanan, S; Votaw, J R

2009-01-01

Image quality is significantly degraded even by small amounts of patient motion in very high-resolution PET scanners. When patient motion is known, deconvolution methods can be used to correct the reconstructed image and reduce motion blur. This paper describes the implementation and optimization of an iterative deconvolution method that uses an ordered subset approach to make it practical and clinically viable. We performed ten separate FDG PET scans using the Hoffman brain phantom and simultaneously measured its motion using the Polaris Vicra tracking system (Northern Digital Inc., Ontario, Canada). The feasibility and effectiveness of the technique was studied by performing scans with different motion and deconvolution parameters. Deconvolution resulted in visually better images and significant improvement as quantified by the Universal Quality Index (UQI) and contrast measures. Finally, the technique was applied to human studies to demonstrate marked improvement. Thus, the deconvolution technique presented here appears promising as a valid alternative to existing motion correction methods for PET. It has the potential for deblurring an image from any modality if the causative motion is known and its effect can be represented in a system matrix.

Motion correction of PET brain images through deconvolution: II. Practical implementation and algorithm optimization

Energy Technology Data Exchange (ETDEWEB)

Raghunath, N; Faber, T L; Suryanarayanan, S; Votaw, J R [Department of Radiology, Emory University Hospital, 1364 Clifton Road, N.E. Atlanta, GA 30322 (United States)], E-mail: John.Votaw@Emory.edu

2009-02-07

Image quality is significantly degraded even by small amounts of patient motion in very high-resolution PET scanners. When patient motion is known, deconvolution methods can be used to correct the reconstructed image and reduce motion blur. This paper describes the implementation and optimization of an iterative deconvolution method that uses an ordered subset approach to make it practical and clinically viable. We performed ten separate FDG PET scans using the Hoffman brain phantom and simultaneously measured its motion using the Polaris Vicra tracking system (Northern Digital Inc., Ontario, Canada). The feasibility and effectiveness of the technique was studied by performing scans with different motion and deconvolution parameters. Deconvolution resulted in visually better images and significant improvement as quantified by the Universal Quality Index (UQI) and contrast measures. Finally, the technique was applied to human studies to demonstrate marked improvement. Thus, the deconvolution technique presented here appears promising as a valid alternative to existing motion correction methods for PET. It has the potential for deblurring an image from any modality if the causative motion is known and its effect can be represented in a system matrix.

Robot Motion and Control 2011

CERN Document Server

2012-01-01

Robot Motion Control 2011 presents very recent results in robot motion and control. Forty short papers have been chosen from those presented at the sixth International Workshop on Robot Motion and Control held in Poland in June 2011. The authors of these papers have been carefully selected and represent leading institutions in this field. The following recent developments are discussed: • Design of trajectory planning schemes for holonomic and nonholonomic systems with optimization of energy, torque limitations and other factors. • New control algorithms for industrial robots, nonholonomic systems and legged robots. • Different applications of robotic systems in industry and everyday life, like medicine, education, entertainment and others. • Multiagent systems consisting of mobile and flying robots with their applications The book is suitable for graduate students of automation and robotics, informatics and management, mechatronics, electronics and production engineering systems as well as scientists...

FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements

KAUST Repository

Agha-mohammadi, A.-a.; Chakravorty, S.; Amato, N. M.

2013-01-01

In this paper we present feedback-based information roadmap (FIRM), a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods. The crucial feature of FIRM is that the costs associated with the edges are independent of each other, and in this sense it is the first method that generates a graph in belief space that preserves the optimal substructure property. From a practical point of view, FIRM is a robust and reliable planning framework. It is robust since the solution is a feedback and there is no need for expensive replanning. It is reliable because accurate collision probabilities can be computed along the edges. In addition, FIRM is a scalable framework, where the complexity of planning with FIRM is a constant multiplier of the complexity of planning with PRM. In this paper, FIRM is introduced as an abstract framework. As a concrete instantiation of FIRM, we adopt stationary linear quadratic Gaussian (SLQG) controllers as belief stabilizers and introduce the so-called SLQG-FIRM. In SLQG-FIRM we focus on kinematic systems and then extend to dynamical systems by sampling in the equilibrium space. We investigate the performance of SLQG-FIRM in different scenarios. © The Author(s) 2013.

FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements

KAUST Repository

Agha-mohammadi, A.-a.

2013-11-15

In this paper we present feedback-based information roadmap (FIRM), a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods. The crucial feature of FIRM is that the costs associated with the edges are independent of each other, and in this sense it is the first method that generates a graph in belief space that preserves the optimal substructure property. From a practical point of view, FIRM is a robust and reliable planning framework. It is robust since the solution is a feedback and there is no need for expensive replanning. It is reliable because accurate collision probabilities can be computed along the edges. In addition, FIRM is a scalable framework, where the complexity of planning with FIRM is a constant multiplier of the complexity of planning with PRM. In this paper, FIRM is introduced as an abstract framework. As a concrete instantiation of FIRM, we adopt stationary linear quadratic Gaussian (SLQG) controllers as belief stabilizers and introduce the so-called SLQG-FIRM. In SLQG-FIRM we focus on kinematic systems and then extend to dynamical systems by sampling in the equilibrium space. We investigate the performance of SLQG-FIRM in different scenarios. © The Author(s) 2013.

SU-E-T-500: Initial Implementation of GPU-Based Particle Swarm Optimization for 4D IMRT Planning in Lung SBRT

International Nuclear Information System (INIS)

Modiri, A; Hagan, A; Gu, X; Sawant, A

2015-01-01

Purpose 4D-IMRT planning, combined with dynamic MLC tracking delivery, utilizes the temporal dimension as an additional degree of freedom to achieve improved OAR-sparing. The computational complexity for such optimization increases exponentially with increase in dimensionality. In order to accomplish this task in a clinically-feasible time frame, we present an initial implementation of GPU-based 4D-IMRT planning based on particle swarm optimization (PSO). Methods The target and normal structures were manually contoured on ten phases of a 4DCT scan of a NSCLC patient with a 54cm3 right-lower-lobe tumor (1.5cm motion). Corresponding ten 3D-IMRT plans were created in the Eclipse treatment planning system (Ver-13.6). A vendor-provided scripting interface was used to export 3D-dose matrices corresponding to each control point (10 phases × 9 beams × 166 control points = 14,940), which served as input to PSO. The optimization task was to iteratively adjust the weights of each control point and scale the corresponding dose matrices. In order to handle the large amount of data in GPU memory, dose matrices were sparsified and placed in contiguous memory blocks with the 14,940 weight-variables. PSO was implemented on CPU (dual-Xeon, 3.1GHz) and GPU (dual-K20 Tesla, 2496 cores, 3.52Tflops, each) platforms. NiftyReg, an open-source deformable image registration package, was used to calculate the summed dose. Results The 4D-PSO plan yielded PTV coverage comparable to the clinical ITV-based plan and significantly higher OAR-sparing, as follows: lung Dmean=33%; lung V20=27%; spinal cord Dmax=26%; esophagus Dmax=42%; heart Dmax=0%; heart Dmean=47%. The GPU-PSO processing time for 14940 variables and 7 PSO-particles was 41% that of CPU-PSO (199 vs. 488 minutes). Conclusion Truly 4D-IMRT planning can yield significant OAR dose-sparing while preserving PTV coverage. The corresponding optimization problem is large-scale, non-convex and computationally rigorous. Our initial results

SU-E-T-500: Initial Implementation of GPU-Based Particle Swarm Optimization for 4D IMRT Planning in Lung SBRT

Energy Technology Data Exchange (ETDEWEB)

Modiri, A; Hagan, A; Gu, X; Sawant, A [UT Southwestern Medical Center, Dallas, TX (United States)

2015-06-15

Purpose 4D-IMRT planning, combined with dynamic MLC tracking delivery, utilizes the temporal dimension as an additional degree of freedom to achieve improved OAR-sparing. The computational complexity for such optimization increases exponentially with increase in dimensionality. In order to accomplish this task in a clinically-feasible time frame, we present an initial implementation of GPU-based 4D-IMRT planning based on particle swarm optimization (PSO). Methods The target and normal structures were manually contoured on ten phases of a 4DCT scan of a NSCLC patient with a 54cm3 right-lower-lobe tumor (1.5cm motion). Corresponding ten 3D-IMRT plans were created in the Eclipse treatment planning system (Ver-13.6). A vendor-provided scripting interface was used to export 3D-dose matrices corresponding to each control point (10 phases × 9 beams × 166 control points = 14,940), which served as input to PSO. The optimization task was to iteratively adjust the weights of each control point and scale the corresponding dose matrices. In order to handle the large amount of data in GPU memory, dose matrices were sparsified and placed in contiguous memory blocks with the 14,940 weight-variables. PSO was implemented on CPU (dual-Xeon, 3.1GHz) and GPU (dual-K20 Tesla, 2496 cores, 3.52Tflops, each) platforms. NiftyReg, an open-source deformable image registration package, was used to calculate the summed dose. Results The 4D-PSO plan yielded PTV coverage comparable to the clinical ITV-based plan and significantly higher OAR-sparing, as follows: lung Dmean=33%; lung V20=27%; spinal cord Dmax=26%; esophagus Dmax=42%; heart Dmax=0%; heart Dmean=47%. The GPU-PSO processing time for 14940 variables and 7 PSO-particles was 41% that of CPU-PSO (199 vs. 488 minutes). Conclusion Truly 4D-IMRT planning can yield significant OAR dose-sparing while preserving PTV coverage. The corresponding optimization problem is large-scale, non-convex and computationally rigorous. Our initial results

Bi-objective optimization for multi-modal transportation routing planning problem based on Pareto optimality

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Yan Sun

2015-09-01

Full Text Available Purpose: The purpose of study is to solve the multi-modal transportation routing planning problem that aims to select an optimal route to move a consignment of goods from its origin to its destination through the multi-modal transportation network. And the optimization is from two viewpoints including cost and time. Design/methodology/approach: In this study, a bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. Minimizing the total transportation cost and the total transportation time are set as the optimization objectives of the model. In order to balance the benefit between the two objectives, Pareto optimality is utilized to solve the model by gaining its Pareto frontier. The Pareto frontier of the model can provide the multi-modal transportation operator (MTO and customers with better decision support and it is gained by the normalized normal constraint method. Then, an experimental case study is designed to verify the feasibility of the model and Pareto optimality by using the mathematical programming software Lingo. Finally, the sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case. Findings: The calculation results indicate that the proposed model and Pareto optimality have good performance in dealing with the bi-objective optimization. The sensitivity analysis also shows the influence of the variation of the demand and supply on the multi-modal transportation organization clearly. Therefore, this method can be further promoted to the practice. Originality/value: A bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. The Pareto frontier based sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case.

Phase-Division-Based Dynamic Optimization of Linkages for Drawing Servo Presses

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Zhang, Zhi-Gang; Wang, Li-Ping; Cao, Yan-Ke

2017-11-01

Existing linkage-optimization methods are designed for mechanical presses; few can be directly used for servo presses, so development of the servo press is limited. Based on the complementarity of linkage optimization and motion planning, a phase-division-based linkage-optimization model for a drawing servo press is established. Considering the motion-planning principles of a drawing servo press, and taking account of work rating and efficiency, the constraints of the optimization model are constructed. Linkage is optimized in two modes: use of either constant eccentric speed or constant slide speed in the work segments. The performances of optimized linkages are compared with those of a mature linkage SL4-2000A, which is optimized by a traditional method. The results show that the work rating of a drawing servo press equipped with linkages optimized by this new method improved and the root-mean-square torque of the servo motors is reduced by more than 10%. This research provides a promising method for designing energy-saving drawing servo presses with high work ratings.

Optimization of voltage output of energy harvesters with continuous mechanical rotation extracted from human motion (Conference Presentation)

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Rashid, Evan; Hamidi, Armita; Tadesse, Yonas

2017-04-01

With increasing popularity of portable devices for outdoor activities, portable energy harvesting devices are coming into spot light. The next generation energy harvester which is called hybrid energy harvester can employ more than one mechanism in a single device to optimize portion of the energy that can be harvested from any source of waste energy namely motion, vibration, heat and etc. In spite of few recent attempts for creating hybrid portable devices, the level of output energy still needs to be improved with the intention of employing them in commercial electronic systems or further applications. Moreover, implementing a practical hybrid energy harvester in different application for further investigation is still challenging. This proposal is projected to incorporate a novel approach to maximize and optimize the voltage output of hybrid energy harvesters to achieve a greater conversion efficiency normalized by the total mass of the hybrid device than the simple arithmetic sum of the individual harvesting mechanisms. The energy harvester model previously proposed by Larkin and Tadesse [1] is used as a baseline and a continuous unidirectional rotation is incorporated to maximize and optimize the output. The device harvest mechanical energy from oscillatory motion and convert it to electrical energy through electromagnetic and piezoelectric systems. The new designed mechanism upgrades the device in a way that can harvest energy from both rotational and linear motions by using magnets. Likewise, the piezoelectric section optimized to harvest at least 10% more energy. To the end, the device scaled down for tested with different sources of vibrations in the immediate environment, including machinery operation, bicycle, door motion while opening and closing and finally, human motions. Comparing the results from literature proved that current device has capability to be employed in commercial small electronic devices for enhancement of battery usage or as a backup

Optimal dividends in the Brownian motion risk model with interest

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Fang, Ying; Wu, Rong

2009-07-01

In this paper, we consider a Brownian motion risk model, and in addition, the surplus earns investment income at a constant force of interest. The objective is to find a dividend policy so as to maximize the expected discounted value of dividend payments. It is well known that optimality is achieved by using a barrier strategy for unrestricted dividend rate. However, ultimate ruin of the company is certain if a barrier strategy is applied. In many circumstances this is not desirable. This consideration leads us to impose a restriction on the dividend stream. We assume that dividends are paid to the shareholders according to admissible strategies whose dividend rate is bounded by a constant. Under this additional constraint, we show that the optimal dividend strategy is formed by a threshold strategy.

Funnel Libraries for Real-Time Robust Feedback Motion Planning

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2016-07-21

control inputs to the SBach are raw servo commands to the control surfaces (ailerons, rudder, elevator) and a raw throttle setting. These commands are...Convex Optimization. Cambridge University Press . [Brooks, 1982] Brooks, R. (1982). Symbolic error analysis and robot planning. The International Journal

Optimized planning methodologies of ASON implementation

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Zhou, Michael M.; Tamil, Lakshman S.

2005-02-01

Advanced network planning concerns effective network-resource allocation for dynamic and open business environment. Planning methodologies of ASON implementation based on qualitative analysis and mathematical modeling are presented in this paper. The methodology includes method of rationalizing technology and architecture, building network and nodal models, and developing dynamic programming for multi-period deployment. The multi-layered nodal architecture proposed here can accommodate various nodal configurations for a multi-plane optical network and the network modeling presented here computes the required network elements for optimizing resource allocation.

Use of plan quality degradation to evaluate tradeoffs in delivery efficiency and clinical plan metrics arising from IMRT optimizer and sequencer compromises

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Wilkie, Joel R.; Matuszak, Martha M.; Feng, Mary; Moran, Jean M.; Fraass, Benedick A.

2013-01-01

Purpose: Plan degradation resulting from compromises made to enhance delivery efficiency is an important consideration for intensity modulated radiation therapy (IMRT) treatment plans. IMRT optimization and/or multileaf collimator (MLC) sequencing schemes can be modified to generate more efficient treatment delivery, but the effect those modifications have on plan quality is often difficult to quantify. In this work, the authors present a method for quantitative assessment of overall plan quality degradation due to tradeoffs between delivery efficiency and treatment plan quality, illustrated using comparisons between plans developed allowing different numbers of intensity levels in IMRT optimization and/or MLC sequencing for static segmental MLC IMRT plans. Methods: A plan quality degradation method to evaluate delivery efficiency and plan quality tradeoffs was developed and used to assess planning for 14 prostate and 12 head and neck patients treated with static IMRT. Plan quality was evaluated using a physician's predetermined “quality degradation” factors for relevant clinical plan metrics associated with the plan optimization strategy. Delivery efficiency and plan quality were assessed for a range of optimization and sequencing limitations. The “optimal” (baseline) plan for each case was derived using a clinical cost function with an unlimited number of intensity levels. These plans were sequenced with a clinical MLC leaf sequencer which uses >100 segments, assuring delivered intensities to be within 1% of the optimized intensity pattern. Each patient's optimal plan was also sequenced limiting the number of intensity levels (20, 10, and 5), and then separately optimized with these same numbers of intensity levels. Delivery time was measured for all plans, and direct evaluation of the tradeoffs between delivery time and plan degradation was performed. Results: When considering tradeoffs, the optimal number of intensity levels depends on the treatment

Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning

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Hellinton H. Takada

2018-01-01

Full Text Available There are several electricity generation technologies based on different sources such as wind, biomass, gas, coal, and so on. The consideration of the uncertainties associated with the future costs of such technologies is crucial for planning purposes. In the literature, the allocation of resources in the available technologies has been solved as a mean-variance optimization problem assuming knowledge of the expected values and the covariance matrix of the costs. However, in practice, they are not exactly known parameters. Consequently, the obtained optimal allocations from the mean-variance optimization are not robust to possible estimation errors of such parameters. Additionally, it is usual to have electricity generation technology specialists participating in the planning processes and, obviously, the consideration of useful prior information based on their previous experience is of utmost importance. The Bayesian models consider not only the uncertainty in the parameters, but also the prior information from the specialists. In this paper, we introduce the classical-equivalent Bayesian mean-variance optimization to solve the electricity generation planning problem using both improper and proper prior distributions for the parameters. In order to illustrate our approach, we present an application comparing the classical-equivalent Bayesian with the naive mean-variance optimal portfolios.

Therapeutic treatment plan optimization with probability density-based dose prescription

International Nuclear Information System (INIS)

Lian Jun; Cotrutz, Cristian; Xing Lei

2003-01-01

The dose optimization in inverse planning is realized under the guidance of an objective function. The prescription doses in a conventional approach are usually rigid values, defining in most instances an ill-conditioned optimization problem. In this work, we propose a more general dose optimization scheme based on a statistical formalism [Xing et al., Med. Phys. 21, 2348-2358 (1999)]. Instead of a rigid dose, the prescription to a structure is specified by a preference function, which describes the user's preference over other doses in case the most desired dose is not attainable. The variation range of the prescription dose and the shape of the preference function are predesigned by the user based on prior clinical experience. Consequently, during the iterative optimization process, the prescription dose is allowed to deviate, with a certain preference level, from the most desired dose. By not restricting the prescription dose to a fixed value, the optimization problem becomes less ill-defined. The conventional inverse planning algorithm represents a special case of the new formalism. An iterative dose optimization algorithm is used to optimize the system. The performance of the proposed technique is systematically studied using a hypothetical C-shaped tumor with an abutting circular critical structure and a prostate case. It is shown that the final dose distribution can be manipulated flexibly by tuning the shape of the preference function and that using a preference function can lead to optimized dose distributions in accordance with the planner's specification. The proposed framework offers an effective mechanism to formalize the planner's priorities over different possible clinical scenarios and incorporate them into dose optimization. The enhanced control over the final plan may greatly facilitate the IMRT treatment planning process

SU-F-J-105: Towards a Novel Treatment Planning Pipeline Delivering Pareto- Optimal Plans While Enabling Inter- and Intrafraction Plan Adaptation

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Kontaxis, C; Bol, G; Lagendijk, J; Raaymakers, B [University Medical Center Utrecht, Utrecht (Netherlands); Breedveld, S; Sharfo, A; Heijmen, B [Erasmus University Medical Center Rotterdam, Rotterdam (Netherlands)

2016-06-15

Purpose: To develop a new IMRT treatment planning methodology suitable for the new generation of MR-linear accelerator machines. The pipeline is able to deliver Pareto-optimal plans and can be utilized for conventional treatments as well as for inter- and intrafraction plan adaptation based on real-time MR-data. Methods: A Pareto-optimal plan is generated using the automated multicriterial optimization approach Erasmus-iCycle. The resulting dose distribution is used as input to the second part of the pipeline, an iterative process which generates deliverable segments that target the latest anatomical state and gradually converges to the prescribed dose. This process continues until a certain percentage of the dose has been delivered. Under a conventional treatment, a Segment Weight Optimization (SWO) is then performed to ensure convergence to the prescribed dose. In the case of inter- and intrafraction adaptation, post-processing steps like SWO cannot be employed due to the changing anatomy. This is instead addressed by transferring the missing/excess dose to the input of the subsequent fraction. In this work, the resulting plans were delivered on a Delta4 phantom as a final Quality Assurance test. Results: A conventional static SWO IMRT plan was generated for two prostate cases. The sequencer faithfully reproduced the input dose for all volumes of interest. For the two cases the mean relative dose difference of the PTV between the ideal input and sequenced dose was 0.1% and −0.02% respectively. Both plans were delivered on a Delta4 phantom and passed the clinical Quality Assurance procedures by achieving 100% pass rate at a 3%/3mm gamma analysis. Conclusion: We have developed a new sequencing methodology capable of online plan adaptation. In this work, we extended the pipeline to support Pareto-optimal input and clinically validated that it can accurately achieve these ideal distributions, while its flexible design enables inter- and intrafraction plan

SU-F-J-105: Towards a Novel Treatment Planning Pipeline Delivering Pareto- Optimal Plans While Enabling Inter- and Intrafraction Plan Adaptation

International Nuclear Information System (INIS)

Kontaxis, C; Bol, G; Lagendijk, J; Raaymakers, B; Breedveld, S; Sharfo, A; Heijmen, B

2016-01-01

Purpose: To develop a new IMRT treatment planning methodology suitable for the new generation of MR-linear accelerator machines. The pipeline is able to deliver Pareto-optimal plans and can be utilized for conventional treatments as well as for inter- and intrafraction plan adaptation based on real-time MR-data. Methods: A Pareto-optimal plan is generated using the automated multicriterial optimization approach Erasmus-iCycle. The resulting dose distribution is used as input to the second part of the pipeline, an iterative process which generates deliverable segments that target the latest anatomical state and gradually converges to the prescribed dose. This process continues until a certain percentage of the dose has been delivered. Under a conventional treatment, a Segment Weight Optimization (SWO) is then performed to ensure convergence to the prescribed dose. In the case of inter- and intrafraction adaptation, post-processing steps like SWO cannot be employed due to the changing anatomy. This is instead addressed by transferring the missing/excess dose to the input of the subsequent fraction. In this work, the resulting plans were delivered on a Delta4 phantom as a final Quality Assurance test. Results: A conventional static SWO IMRT plan was generated for two prostate cases. The sequencer faithfully reproduced the input dose for all volumes of interest. For the two cases the mean relative dose difference of the PTV between the ideal input and sequenced dose was 0.1% and −0.02% respectively. Both plans were delivered on a Delta4 phantom and passed the clinical Quality Assurance procedures by achieving 100% pass rate at a 3%/3mm gamma analysis. Conclusion: We have developed a new sequencing methodology capable of online plan adaptation. In this work, we extended the pipeline to support Pareto-optimal input and clinically validated that it can accurately achieve these ideal distributions, while its flexible design enables inter- and intrafraction plan

Functional avoidance of lung in plan optimization with an aperture-based inverse planning system

International Nuclear Information System (INIS)

St-Hilaire, Jason; Lavoie, Caroline; Dagnault, Anne; Beaulieu, Frederic; Morin, Francis; Beaulieu, Luc; Tremblay, Daniel

2011-01-01

Purpose: To implement SPECT-based optimization in an anatomy-based aperture inverse planning system for the functional avoidance of lung in thoracic irradiation. Material and methods: SPECT information has been introduced as a voxel-by-voxel modulation of lung importance factors proportionally to the local perfusion count. Fifteen cases of lung cancer have been retrospectively analyzed by generating angle-optimized non-coplanar plans, comparing a purely anatomical approach and our functional approach. Planning target volume coverage and lung sparing have been compared. Statistical significance was assessed by a Wilcoxon matched pairs test. Results: For similar target coverage, perfusion-weighted volume receiving 10 Gy was reduced by a median of 2.2% (p = 0.022) and mean perfusion-weighted lung dose, by a median of 0.9 Gy (p = 0.001). A separate analysis of patients with localized or non-uniform hypoperfusion could not show which would benefit more from SPECT-based treatment planning. Redirection of dose sometimes created overdosage regions in the target volume. Plans consisted of a similar number of segments and monitor units. Conclusions: Angle optimization and SPECT-based modulation of importance factors allowed for functional avoidance of the lung while preserving target coverage. The technique could be also applied to implement PET-based modulation inside the target volume, leading to a safer dose escalation.

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining.

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Salehi, Mojtaba; Bahreininejad, Ardeshir

2011-08-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

Comparison of optimization algorithms in intensity-modulated radiation therapy planning

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Kendrick, Rachel

Intensity-modulated radiation therapy is used to better conform the radiation dose to the target, which includes avoiding healthy tissue. Planning programs employ optimization methods to search for the best fluence of each photon beam, and therefore to create the best treatment plan. The Computational Environment for Radiotherapy Research (CERR), a program written in MATLAB, was used to examine some commonly-used algorithms for one 5-beam plan. Algorithms include the genetic algorithm, quadratic programming, pattern search, constrained nonlinear optimization, simulated annealing, the optimization method used in Varian EclipseTM, and some hybrids of these. Quadratic programing, simulated annealing, and a quadratic/simulated annealing hybrid were also separately compared using different prescription doses. The results of each dose-volume histogram as well as the visual dose color wash were used to compare the plans. CERR's built-in quadratic programming provided the best overall plan, but avoidance of the organ-at-risk was rivaled by other programs. Hybrids of quadratic programming with some of these algorithms seems to suggest the possibility of better planning programs, as shown by the improved quadratic/simulated annealing plan when compared to the simulated annealing algorithm alone. Further experimentation will be done to improve cost functions and computational time.

An evolutionary algorithm technique for intelligence, surveillance, and reconnaissance plan optimization

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Langton, John T.; Caroli, Joseph A.; Rosenberg, Brad

2008-04-01

To support an Effects Based Approach to Operations (EBAO), Intelligence, Surveillance, and Reconnaissance (ISR) planners must optimize collection plans within an evolving battlespace. A need exists for a decision support tool that allows ISR planners to rapidly generate and rehearse high-performing ISR plans that balance multiple objectives and constraints to address dynamic collection requirements for assessment. To meet this need we have designed an evolutionary algorithm (EA)-based "Integrated ISR Plan Analysis and Rehearsal System" (I2PARS) to support Effects-based Assessment (EBA). I2PARS supports ISR mission planning and dynamic replanning to coordinate assets and optimize their routes, allocation and tasking. It uses an evolutionary algorithm to address the large parametric space of route-finding problems which is sometimes discontinuous in the ISR domain because of conflicting objectives such as minimizing asset utilization yet maximizing ISR coverage. EAs are uniquely suited for generating solutions in dynamic environments and also allow user feedback. They are therefore ideal for "streaming optimization" and dynamic replanning of ISR mission plans. I2PARS uses the Non-dominated Sorting Genetic Algorithm (NSGA-II) to automatically generate a diverse set of high performing collection plans given multiple objectives, constraints, and assets. Intended end users of I2PARS include ISR planners in the Combined Air Operations Centers and Joint Intelligence Centers. Here we show the feasibility of applying the NSGA-II algorithm and EAs in general to the ISR planning domain. Unique genetic representations and operators for optimization within the ISR domain are presented along with multi-objective optimization criteria for ISR planning. Promising results of the I2PARS architecture design, early software prototype, and limited domain testing of the new algorithm are discussed. We also present plans for future research and development, as well as technology

Optimizing perioperative decision making: improved information for clinical workflow planning.

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Doebbeling, Bradley N; Burton, Matthew M; Wiebke, Eric A; Miller, Spencer; Baxter, Laurence; Miller, Donald; Alvarez, Jorge; Pekny, Joseph

2012-01-01

Perioperative care is complex and involves multiple interconnected subsystems. Delayed starts, prolonged cases and overtime are common. Surgical procedures account for 40-70% of hospital revenues and 30-40% of total costs. Most planning and scheduling in healthcare is done without modern planning tools, which have potential for improving access by assisting in operations planning support. We identified key planning scenarios of interest to perioperative leaders, in order to examine the feasibility of applying combinatorial optimization software solving some of those planning issues in the operative setting. Perioperative leaders desire a broad range of tools for planning and assessing alternate solutions. Our modeled solutions generated feasible solutions that varied as expected, based on resource and policy assumptions and found better utilization of scarce resources. Combinatorial optimization modeling can effectively evaluate alternatives to support key decisions for planning clinical workflow and improving care efficiency and satisfaction.

Human Hand Motion Analysis and Synthesis of Optimal Power Grasps for a Robotic Hand

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Francesca Cordella

2014-03-01

Full Text Available Biologically inspired robotic systems can find important applications in biomedical robotics, since studying and replicating human behaviour can provide new insights into motor recovery, functional substitution and human-robot interaction. The analysis of human hand motion is essential for collecting information about human hand movements useful for generalizing reaching and grasping actions on a robotic system. This paper focuses on the definition and extraction of quantitative indicators for describing optimal hand grasping postures and replicating them on an anthropomorphic robotic hand. A motion analysis has been carried out on six healthy human subjects performing a transverse volar grasp. The extracted indicators point to invariant grasping behaviours between the involved subjects, thus providing some constraints for identifying the optimal grasping configuration. Hence, an optimization algorithm based on the Nelder-Mead simplex method has been developed for determining the optimal grasp configuration of a robotic hand, grounded on the aforementioned constraints. It is characterized by a reduced computational cost. The grasp stability has been tested by introducing a quality index that satisfies the form-closure property. The grasping strategy has been validated by means of simulation tests and experimental trials on an arm-hand robotic system. The obtained results have shown the effectiveness of the extracted indicators to reduce the non-linear optimization problem complexity and lead to the synthesis of a grasping posture able to replicate the human behaviour while ensuring grasp stability. The experimental results have also highlighted the limitations of the adopted robotic platform (mainly due to the mechanical structure to achieve the optimal grasp configuration.

Sampling-based real-time motion planning under state uncertainty for autonomous micro-aerial vehicles in GPS-denied environments.

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Li, Dachuan; Li, Qing; Cheng, Nong; Song, Jingyan

2014-11-18

This paper presents a real-time motion planning approach for autonomous vehicles with complex dynamics and state uncertainty. The approach is motivated by the motion planning problem for autonomous vehicles navigating in GPS-denied dynamic environments, which involves non-linear and/or non-holonomic vehicle dynamics, incomplete state estimates, and constraints imposed by uncertain and cluttered environments. To address the above motion planning problem, we propose an extension of the closed-loop rapid belief trees, the closed-loop random belief trees (CL-RBT), which incorporates predictions of the position estimation uncertainty, using a factored form of the covariance provided by the Kalman filter-based estimator. The proposed motion planner operates by incrementally constructing a tree of dynamically feasible trajectories using the closed-loop prediction, while selecting candidate paths with low uncertainty using efficient covariance update and propagation. The algorithm can operate in real-time, continuously providing the controller with feasible paths for execution, enabling the vehicle to account for dynamic and uncertain environments. Simulation results demonstrate that the proposed approach can generate feasible trajectories that reduce the state estimation uncertainty, while handling complex vehicle dynamics and environment constraints.

Clinical Implementation of an Online Adaptive Plan-of-the-Day Protocol for Nonrigid Motion Management in Locally Advanced Cervical Cancer IMRT

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Heijkoop, Sabrina T., E-mail: s.heijkoop@erasmusmc.nl; Langerak, Thomas R.; Quint, Sandra; Bondar, Luiza; Mens, Jan Willem M.; Heijmen, Ben J.M.; Hoogeman, Mischa S.

2014-11-01

Purpose: To evaluate the clinical implementation of an online adaptive plan-of-the-day protocol for nonrigid target motion management in locally advanced cervical cancer intensity modulated radiation therapy (IMRT). Methods and Materials: Each of the 64 patients had four markers implanted in the vaginal fornix to verify the position of the cervix during treatment. Full and empty bladder computed tomography (CT) scans were acquired prior to treatment to build a bladder volume-dependent cervix-uterus motion model for establishment of the plan library. In the first phase of clinical implementation, the library consisted of one IMRT plan based on a single model-predicted internal target volume (mpITV), covering the target for the whole pretreatment observed bladder volume range, and a 3D conformal radiation therapy (3DCRT) motion-robust backup plan based on the same mpITV. The planning target volume (PTV) combined the ITV and nodal clinical target volume (CTV), expanded with a 1-cm margin. In the second phase, for patients showing >2.5-cm bladder-induced cervix-uterus motion during planning, two IMRT plans were constructed, based on mpITVs for empty-to-half-full and half-full-to-full bladder. In both phases, a daily cone beam CT (CBCT) scan was acquired to first position the patient based on bony anatomy and nodal targets and then select the appropriate plan. Daily post-treatment CBCT was used to verify plan selection. Results: Twenty-four and 40 patients were included in the first and second phase, respectively. In the second phase, 11 patients had two IMRT plans. Overall, an IMRT plan was used in 82.4% of fractions. The main reasons for selecting the motion-robust backup plan were uterus outside the PTV (27.5%) and markers outside their margin (21.3%). In patients with two IMRT plans, the half-full-to-full bladder plan was selected on average in 45% of the first 12 fractions, which was reduced to 35% in the last treatment fractions. Conclusions: The implemented

Example-based human motion denoising.

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Lou, Hui; Chai, Jinxiang

2010-01-01

With the proliferation of motion capture data, interest in removing noise and outliers from motion capture data has increased. In this paper, we introduce an efficient human motion denoising technique for the simultaneous removal of noise and outliers from input human motion data. The key idea of our approach is to learn a series of filter bases from precaptured motion data and use them along with robust statistics techniques to filter noisy motion data. Mathematically, we formulate the motion denoising process in a nonlinear optimization framework. The objective function measures the distance between the noisy input and the filtered motion in addition to how well the filtered motion preserves spatial-temporal patterns embedded in captured human motion data. Optimizing the objective function produces an optimal filtered motion that keeps spatial-temporal patterns in captured motion data. We also extend the algorithm to fill in the missing values in input motion data. We demonstrate the effectiveness of our system by experimenting with both real and simulated motion data. We also show the superior performance of our algorithm by comparing it with three baseline algorithms and to those in state-of-art motion capture data processing software such as Vicon Blade.

Optimal Control of Hypersonic Planning Maneuvers Based on Pontryagin’s Maximum Principle

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A. Yu. Melnikov

2015-01-01

Full Text Available The work objective is the synthesis of simple analytical formula of the optimal roll angle of hypersonic gliding vehicles for conditions of quasi-horizontal motion, allowing its practical implementation in onboard control algorithms.The introduction justifies relevance, formulates basic control tasks, and describes a history of scientific research and achievements in the field concerned. The author reveals a common disadvantage of the other authors’ methods, i.e. the problem of practical implementation in onboard control algorithms.The similar tasks of hypersonic maneuvers are systemized according to the type of maneuver, control parameters and limitations.In the statement of the problem the glider launched horizontally with a suborbital speed glides passive in the static Atmosphere on a spherical surface of constant radius in the Central field of gravitation.The work specifies a system of equations of motion in the inertial spherical coordinate system, sets the limits on the roll angle and optimization criteria at the end of the flight: high speed or azimuth and the minimum distances to the specified geocentric points.The solution.1 A system of equations of motion is transformed by replacing the time argument with another independent argument – the normal equilibrium overload. The Hamiltonian and the equations of mated parameters are obtained using the Pontryagin’s maximum principle. The number of equations of motion and mated vector is reduced.2 The mated parameters were expressed by formulas using current movement parameters. The formulas are proved through differentiation and substitution in the equations of motion.3 The Formula of optimal roll-position control by condition of maximum is obtained. After substitution of mated parameters, the insertion of constants, and trigonometric transformations the Formula of the optimal roll angle is obtained as functions of the current parameters of motion.The roll angle is expressed as the ratio

A multicriteria framework with voxel-dependent parameters for radiotherapy treatment plan optimization

International Nuclear Information System (INIS)

Zarepisheh, Masoud; Uribe-Sanchez, Andres F.; Li, Nan; Jia, Xun; Jiang, Steve B.

2014-01-01

Purpose: To establish a new mathematical framework for radiotherapy treatment optimization with voxel-dependent optimization parameters. Methods: In the treatment plan optimization problem for radiotherapy, a clinically acceptable plan is usually generated by an optimization process with weighting factors or reference doses adjusted for a set of the objective functions associated to the organs. Recent discoveries indicate that adjusting parameters associated with each voxel may lead to better plan quality. However, it is still unclear regarding the mathematical reasons behind it. Furthermore, questions about the objective function selection and parameter adjustment to assure Pareto optimality as well as the relationship between the optimal solutions obtained from the organ-based and voxel-based models remain unanswered. To answer these questions, the authors establish in this work a new mathematical framework equipped with two theorems. Results: The new framework clarifies the different consequences of adjusting organ-dependent and voxel-dependent parameters for the treatment plan optimization of radiation therapy, as well as the impact of using different objective functions on plan qualities and Pareto surfaces. The main discoveries are threefold: (1) While in the organ-based model the selection of the objective function has an impact on the quality of the optimized plans, this is no longer an issue for the voxel-based model since the Pareto surface is independent of the objective function selection and the entire Pareto surface could be generated as long as the objective function satisfies certain mathematical conditions; (2) All Pareto solutions generated by the organ-based model with different objective functions are parts of a unique Pareto surface generated by the voxel-based model with any appropriate objective function; (3) A much larger Pareto surface is explored by adjusting voxel-dependent parameters than by adjusting organ-dependent parameters, possibly

TECHNIQUE OF OPTIMAL AUDIT PLANNING FOR INFORMATION SECURITY MANAGEMENT SYSTEM

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F. N. Shago

2014-03-01

Full Text Available Complication of information security management systems leads to the necessity of improving the scientific and methodological apparatus for these systems auditing. Planning is an important and determining part of information security management systems auditing. Efficiency of audit will be defined by the relation of the reached quality indicators to the spent resources. Thus, there is an important and urgent task of developing methods and techniques for optimization of the audit planning, making it possible to increase its effectiveness. The proposed technique gives the possibility to implement optimal distribution for planning time and material resources on audit stages on the basis of dynamics model for the ISMS quality. Special feature of the proposed approach is the usage of a priori data as well as a posteriori data for the initial audit planning, and also the plan adjustment after each audit event. This gives the possibility to optimize the usage of audit resources in accordance with the selected criteria. Application examples of the technique are given while planning audit information security management system of the organization. The result of computational experiment based on the proposed technique showed that the time (cost audit costs can be reduced by 10-15% and, consequently, quality assessments obtained through audit resources allocation can be improved with respect to well-known methods of audit planning.

Optimal margin and edge-enhanced intensity maps in the presence of motion and uncertainty

International Nuclear Information System (INIS)

Chan, Timothy C Y; Tsitsiklis, John N; Bortfeld, Thomas

2010-01-01

In radiation therapy, intensity maps involving margins have long been used to counteract the effects of dose blurring arising from motion. More recently, intensity maps with increased intensity near the edge of the tumour (edge enhancements) have been studied to evaluate their ability to offset similar effects that affect tumour coverage. In this paper, we present a mathematical methodology to derive margin and edge-enhanced intensity maps that aim to provide tumour coverage while delivering minimum total dose. We show that if the tumour is at most about twice as large as the standard deviation of the blurring distribution, the optimal intensity map is a pure scaling increase of the static intensity map without any margins or edge enhancements. Otherwise, if the tumour size is roughly twice (or more) the standard deviation of motion, then margins and edge enhancements are preferred, and we present formulae to calculate the exact dimensions of these intensity maps. Furthermore, we extend our analysis to include scenarios where the parameters of the motion distribution are not known with certainty, but rather can take any value in some range. In these cases, we derive a similar threshold to determine the structure of an optimal margin intensity map.

Dose-mass inverse optimization for minimally moving thoracic lesions

Science.gov (United States)

Mihaylov, I. B.; Moros, E. G.

2015-05-01

In the past decade, several different radiotherapy treatment plan evaluation and optimization schemes have been proposed as viable approaches, aiming for dose escalation or an increase of healthy tissue sparing. In particular, it has been argued that dose-mass plan evaluation and treatment plan optimization might be viable alternatives to the standard of care, which is realized through dose-volume evaluation and optimization. The purpose of this investigation is to apply dose-mass optimization to a cohort of lung cancer patients and compare the achievable healthy tissue sparing to that one achievable through dose-volume optimization. Fourteen non-small cell lung cancer (NSCLC) patient plans were studied retrospectively. The range of tumor motion was less than 0.5 cm and motion management in the treatment planning process was not considered. For each case, dose-volume (DV)-based and dose-mass (DM)-based optimization was performed. Nine-field step-and-shoot IMRT was used, with all of the optimization parameters kept the same between DV and DM optimizations. Commonly used dosimetric indices (DIs) such as dose to 1% the spinal cord volume, dose to 50% of the esophageal volume, and doses to 20 and 30% of healthy lung volumes were used for cross-comparison. Similarly, mass-based indices (MIs), such as doses to 20 and 30% of healthy lung masses, 1% of spinal cord mass, and 33% of heart mass, were also tallied. Statistical equivalence tests were performed to quantify the findings for the entire patient cohort. Both DV and DM plans for each case were normalized such that 95% of the planning target volume received the prescribed dose. DM optimization resulted in more organs at risk (OAR) sparing than DV optimization. The average sparing of cord, heart, and esophagus was 23, 4, and 6%, respectively. For the majority of the DIs, DM optimization resulted in lower lung doses. On average, the doses to 20 and 30% of healthy lung were lower by approximately 3 and 4%, whereas lung

Optimization of Gamma Knife treatment planning via guided evolutionary simulated annealing

International Nuclear Information System (INIS)

Zhang Pengpeng; Dean, David; Metzger, Andrew; Sibata, Claudio

2001-01-01

We present a method for generating optimized Gamma Knife trade mark sign (Elekta, Stockholm, Sweden) radiosurgery treatment plans. This semiautomatic method produces a highly conformal shot packing plan for the irradiation of an intracranial tumor. We simulate optimal treatment planning criteria with a probability function that is linked to every voxel in a volumetric (MR or CT) region of interest. This sigmoidal P + parameter models the requirement of conformality (i.e., tumor ablation and normal tissue sparing). After determination of initial radiosurgery treatment parameters, a guided evolutionary simulated annealing (GESA) algorithm is used to find the optimal size, position, and weight for each shot. The three-dimensional GESA algorithm searches the shot parameter space more thoroughly than is possible during manual shot packing and provides one plan that is suitable to the treatment criteria of the attending neurosurgeon and radiation oncologist. The result is a more conformal plan, which also reduces redundancy, and saves treatment administration time

Optimization of rotational radiotherapy treatment planning

International Nuclear Information System (INIS)

Tulovsky, Vladimir; Ringor, Michael; Papiez, Lech

1995-01-01

Purpose: Rotational therapy treatment planning for rotationally symmetric geometry of tumor and healthy tissue provides an important example of testing various approaches to optimizing dose distributions for therapeutic x-ray irradiations. In this article, dose distribution optimization is formulated as a variational problem. This problem is solved analytically and numerically. Methods and Materials: The classical Lagrange method is used to derive equations and inequalities that give necessary conditions for minimizing the mean-square deviation between the ideal dose distribution and the achievable dose distribution. The solution of the resulting integral equation with Cauchy kernel is used to derive analytical formulas for the minimizing irradiation intensity function. Results: The solutions are evaluated numerically and the graphs of the minimizing intensity functions and the corresponding dose distributions are presented. Conclusions: The optimal solutions obtained using the mean-square criterion lead to significant underdosage in some areas of the tumor volume. Possible solutions to this shortcoming are investigated and medically more appropriate criteria for optimization are proposed for future investigations

Optimal design of a novel remote center-of-motion mechanism for minimally invasive surgical robot

Science.gov (United States)

Sun, Jingyuan; Yan, Zhiyuan; Du, Zhijiang

2017-06-01

Surgical robot with a remote center-of-motion (RCM) plays an important role in minimally invasive surgery (MIS) field. To make the mechanism has high flexibility and meet the demand of movements during processing of operation, an optimized RCM mechanism is proposed in this paper. Then, the kinematic performance and workspace are analyzed. Finally, a new optimization objective function is built by using the condition number index and the workspace index.

Optimization in underground mine planning - developments and opportunities

OpenAIRE

Musingwini, C.

2016-01-01

The application of mining-specific and generic optimization techniques in the mining industry is deeply rooted in the discipline of operations research (OR). OR has its origins in the British Royal Air Force and Army around the early 1930s. Its development continued during and after World War II. The application of OR techniques to optimization in the mining industry started to emerge in the early 1960s. Since then, optimization techniques have been applied to solve widely different mine plan...

Optimum motion track planning for avoiding obstacles

International Nuclear Information System (INIS)

Attia, A.A.A

2008-01-01

A genetic algorithm (GA) is a stochastic search and optimization technique based on the mechanism of natural selection. A population of candidate solutions (Chromosomes) is held and interacts over a number of iterations (Generations) to produce better solutions. In canonical GA, the chromosomes are encoded as binary strings. Driving the process is the fitness of the chromosomes, which relates the quality of a candidate in quantitative terms. The fitness function encapsulates the problem- specific knowledge. The fitness is used in a stochastic selection of pairs of chromosomes which are 'reproduced' to generate new solution strings. Reproduction involves crossover, which generates new children by combining chromosomes in a process which swaps portions of each others genes. The other reproduction operator is called mutation. Mutation randomly changes genes and is used to introduce new information into the search. Both crossover and mutation make heavy use of random numbers.The aim of this thesis is to investigate the H/W implementation of genetic algorithm based motion path planning of robot. The potential benefit of using genetic algorithm hardware is that it allows both the huge parallelism which is suited to random number generation, crossover, mutation and fitness evaluation. For many real-world applications, GA can run for days, even when it is executed on a high performance workstation. According to the extensive computation of GA, it follows that hardware-based GA has been put forward. There are aspects of GA approach attract H/W implementation. The operation of selection and reproduction are basically problem independent and involve basic string manipulation tasks. These can be achieved by logical circuits.The fitness evaluation task, which is problem dependent, however proves a major difficulty in H/W implementation. Another difficulty comes from that designs can only be used for the individual problem their fitness function represents. Therefore, in this

Energy-Performance as a driver for optimal production planning

International Nuclear Information System (INIS)

Salahi, Niloofar; Jafari, Mohsen A.

2016-01-01

Highlights: • A 2-dimensional Energy-Performance measure is proposed for energy aware production. • This is a novel approach integrates energy efficiency with production requirements. • This approach simultaneously incorporates machine and process related specifications. • The problem is solved as stochastic MILP with constraints addressing risk averseness. • The optimization is illustrated for 2 cases of single and serial machining operation. • Impact of various electricity pricing schemes on proposed production plan is analyzed. - Abstract: In this paper, we present energy-aware production planning using a two-dimensional “Energy-Performance” measure. With this measure, the production plan explicitly takes into account machine-level requirements, process control strategies, product types and demand patterns. The “Energy-Performance” measure is developed based on an existing concept, namely, “Specific Energy” at machine level. It is further expanded to an “Energy-Performance” profile for a production line. A production planning problem is formulated as a stochastic MILP with risk-averse constraints to account for manufacturer’s risk averseness. The objective is to attain an optimal production plan that minimizes the total loss distribution subject to system throughput targets, probabilistic risk constraints and constraints imposed by the underlying “Energy-Performance” pattern. Electricity price and demand per unit time are assumed to be stochastic. Conditional Value at Risk (CVaR) of loss distributions is used as the manufacturer’s risk measure. Both single-machine and production lines are studied for different profiles and electricity pricing schemes. It is shown that the shape of “Energy-Performance” profile can change optimal plans.

Software for CATV Design and Frequency Plan Optimization

OpenAIRE

Hala, O.

2007-01-01

The paper deals with the structure of a software medium used for design and sub-optimization of frequency plan in CATV networks, their description and design method. The software performance is described and a simple design example of energy balance of a simplified CATV network is given. The software was created in programming environment called Delphi and local optimization was made in Matlab.

Optimal ground motion intensity measure for long-period structures

International Nuclear Information System (INIS)

Guan, Minsheng; Du, Hongbiao; Zeng, Qingli; Cui, Jie; Jiang, Haibo

2015-01-01

This paper aims to select the most appropriate ground motion intensity measure (IM) that is used in selecting earthquake records for the dynamic time history analysis of long-period structures. For this purpose, six reinforced concrete frame-core wall structures, designed according to modern seismic codes, are studied through dynamic time history analyses with a set of twelve selected earthquake records. Twelve IMs and two types of seismic damage indices, namely, the maximum seismic response-based and energy-based parameters, are chosen as the examined indices. Selection criteria such as correlation, efficiency, and proficiency are considered in the selection process. The optimal IM is identified by means of a comprehensive evaluation using a large number of data of correlation, efficiency, and proficiency coefficients. Numerical results illustrate that peak ground velocity is the optimal one for long-period structures and peak ground displacement is also a close contender. As compared to previous reports, the spectral-correlated parameters can only be taken as moderate IMs. Moreover, the widely used peak ground acceleration in the current seismic codes is considered inappropriate for long-period structures. (paper)

A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

International Nuclear Information System (INIS)

Zarepisheh, Masoud; Li, Nan; Long, Troy; Romeijn, H. Edwin; Tian, Zhen; Jia, Xun; Jiang, Steve B.

2014-01-01

Purpose: To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning. Methods: The algorithm automatically creates a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician-approved dose-volume trade-offs among different targets/organs and among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans. Results: The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel-weighting factors in order to generate a Pareto plan with similar DVH trade-offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency. Conclusions: A novel prior-knowledge-based optimization algorithm has been developed that automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART replanning and automatic treatment

Investigation of point triangulation methods for optimality and performance in Structure from Motion systems

DEFF Research Database (Denmark)

Structure from Motion (SFM) systems are composed of cameras and structure in the form of 3D points and other features. It is most often that the structure components outnumber the cameras by a great margin. It is not uncommon to have a configuration with 3 cameras observing more than 500 3D points...... an overview of existing triangulation methods with emphasis on performance versus optimality, and will suggest a fast triangulation algorithm based on linear constraints. The structure and camera motion estimation in a SFM system is based on the minimization of some norm of the reprojection error between...

Validation of a computational method for assessing the impact of intra-fraction motion on helical tomotherapy plans

Energy Technology Data Exchange (ETDEWEB)

Ngwa, Wilfred; Meeks, Sanford L; Kupelian, Patrick A; Langen, Katja M [Department of Radiation Oncology, M D Anderson Cancer Center Orlando, 1400 South Orange Avenue, Orlando, FL 32806 (United States); Schnarr, Eric [TomoTherapy, Inc., 1240 Deming Way, Madison, WI 53717 (United States)], E-mail: wilfred.ngwa@orlandohealth.com

2009-11-07

In this work, a method for direct incorporation of patient motion into tomotherapy dose calculations is developed and validated. This computational method accounts for all treatment dynamics and can incorporate random as well as cyclical motion data. Hence, interplay effects between treatment dynamics and patient motion are taken into account during dose calculation. This allows for a realistic assessment of intra-fraction motion on the dose distribution. The specific approach entails modifying the position and velocity events in the tomotherapy delivery plan to accommodate any known motion. The computational method is verified through phantom and film measurements. Here, measured prostate motion and simulated respiratory motion tracks were incorporated in the dose calculation. The calculated motion-encoded dose profiles showed excellent agreement with the measurements. Gamma analysis using 3 mm and 3% tolerance criteria showed over 97% and 96% average of points passing for the prostate and breathing motion tracks, respectively. The profile and gamma analysis results validate the accuracy of this method for incorporating intra-fraction motion into the dose calculation engine for assessment of dosimetric effects on helical tomotherapy dose deliveries.

High-resolution temperature-based optimization for hyperthermia treatment planning

International Nuclear Information System (INIS)

Kok, H P; Haaren, P M A van; Kamer, J B Van de; Wiersma, J; Dijk, J D P Van; Crezee, J

2005-01-01

In regional hyperthermia, optimization techniques are valuable in order to obtain amplitude/phase settings for the applicators to achieve maximal tumour heating without toxicity to normal tissue. We implemented a temperature-based optimization technique and maximized tumour temperature with constraints on normal tissue temperature to prevent hot spots. E-field distributions are the primary input for the optimization method. Due to computer limitations we are restricted to a resolution of 1 x 1 x 1 cm 3 for E-field calculations, too low for reliable treatment planning. A major problem is the fact that hot spots at low-resolution (LR) do not always correspond to hot spots at high-resolution (HR), and vice versa. Thus, HR temperature-based optimization is necessary for adequate treatment planning and satisfactory results cannot be obtained with LR strategies. To obtain HR power density (PD) distributions from LR E-field calculations, a quasi-static zooming technique has been developed earlier at the UMC Utrecht. However, quasi-static zooming does not preserve phase information and therefore it does not provide the HR E-field information required for direct HR optimization. We combined quasi-static zooming with the optimization method to obtain a millimetre resolution temperature-based optimization strategy. First we performed a LR (1 cm) optimization and used the obtained settings to calculate the HR (2 mm) PD and corresponding HR temperature distribution. Next, we performed a HR optimization using an estimation of the new HR temperature distribution based on previous calculations. This estimation is based on the assumption that the HR and LR temperature distributions, though strongly different, respond in a similar way to amplitude/phase steering. To verify the newly obtained settings, we calculate the corresponding HR temperature distribution. This method was applied to several clinical situations and found to work very well. Deviations of this estimation method for

SU-G-BRA-08: Diaphragm Motion Tracking Based On KV CBCT Projections with a Constrained Linear Regression Optimization

Energy Technology Data Exchange (ETDEWEB)

Wei, J [City College of New York, New York, NY (United States); Chao, M [The Mount Sinai Medical Center, New York, NY (United States)

2016-06-15

Purpose: To develop a novel strategy to extract the respiratory motion of the thoracic diaphragm from kilovoltage cone beam computed tomography (CBCT) projections by a constrained linear regression optimization technique. Methods: A parabolic function was identified as the geometric model and was employed to fit the shape of the diaphragm on the CBCT projections. The search was initialized by five manually placed seeds on a pre-selected projection image. Temporal redundancies, the enabling phenomenology in video compression and encoding techniques, inherent in the dynamic properties of the diaphragm motion together with the geometrical shape of the diaphragm boundary and the associated algebraic constraint that significantly reduced the searching space of viable parabolic parameters was integrated, which can be effectively optimized by a constrained linear regression approach on the subsequent projections. The innovative algebraic constraints stipulating the kinetic range of the motion and the spatial constraint preventing any unphysical deviations was able to obtain the optimal contour of the diaphragm with minimal initialization. The algorithm was assessed by a fluoroscopic movie acquired at anteriorposterior fixed direction and kilovoltage CBCT projection image sets from four lung and two liver patients. The automatic tracing by the proposed algorithm and manual tracking by a human operator were compared in both space and frequency domains. Results: The error between the estimated and manual detections for the fluoroscopic movie was 0.54mm with standard deviation (SD) of 0.45mm, while the average error for the CBCT projections was 0.79mm with SD of 0.64mm for all enrolled patients. The submillimeter accuracy outcome exhibits the promise of the proposed constrained linear regression approach to track the diaphragm motion on rotational projection images. Conclusion: The new algorithm will provide a potential solution to rendering diaphragm motion and ultimately

SU-G-BRA-08: Diaphragm Motion Tracking Based On KV CBCT Projections with a Constrained Linear Regression Optimization

International Nuclear Information System (INIS)

Wei, J; Chao, M

2016-01-01

Purpose: To develop a novel strategy to extract the respiratory motion of the thoracic diaphragm from kilovoltage cone beam computed tomography (CBCT) projections by a constrained linear regression optimization technique. Methods: A parabolic function was identified as the geometric model and was employed to fit the shape of the diaphragm on the CBCT projections. The search was initialized by five manually placed seeds on a pre-selected projection image. Temporal redundancies, the enabling phenomenology in video compression and encoding techniques, inherent in the dynamic properties of the diaphragm motion together with the geometrical shape of the diaphragm boundary and the associated algebraic constraint that significantly reduced the searching space of viable parabolic parameters was integrated, which can be effectively optimized by a constrained linear regression approach on the subsequent projections. The innovative algebraic constraints stipulating the kinetic range of the motion and the spatial constraint preventing any unphysical deviations was able to obtain the optimal contour of the diaphragm with minimal initialization. The algorithm was assessed by a fluoroscopic movie acquired at anteriorposterior fixed direction and kilovoltage CBCT projection image sets from four lung and two liver patients. The automatic tracing by the proposed algorithm and manual tracking by a human operator were compared in both space and frequency domains. Results: The error between the estimated and manual detections for the fluoroscopic movie was 0.54mm with standard deviation (SD) of 0.45mm, while the average error for the CBCT projections was 0.79mm with SD of 0.64mm for all enrolled patients. The submillimeter accuracy outcome exhibits the promise of the proposed constrained linear regression approach to track the diaphragm motion on rotational projection images. Conclusion: The new algorithm will provide a potential solution to rendering diaphragm motion and ultimately

Clinical treatment planning optimization by Powell's method for gamma unit treatment system

International Nuclear Information System (INIS)

Yan Yulong; Shu Huazhong; Bao Xudong; Luo Limin; Bai Yi

1997-01-01

Purpose: This article presents a new optimization method for stereotactic radiosurgery treatment planning for gamma unit treatment system. Methods and Materials: The gamma unit has been utilized in stereotactic radiosurgery for about 30 years, but the usual procedure for a physician-physicist team to design a treatment plan is a trial-and-error approach. Isodose curves are viewed on two-dimensional computed tomography (CT) or magnetic resonance (MR) image planes, which is not only time consuming but also seldom achieves the optimal treatment plan, especially when the isocenter weights are regarded. We developed a treatment-planning system on a computer workstation in which Powell's optimization method is realized. The optimization process starts with the initial parameters (the number of iso centers as well as corresponding 3D iso centers' coordinates, collimator sizes, and weight factors) roughly determined by the physician-physicist team. The objective function can be changed to consider protection of sensitive tissues. Results: We use the plan parameters given by a well-trained physician-physicist team, or ones that the author give roughly as the initial parameters for the optimization procedure. Dosimetric results of optimization show a better high dose-volume conformation to the target volume compared to the doctor's plan. Conclusion: This method converges quickly and is not sensitive to the initial parameters. It achieves an excellent conformation of the estimated isodose curves with the contours of the target volume. If the initial parameters are varied, there will be a little difference in parameters' configuration, but the dosimetric results proved almost to be the same

An Optimization Model and Modified Harmony Search Algorithm for Microgrid Planning with ESS

Directory of Open Access Journals (Sweden)

Yang Jiao

2017-01-01

Full Text Available To solve problems such as the high cost of microgrids (MGs, balance between supply and demand, stability of system operation, and optimizing the MG planning model, the energy storage system (ESS and harmony search algorithm (HSA are proposed. First, the conventional MG planning optimization model is constructed and the constraint conditions are defined: the supply and demand balance and reserve requirements. Second, an ESS is integrated into the optimal model of MG planning. The model with an ESS can solve and identify parameters such as the optimal power, optimal capacity, and optimal installation year. Third, the convergence speed and robustness of the ESS are optimized and improved. A case study comprising three different cases concludes the paper. The results show that the modified HSA (MHSA can effectively improve the stability and economy of MG operation with an ESS.

4D Lung Reconstruction with Phase Optimization

DEFF Research Database (Denmark)

Lyksborg, Mark; Paulsen, Rasmus; Brink, Carsten

2009-01-01

This paper investigates and demonstrates a 4D lung CT reconstruction/registration method which results in a complete volumetric model of the lung that deforms according to a respiratory motion field. The motion field is estimated iteratively between all available slice samples and a reference...... volume which is updated on the fly. The method is two part and the second part of the method aims to correct wrong phase information by employing another iterative optimizer. This two part iterative optimization allows for complete reconstruction at any phase and it will be demonstrated that it is better...... than using an optimization which does not correct for phase errors. Knowing how the lung and any tumors located within the lung deforms is relevant in planning the treatment of lung cancer....

Software for CATV Design and Frequency Plan Optimization

Directory of Open Access Journals (Sweden)

O. Hala

2007-09-01

Full Text Available The paper deals with the structure of a software medium used for design and sub-optimization of frequency plan in CATV networks, their description and design method. The software performance is described and a simple design example of energy balance of a simplified CATV network is given. The software was created in programming environment called Delphi and local optimization was made in Matlab.

Convex optimisation approach to constrained fuel optimal control of spacecraft in close relative motion

Science.gov (United States)

Massioni, Paolo; Massari, Mauro

2018-05-01

This paper describes an interesting and powerful approach to the constrained fuel-optimal control of spacecraft in close relative motion. The proposed approach is well suited for problems under linear dynamic equations, therefore perfectly fitting to the case of spacecraft flying in close relative motion. If the solution of the optimisation is approximated as a polynomial with respect to the time variable, then the problem can be approached with a technique developed in the control engineering community, known as "Sum Of Squares" (SOS), and the constraints can be reduced to bounds on the polynomials. Such a technique allows rewriting polynomial bounding problems in the form of convex optimisation problems, at the cost of a certain amount of conservatism. The principles of the techniques are explained and some application related to spacecraft flying in close relative motion are shown.

Combining spanwise morphing, inline motion and model based optimization for force magnitude and direction control

Science.gov (United States)

Scheller, Johannes; Braza, Marianna; Triantafyllou, Michael

2016-11-01

Bats and other animals rapidly change their wingspan in order to control the aerodynamic forces. A NACA0013 type airfoil with dynamically changing span is proposed as a simple model to experimentally study these biomimetic morphing wings. Combining this large-scale morphing with inline motion allows to control both force magnitude and direction. Force measurements are conducted in order to analyze the impact of the 4 degree of freedom flapping motion on the flow. A blade-element theory augmented unsteady aerodynamic model is then used to derive optimal flapping trajectories.

DETERMINING OPTIMAL CUBE FOR 3D-DCT BASED VIDEO COMPRESSION FOR DIFFERENT MOTION LEVELS

Directory of Open Access Journals (Sweden)

J. Augustin Jacob

2012-11-01

Full Text Available This paper proposes new three dimensional discrete cosine transform (3D-DCT based video compression algorithm that will select the optimal cube size based on the motion content of the video sequence. It is determined by finding normalized pixel difference (NPD values, and by categorizing the cubes as “low” or “high” motion cube suitable cube size of dimension either [16×16×8] or[8×8×8] is chosen instead of fixed cube algorithm. To evaluate the performance of the proposed algorithm test sequence with different motion levels are chosen. By doing rate vs. distortion analysis the level of compression that can be achieved and the quality of reconstructed video sequence are determined and compared against fixed cube size algorithm. Peak signal to noise ratio (PSNR is taken to measure the video quality. Experimental result shows that varying the cube size with reference to the motion content of video frames gives better performance in terms of compression ratio and video quality.

A Motion Planning Approach to Studying Molecular Motions

KAUST Repository

Amato, Nancy M.; Tapia, Lydia; Thomas, Shawna

2010-01-01

While structurally very different, protein and RNA molecules share an important attribute. The motions they undergo are strongly related to the function they perform. For example, many diseases such as Mad Cow disease or Alzheimer's disease

Optimization in radiotherapy treatment planning thanks to a fast dose calculation method

International Nuclear Information System (INIS)

Yang, Mingchao

2014-01-01

This thesis deals with the radiotherapy treatments planning issue which need a fast and reliable treatment planning system (TPS). The TPS is composed of a dose calculation algorithm and an optimization method. The objective is to design a plan to deliver the dose to the tumor while preserving the surrounding healthy and sensitive tissues. The treatment planning aims to determine the best suited radiation parameters for each patient's treatment. In this thesis, the parameters of treatment with IMRT (Intensity modulated radiation therapy) are the beam angle and the beam intensity. The objective function is multi-criteria with linear constraints. The main objective of this thesis is to demonstrate the feasibility of a treatment planning optimization method based on a fast dose-calculation technique developed by (Blanpain, 2009). This technique proposes to compute the dose by segmenting the patient's phantom into homogeneous meshes. The dose computation is divided into two steps. The first step impacts the meshes: projections and weights are set according to physical and geometrical criteria. The second step impacts the voxels: the dose is computed by evaluating the functions previously associated to their mesh. A reformulation of this technique makes possible to solve the optimization problem by the gradient descent algorithm. The main advantage of this method is that the beam angle parameters could be optimized continuously in 3 dimensions. The obtained results in this thesis offer many opportunities in the field of radiotherapy treatment planning optimization. (author) [fr

Planning Framework for Mesolevel Optimization of Urban Runoff Control Schemes

Energy Technology Data Exchange (ETDEWEB)

Zhou, Qianqian; Blohm, Andrew; Liu, Bo

2017-04-01

A planning framework is developed to optimize runoff control schemes at scales relevant for regional planning at an early stage. The framework employs less sophisticated modeling approaches to allow a practical application in developing regions with limited data sources and computing capability. The methodology contains three interrelated modules: (1)the geographic information system (GIS)-based hydrological module, which aims at assessing local hydrological constraints and potential for runoff control according to regional land-use descriptions; (2)the grading module, which is built upon the method of fuzzy comprehensive evaluation. It is used to establish a priority ranking system to assist the allocation of runoff control targets at the subdivision level; and (3)the genetic algorithm-based optimization module, which is included to derive Pareto-based optimal solutions for mesolevel allocation with multiple competing objectives. The optimization approach describes the trade-off between different allocation plans and simultaneously ensures that all allocation schemes satisfy the minimum requirement on runoff control. Our results highlight the importance of considering the mesolevel allocation strategy in addition to measures at macrolevels and microlevels in urban runoff management. (C) 2016 American Society of Civil Engineers.

A new column-generation-based algorithm for VMAT treatment plan optimization

International Nuclear Information System (INIS)

Peng Fei; Epelman, Marina A; Romeijn, H Edwin; Jia Xun; Gu Xuejun; Jiang, Steve B

2012-01-01

We study the treatment plan optimization problem for volumetric modulated arc therapy (VMAT). We propose a new column-generation-based algorithm that takes into account bounds on the gantry speed and dose rate, as well as an upper bound on the rate of change of the gantry speed, in addition to MLC constraints. The algorithm iteratively adds one aperture at each control point along the treatment arc. In each iteration, a restricted problem optimizing intensities at previously selected apertures is solved, and its solution is used to formulate a pricing problem, which selects an aperture at another control point that is compatible with previously selected apertures and leads to the largest rate of improvement in the objective function value of the restricted problem. Once a complete set of apertures is obtained, their intensities are optimized and the gantry speeds and dose rates are adjusted to minimize treatment time while satisfying all machine restrictions. Comparisons of treatment plans obtained by our algorithm to idealized IMRT plans of 177 beams on five clinical prostate cancer cases demonstrate high quality with respect to clinical dose–volume criteria. For all cases, our algorithm yields treatment plans that can be delivered in around 2 min. Implementation on a graphic processing unit enables us to finish the optimization of a VMAT plan in 25–55 s. (paper)

Control of nonholonomic systems from sub-Riemannian geometry to motion planning

CERN Document Server

Jean, Frédéric

2014-01-01

Nonholonomic systems are control systems which depend linearly on the control. Their underlying geometry is the sub-Riemannian geometry, which plays for these systems the same role as Euclidean geometry does for linear systems. In particular the usual notions of approximations at the first order, that are essential for control purposes, have to be defined in terms of this geometry. The aim of these notes is to present these notions of approximation and their application to the motion planning problem for nonholonomic systems.

Optimal design and planning of glycerol-based biorefinery supply chains under uncertainty

DEFF Research Database (Denmark)

Loureiro da Costa Lira Gargalo, Carina; Carvalho, Ana; Gernaey, Krist V.

2017-01-01

-echelon mixed integer linear programming problem is proposed based upon a previous model, GlyThink. In the new formulation, market uncertainties are taken into account at the strategic planning level. The robustness of the supply chain structures is analyzed based on statistical data provided...... by the implementation of the Monte Carlo method, where a deterministic optimization problem is solved for each scenario. Furthermore, the solution of the stochastic multi-objective optimization model, points to the Pareto set of trade-off solutions obtained when maximizing the NPV and minimizing environmental......The optimal design and planning of glycerol-based biorefinery supply chains is critical for the development and implementation of this concept in a sustainable manner. To achieve this, a decision-making framework is proposed in this work, to holistically optimize the design and planning...

System of optimization computations of five year plans in the coal industry. [USSR

Energy Technology Data Exchange (ETDEWEB)

Goyzman, E I; Korenev, V G

1980-01-01

A system of optimization computations of five year plans is set forth which was developed over a number of years at the Central Scientific Research Institute of Economics and Scientific-Technical Information of the Coal Industry. Basic principles of design and methodological approaches are given which were used in development of the systemas well as levels of administration and information links between them. The report analyzes the characteristics of problems of planning which arise at different stages of formation of the five year plans and discusses possibilities of taking into account these characteristics in optimized models. Economic formulations of problems of optimization of five year plans are given as applied to two levels of administration: at the branch level and at the level of production associations. Economic-mathematical models of optimization of five year plans are developed for each of the levels and their characteristic features described. The primary methodological principles, on the basis of which optimization models were developed are examined. An economic-mathematical model with continuous variables was developed for the branch level of planning. Volumes of recovery according to a group of shafts in the stage of normal operation (stable group) of each production enterprise are adopted as model variables. A system of limitations which includes limitations on volumes and distinguishable resources is formulated. The minimum of operating expenses, minimum of capital investments and maximum of recovery volumes for the planned period can be used as the optimization criteria. An economic-mathematical model which uses integral variable was developed for the production association level.

Learning Motion Features for Example-Based Finger Motion Estimation for Virtual Characters

Science.gov (United States)

Mousas, Christos; Anagnostopoulos, Christos-Nikolaos

2017-09-01

This paper presents a methodology for estimating the motion of a character's fingers based on the use of motion features provided by a virtual character's hand. In the presented methodology, firstly, the motion data is segmented into discrete phases. Then, a number of motion features are computed for each motion segment of a character's hand. The motion features are pre-processed using restricted Boltzmann machines, and by using the different variations of semantically similar finger gestures in a support vector machine learning mechanism, the optimal weights for each feature assigned to a metric are computed. The advantages of the presented methodology in comparison to previous solutions are the following: First, we automate the computation of optimal weights that are assigned to each motion feature counted in our metric. Second, the presented methodology achieves an increase (about 17%) in correctly estimated finger gestures in comparison to a previous method.

The dosimetric impact of inversely optimized arc radiotherapy plan modulation for real-time dynamic MLC tracking delivery

DEFF Research Database (Denmark)

Falk, Marianne; Larsson, Tobias; Keall, P.

2012-01-01

Purpose: Real-time dynamic multileaf collimator (MLC) tracking for management of intrafraction tumor motion can be challenging for highly modulated beams, as the leaves need to travel far to adjust for target motion perpendicular to the leaf travel direction. The plan modulation can be reduced......-to-peak displacement of 2 cm and a cycle time of 6 s. The delivery was adjusted to the target motion using MLC tracking, guided in real-time by an infrared optical system. The dosimetric results were evaluated using gamma index evaluation with static target measurements as reference. Results: The plan quality...

Optimality of profit-including prices under ideal planning.

Science.gov (United States)

Samuelson, P A

1973-07-01

Although prices calculated by a constant percentage markup on all costs (nonlabor as well as direct-labor) are usually admitted to be more realistic for a competitive capitalistic model, the view is often expressed that, for optimal planning purposes, the "values" model of Marx's Capital, Volume I, is to be preferred. It is shown here that an optimal-control model that maximizes discounted social utility of consumption per capita and that ultimately approaches a steady state must ultimately have optimal pricing that involves equal rates of steady-state profit in all industries; and such optimal pricing will necessarily deviate from Marx's model of equal rates of surplus value (markups on direct-labor only) in all industries.

Optimization of Investment Planning Based on Game-Theoretic Approach

Directory of Open Access Journals (Sweden)

Elena Vladimirovna Butsenko

2018-03-01

Full Text Available The game-theoretic approach has a vast potential in solving economic problems. On the other hand, the theory of games itself can be enriched by the studies of real problems of decision-making. Hence, this study is aimed at developing and testing the game-theoretic technique to optimize the management of investment planning. This technique enables to forecast the results and manage the processes of investment planning. The proposed method of optimizing the management of investment planning allows to choose the best development strategy of an enterprise. This technique uses the “game with nature” model, and the Wald criterion, the maximum criterion and the Hurwitz criterion as criteria. The article presents a new algorithm for constructing the proposed econometric method to optimize investment project management. This algorithm combines the methods of matrix games. Furthermore, I show the implementation of this technique in a block diagram. The algorithm includes the formation of initial data, the elements of the payment matrix, as well as the definition of maximin, maximal, compromise and optimal management strategies. The methodology is tested on the example of the passenger transportation enterprise of the Sverdlovsk Railway in Ekaterinburg. The application of the proposed methodology and the corresponding algorithm allowed to obtain an optimal price strategy for transporting passengers for one direction of traffic. This price strategy contributes to an increase in the company’s income with minimal risk from the launch of this direction. The obtained results and conclusions show the effectiveness of using the developed methodology for optimizing the management of investment processes in the enterprise. The results of the research can be used as a basis for the development of an appropriate tool and applied by any economic entity in its investment activities.

Improved Planning Time and Plan Quality Through Multicriteria Optimization for Intensity-Modulated Radiotherapy

International Nuclear Information System (INIS)

Craft, David L.; Hong, Theodore S.; Shih, Helen A.; Bortfeld, Thomas R.

2012-01-01

Purpose: To test whether multicriteria optimization (MCO) can reduce treatment planning time and improve plan quality in intensity-modulated radiotherapy (IMRT). Methods and Materials: Ten IMRT patients (5 with glioblastoma and 5 with locally advanced pancreatic cancers) were logged during the standard treatment planning procedure currently in use at Massachusetts General Hospital (MGH). Planning durations and other relevant planning information were recorded. In parallel, the patients were planned using an MCO planning system, and similar planning time data were collected. The patients were treated with the standard plan, but each MCO plan was also approved by the physicians. Plans were then blindly reviewed 3 weeks after planning by the treating physician. Results: In all cases, the treatment planning time was vastly shorter for the MCO planning (average MCO treatment planning time was 12 min; average standard planning time was 135 min). The physician involvement time in the planning process increased from an average of 4.8 min for the standard process to 8.6 min for the MCO process. In all cases, the MCO plan was blindly identified as the superior plan. Conclusions: This provides the first concrete evidence that MCO-based planning is superior in terms of both planning efficiency and dose distribution quality compared with the current trial and error–based IMRT planning approach.

Vector-model-supported optimization in volumetric-modulated arc stereotactic radiotherapy planning for brain metastasis

International Nuclear Information System (INIS)

Liu, Eva Sau Fan; Wu, Vincent Wing Cheung; Harris, Benjamin; Foote, Matthew; Lehman, Margot; Chan, Lawrence Wing Chi

2017-01-01

Long planning time in volumetric-modulated arc stereotactic radiotherapy (VMA-SRT) cases can limit its clinical efficiency and use. A vector model could retrieve previously successful radiotherapy cases that share various common anatomic features with the current case. The prsent study aimed to develop a vector model that could reduce planning time by applying the optimization parameters from those retrieved reference cases. Thirty-six VMA-SRT cases of brain metastasis (gender, male [n = 23], female [n = 13]; age range, 32 to 81 years old) were collected and used as a reference database. Another 10 VMA-SRT cases were planned with both conventional optimization and vector-model-supported optimization, following the oncologists' clinical dose prescriptions. Planning time and plan quality measures were compared using the 2-sided paired Wilcoxon signed rank test with a significance level of 0.05, with positive false discovery rate (pFDR) of less than 0.05. With vector-model-supported optimization, there was a significant reduction in the median planning time, a 40% reduction from 3.7 to 2.2 hours (p = 0.002, pFDR = 0.032), and for the number of iterations, a 30% reduction from 8.5 to 6.0 (p = 0.006, pFDR = 0.047). The quality of plans from both approaches was comparable. From these preliminary results, vector-model-supported optimization can expedite the optimization of VMA-SRT for brain metastasis while maintaining plan quality.

Vector-model-supported optimization in volumetric-modulated arc stereotactic radiotherapy planning for brain metastasis

Energy Technology Data Exchange (ETDEWEB)

Liu, Eva Sau Fan [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong); Wu, Vincent Wing Cheung [Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong); Harris, Benjamin [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); Foote, Matthew; Lehman, Margot [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); School of Medicine, University of Queensland (Australia); Chan, Lawrence Wing Chi, E-mail: wing.chi.chan@polyu.edu.hk [Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong)

2017-07-01

Long planning time in volumetric-modulated arc stereotactic radiotherapy (VMA-SRT) cases can limit its clinical efficiency and use. A vector model could retrieve previously successful radiotherapy cases that share various common anatomic features with the current case. The prsent study aimed to develop a vector model that could reduce planning time by applying the optimization parameters from those retrieved reference cases. Thirty-six VMA-SRT cases of brain metastasis (gender, male [n = 23], female [n = 13]; age range, 32 to 81 years old) were collected and used as a reference database. Another 10 VMA-SRT cases were planned with both conventional optimization and vector-model-supported optimization, following the oncologists' clinical dose prescriptions. Planning time and plan quality measures were compared using the 2-sided paired Wilcoxon signed rank test with a significance level of 0.05, with positive false discovery rate (pFDR) of less than 0.05. With vector-model-supported optimization, there was a significant reduction in the median planning time, a 40% reduction from 3.7 to 2.2 hours (p = 0.002, pFDR = 0.032), and for the number of iterations, a 30% reduction from 8.5 to 6.0 (p = 0.006, pFDR = 0.047). The quality of plans from both approaches was comparable. From these preliminary results, vector-model-supported optimization can expedite the optimization of VMA-SRT for brain metastasis while maintaining plan quality.

Optimizing monoscopic kV fluoro acquisition for prostate intrafraction motion evaluation

International Nuclear Information System (INIS)

Adamson, Justus; Wu Qiuwen

2009-01-01

Monoscopic kV imaging during radiotherapy has been recently implemented for prostate intrafraction motion evaluation. However, the accuracy of 3D localization techniques from monoscopic imaging of prostate and the effect of acquisition parameters on the 3D accuracy have not been studied in detail, with imaging dose remaining a concern. In this paper, we investigate methods to optimize the kV acquisition parameters and imaging protocol to achieve improved 3D localization and 2D image registration accuracy for minimal imaging dose. Prostate motion during radiotherapy was simulated using existing cine-MRI measurements, and was used to investigate the accuracy of various 3D localization techniques and the effect of the kV acquisition protocol. We also investigated the relationship between mAs and the accuracy of the 2D image registration for localization of fiducial markers and we measured imaging dose for a 30 cm diameter phantom to evaluate the necessary dose to achieve acceptable image registration accuracy. Simulations showed that the error in assuming the shortest path to localize the prostate in 3D using monoscopic imaging during a typical IMRT fraction will be less than ∼1.5 mm for 95% of localizations, and will also depend on prostate motion distribution, treatment duration and image acquisition and treatment protocol. Most uncertainty cannot be reduced from higher imaging frequency or acquiring during gantry rotation between beams. Measured maximum surface dose to the cylindrical phantom from monoscopic kV intrafraction acquisitions varied between 0.4 and 5.5 mGy, depending on the acquisition protocol, and was lower than the required dose for CBCT (21.1 mGy). Imaging dose can be lowered by ∼15-40% when mAs is optimized with acquisition angle. Images acquired during MV beam delivery require increased mAs to obtain the same level of registration accuracy, with mAs/registration increasing roughly linearly with field size and dose rate.

Inverse planning and optimization: a comparison of solutions

Energy Technology Data Exchange (ETDEWEB)

Ringor, Michael [School of Health Sciences, Purdue University, West Lafayette, IN (United States); Papiez, Lech [Department of Radiation Oncology, Indiana University, Indianapolis, IN (United States)

1998-09-01

The basic problem in radiation therapy treatment planning is to determine an appropriate set of treatment parameters that would induce an effective dose distribution inside a patient. One can approach this task as an inverse problem, or as an optimization problem. In this presentation, we compare both approaches. The inverse problem is presented as a dose reconstruction problem similar to tomography reconstruction. We formulate the optimization problem as linear and quadratic programs. Explicit comparisons are made between the solutions obtained by inversion and those obtained by optimization for the case in which scatter and attenuation are ignored (the NS-NA approximation)

An optimization planning technique for Suez Canal Network in Egypt

Energy Technology Data Exchange (ETDEWEB)

Abou El-Ela, A.A.; El-Zeftawy, A.A.; Allam, S.M.; Atta, Gasir M. [Electrical Engineering Dept., Faculty of Eng., Shebin El-Kom (Egypt)

2010-02-15

This paper introduces a proposed optimization technique POT for predicting the peak load demand and planning of transmission line systems. Many of traditional methods have been presented for long-term load forecasting of electrical power systems. But, the results of these methods are approximated. Therefore, the artificial neural network (ANN) technique for long-term peak load forecasting is modified and discussed as a modern technique in long-term load forecasting. The modified technique is applied on the Egyptian electrical network dependent on its historical data to predict the electrical peak load demand forecasting up to year 2017. This technique is compared with extrapolation of trend curves as a traditional method. The POT is applied also to obtain the optimal planning of transmission lines for the 220 kV of Suez Canal Network (SCN) using the ANN technique. The minimization of the transmission network costs are considered as an objective function, while the transmission lines (TL) planning constraints are satisfied. Zafarana site on the Red Sea coast is considered as an optimal site for installing big wind farm (WF) units in Egypt. So, the POT is applied to plan both the peak load and the electrical transmission of SCN with and without considering WF to develop the impact of WF units on the electrical transmission system of Egypt, considering the reliability constraints which were taken as a separate model in the previous techniques. The application on SCN shows the capability and the efficiently of the proposed techniques to obtain the predicting peak load demand and the optimal planning of transmission lines of SCN up to year 2017. (author)

Resampling: An optimization method for inverse planning in robotic radiosurgery

International Nuclear Information System (INIS)

Schweikard, Achim; Schlaefer, Alexander; Adler, John R. Jr.

2006-01-01

By design, the range of beam directions in conventional radiosurgery are constrained to an isocentric array. However, the recent introduction of robotic radiosurgery dramatically increases the flexibility of targeting, and as a consequence, beams need be neither coplanar nor isocentric. Such a nonisocentric design permits a large number of distinct beam directions to be used in one single treatment. These major technical differences provide an opportunity to improve upon the well-established principles for treatment planning used with GammaKnife or LINAC radiosurgery. With this objective in mind, our group has developed over the past decade an inverse planning tool for robotic radiosurgery. This system first computes a set of beam directions, and then during an optimization step, weights each individual beam. Optimization begins with a feasibility query, the answer to which is derived through linear programming. This approach offers the advantage of completeness and avoids local optima. Final beam selection is based on heuristics. In this report we present and evaluate a new strategy for utilizing the advantages of linear programming to improve beam selection. Starting from an initial solution, a heuristically determined set of beams is added to the optimization problem, while beams with zero weight are removed. This process is repeated to sample a set of beams much larger compared with typical optimization. Experimental results indicate that the planning approach efficiently finds acceptable plans and that resampling can further improve its efficiency

Fast Generation of Near-Optimal Plans for Eco-Efficient Stowage of Large Container Vessels

DEFF Research Database (Denmark)

Pacino, Dario; Delgado, Alberto; Jensen, Rune Møller

2011-01-01

Eco-efficient stowage plans that are both competitive and sustainable have become a priority for the shipping industry. Stowage planning is NP-hard and is a challenging optimization problem in practice. We propose a new 2-phase approach that generates near-optimal stowage plans and fulfills indus...

Maintenance optimization plan for essential equipment reliability

International Nuclear Information System (INIS)

Steffen, D.H.

1996-02-01

The Maintenance Optimization Plan (MOP) for Essential Equipment Reliability will furnish Tank Waste Remediation System (TWRS) management with a pro-active, forward-thinking process for maintaining essential structures, systems, and components (ESSC) at the Hanford Site tank farms in their designed condition, and to ensure optimum ESSC availability and reliability

Spatial planning via extremal optimization enhanced by cell-based local search

International Nuclear Information System (INIS)

Sidiropoulos, Epaminondas

2014-01-01

A new treatment is presented for land use planning problems by means of extremal optimization in conjunction to cell-based neighborhood local search. Extremal optimization, inspired by self-organized critical models of evolution has been applied mainly to the solution of classical combinatorial optimization problems. Cell-based local search has been employed by the author elsewhere in problems of spatial resource allocation in combination with genetic algorithms and simulated annealing. In this paper it complements extremal optimization in order to enhance its capacity for a spatial optimization problem. The hybrid method thus formed is compared to methods of the literature on a specific characteristic problem. It yields better results both in terms of objective function values and in terms of compactness. The latter is an important quantity for spatial planning. The present treatment yields significant compactness values as emergent results

Pre-optimization of radiotherapy treatment planning: an artificial neural network classification aided technique

International Nuclear Information System (INIS)

Hosseini-Ashrafi, M.E.; Bagherebadian, H.; Yahaqi, E.

1999-01-01

A method has been developed which, by using the geometric information from treatment sample cases, selects from a given data set an initial treatment plan as a step for treatment plan optimization. The method uses an artificial neural network (ANN) classification technique to select a best matching plan from the 'optimized' ANN database. Separate back-propagation ANN classifiers were trained using 50, 60 and 77 examples for three groups of treatment case classes (up to 21 examples from each class were used). The performance of the classifiers in selecting the correct treatment class was tested using the leave-one-out method; the networks were optimized with respect their architecture. For the three groups used in this study, successful classification fractions of 0.83, 0.98 and 0.93 were achieved by the optimized ANN classifiers. The automated response of the ANN may be used to arrive at a pre-plan where many treatment parameters may be identified and therefore a significant reduction in the steps required to arrive at the optimum plan may be achieved. Treatment planning 'experience' and also results from lengthy calculations may be used for training the ANN. (author)

A Generalized Decision Framework Using Multi-objective Optimization for Water Resources Planning

Science.gov (United States)

Basdekas, L.; Stewart, N.; Triana, E.

2013-12-01

Colorado Springs Utilities (CSU) is currently engaged in an Integrated Water Resource Plan (IWRP) to address the complex planning scenarios, across multiple time scales, currently faced by CSU. The modeling framework developed for the IWRP uses a flexible data-centered Decision Support System (DSS) with a MODSIM-based modeling system to represent the operation of the current CSU raw water system coupled with a state-of-the-art multi-objective optimization algorithm. Three basic components are required for the framework, which can be implemented for planning horizons ranging from seasonal to interdecadal. First, a water resources system model is required that is capable of reasonable system simulation to resolve performance metrics at the appropriate temporal and spatial scales of interest. The system model should be an existing simulation model, or one developed during the planning process with stakeholders, so that 'buy-in' has already been achieved. Second, a hydrologic scenario tool(s) capable of generating a range of plausible inflows for the planning period of interest is required. This may include paleo informed or climate change informed sequences. Third, a multi-objective optimization model that can be wrapped around the system simulation model is required. The new generation of multi-objective optimization models do not require parameterization which greatly reduces problem complexity. Bridging the gap between research and practice will be evident as we use a case study from CSU's planning process to demonstrate this framework with specific competing water management objectives. Careful formulation of objective functions, choice of decision variables, and system constraints will be discussed. Rather than treating results as theoretically Pareto optimal in a planning process, we use the powerful multi-objective optimization models as tools to more efficiently and effectively move out of the inferior decision space. The use of this framework will help CSU

Path Planning of Mobile Elastic Robotic Arms by Indirect Approach of Optimal Control

Directory of Open Access Journals (Sweden)

Moharam Habibnejad Korayem

2011-03-01

Full Text Available Finding optimal trajectory is critical in several applications of robot manipulators. This paper is applied the open-loop optimal control approach for generating the optimal trajectory of the flexible mobile manipulators in point-to-point motion. This method is based on the Pontryagin-s minimum principle that by providing a two-point boundary value problem is solved the problem. This problem is known to be complex in particular when combined motion of the base and manipulator, non-holonomic constraint of the base and highly non-linear and complicated dynamic equations as a result of flexible nature of links are taken into account. The study emphasizes on modeling of the complete optimal control problem by remaining all nonlinear state and costate variables as well as control constraints. In this method, designer can compromise between different objectives by considering the proper penalty matrices and it yields to choose the proper trajectory among the various paths. The effectiveness and capability of the proposed approach are demonstrated through simulation studies. Finally, to verify the proposed method, the simulation results obtained from the model are compared with the results of those available in the literature.

TH-CD-209-06: LET-Based Adjustment of IMPT Plans Using Prioritized Optimization

Energy Technology Data Exchange (ETDEWEB)

Unkelbach, J; Giantsoudi, D; Paganetti, H [Massachusetts General Hospital, Boston, MA (United States); Botas, P [Massachusetts General Hospital, Boston, MA (United States); Heidelberg University, Heidelberg, DE (Germany); Qin, N; Jia, X [The University of Texas Southwestern Medical Ctr, Dallas, TX (United States)

2016-06-15

Purpose: In-vitro experiments suggest an increase in proton relative biological effectiveness (RBE) towards the end of range. However, proton treatment planning and dose reporting for clinical outcome assessment has been based on physical dose and constant RBE. Therefore, treatment planning for intensity-modulated proton therapy (IMPT) is unlikely to transition radically to pure RBE-based planning. We suggest a hybrid approach where treatment plans are initially created based on physical dose constraints and prescriptions, and are subsequently altered to avoid high linear energy transfer (LET) in critical structures while limiting the degradation of the physical dose distribution. Methods: To allow fast optimization based on dose and LET we extended a GPU-based Monte-Carlo code towards providing dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dose objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of physical dose and LET (LETxD). To first approximation, LETxD represents a measure of the additional biological dose that is caused by high LET. Regarding optimization techniques, LETxD has the advantage of being a linear function of the pencil beam intensities. Results: The method is applicable to treatments where serial critical structures with maximum dose constraint are located in or near the target. We studied intra-cranial tumors (high-grade meningiomas, base-of-skull chordomas) where the target (CTV) overlaps with the brainstem and optic structures. Often, high LETxD in critical structures can be avoided while minimally compromising physical dose planning objectives. Conclusion: LET-based re-optimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose

Reliability-Based Optimal Design of Experiment Plans for Offshore Structures

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Faber, Michael Havbro; Kroon, I. B.

1993-01-01

Design of cost optimal experiment plans on the basis of a preposterior analysis is discussed. In particular the planning of on-site response measurements on offshore structures in order to update probabilistic models for fatigue life estimation is addressed. Special emphasis is given to modelling...

Radiotherapy of tumors under respiratory motion. Estimation of the motional velocity field and dose accumulation based on 4D image data

International Nuclear Information System (INIS)

Werner, Rene

2013-01-01

Respiratory motion represents a major challenge in radiation therapy in general, and especially for the therapy of lung tumors. In recent years and due to the introduction of modern techniques to 'acquire temporally resolved computed tomography images (4D CT images), different approaches have been developed to explicitly account for breathing motion during treatment. An integral component of such approaches is the concept of motion field estimation, which aims at a mathematical description and the computation of the motion sequences represented by the patient's images. As part of a 4D dose calculation/dose accumulation, the resulting vector fields are applied for assessing and accounting for breathing-induced effects on the dose distribution to be delivered. The reliability of related 4D treatment planning concepts is therefore directly tailored to the precision of the underlying motion field estimation process. Taking this into account, the thesis aims at developing optimized methods for the estimation of motion fields using 4D CT images and applying the resulting methods for the analysis of breathing induced dosimetric effects in radiation therapy. The thesis is subdivided into three parts that thematically build upon each other. The first part of the thesis is about the implementation, evaluation and optimization of methods for motion field estimation with the goal of precisely assessing respiratory motion of anatomical and pathological structures represented in a patient's 4D er image sequence; this step is the basis of subsequent developments and analysis parts. Especially non-linear registration techniques prove to be well suited to this purpose. After being optimized for the particular problem at hand, it is shown as part of an extensive multi-criteria evaluation study and additionally taking into account publicly accessible evaluation platforms that such methods allow estimating motion fields with subvoxel accuracy - which means that the developed methods

TH-E-BRE-08: GPU-Monte Carlo Based Fast IMRT Plan Optimization

Energy Technology Data Exchange (ETDEWEB)

Li, Y; Tian, Z; Shi, F; Jiang, S; Jia, X [The University of Texas Southwestern Medical Ctr, Dallas, TX (United States)

2014-06-15

Purpose: Intensity-modulated radiation treatment (IMRT) plan optimization needs pre-calculated beamlet dose distribution. Pencil-beam or superposition/convolution type algorithms are typically used because of high computation speed. However, inaccurate beamlet dose distributions, particularly in cases with high levels of inhomogeneity, may mislead optimization, hindering the resulting plan quality. It is desire to use Monte Carlo (MC) methods for beamlet dose calculations. Yet, the long computational time from repeated dose calculations for a number of beamlets prevents this application. It is our objective to integrate a GPU-based MC dose engine in lung IMRT optimization using a novel two-steps workflow. Methods: A GPU-based MC code gDPM is used. Each particle is tagged with an index of a beamlet where the source particle is from. Deposit dose are stored separately for beamlets based on the index. Due to limited GPU memory size, a pyramid space is allocated for each beamlet, and dose outside the space is neglected. A two-steps optimization workflow is proposed for fast MC-based optimization. At first step, rough beamlet dose calculations is conducted with only a small number of particles per beamlet. Plan optimization is followed to get an approximated fluence map. In the second step, more accurate beamlet doses are calculated, where sampled number of particles for a beamlet is proportional to the intensity determined previously. A second-round optimization is conducted, yielding the final Result. Results: For a lung case with 5317 beamlets, 10{sup 5} particles per beamlet in the first round, and 10{sup 8} particles per beam in the second round are enough to get a good plan quality. The total simulation time is 96.4 sec. Conclusion: A fast GPU-based MC dose calculation method along with a novel two-step optimization workflow are developed. The high efficiency allows the use of MC for IMRT optimizations.

Dynamic trajectory-based couch motion for improvement of radiation therapy trajectories in cranial SRT

Energy Technology Data Exchange (ETDEWEB)

MacDonald, R. Lee [Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia B3H 4R2 (Canada); Thomas, Christopher G., E-mail: Chris.Thomas@cdha.nshealth.ca [Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia B3H 4R2 (Canada); Department of Medical Physics, Nova Scotia Cancer Centre, Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia B3H 1V7 (Canada); Department of Radiation Oncology, Dalhousie University, Halifax, Nova Scotia B3H 4R2 (Canada); Department of Radiology, Dalhousie University, Halifax, Nova Scotia B3H 4R2 (Canada)

2015-05-15

Purpose: To investigate potential improvement in external beam stereotactic radiation therapy plan quality for cranial cases using an optimized dynamic gantry and patient support couch motion trajectory, which could minimize exposure to sensitive healthy tissue. Methods: Anonymized patient anatomy and treatment plans of cranial cancer patients were used to quantify the geometric overlap between planning target volumes and organs-at-risk (OARs) based on their two-dimensional projection from source to a plane at isocenter as a function of gantry and couch angle. Published dose constraints were then used as weighting factors for the OARs to generate a map of couch-gantry coordinate space, indicating degree of overlap at each point in space. A couch-gantry collision space was generated by direct measurement on a linear accelerator and couch using an anthropomorphic solid-water phantom. A dynamic, fully customizable algorithm was written to generate a navigable ideal trajectory for the patient specific couch-gantry space. The advanced algorithm can be used to balance the implementation of absolute minimum values of overlap with the clinical practicality of large-scale couch motion and delivery time. Optimized cranial cancer treatment trajectories were compared to conventional treatment trajectories. Results: Comparison of optimized treatment trajectories with conventional treatment trajectories indicated an average decrease in mean dose to the OARs of 19% and an average decrease in maximum dose to the OARs of 12%. Degradation was seen for homogeneity index (6.14% ± 0.67%–5.48% ± 0.76%) and conformation number (0.82 ± 0.02–0.79 ± 0.02), but neither was statistically significant. Removal of OAR constraints from volumetric modulated arc therapy optimization reveals that reduction in dose to OARs is almost exclusively due to the optimized trajectory and not the OAR constraints. Conclusions: The authors’ study indicated that simultaneous couch and gantry motion

Dynamic trajectory-based couch motion for improvement of radiation therapy trajectories in cranial SRT

International Nuclear Information System (INIS)

MacDonald, R. Lee; Thomas, Christopher G.

2015-01-01

Purpose: To investigate potential improvement in external beam stereotactic radiation therapy plan quality for cranial cases using an optimized dynamic gantry and patient support couch motion trajectory, which could minimize exposure to sensitive healthy tissue. Methods: Anonymized patient anatomy and treatment plans of cranial cancer patients were used to quantify the geometric overlap between planning target volumes and organs-at-risk (OARs) based on their two-dimensional projection from source to a plane at isocenter as a function of gantry and couch angle. Published dose constraints were then used as weighting factors for the OARs to generate a map of couch-gantry coordinate space, indicating degree of overlap at each point in space. A couch-gantry collision space was generated by direct measurement on a linear accelerator and couch using an anthropomorphic solid-water phantom. A dynamic, fully customizable algorithm was written to generate a navigable ideal trajectory for the patient specific couch-gantry space. The advanced algorithm can be used to balance the implementation of absolute minimum values of overlap with the clinical practicality of large-scale couch motion and delivery time. Optimized cranial cancer treatment trajectories were compared to conventional treatment trajectories. Results: Comparison of optimized treatment trajectories with conventional treatment trajectories indicated an average decrease in mean dose to the OARs of 19% and an average decrease in maximum dose to the OARs of 12%. Degradation was seen for homogeneity index (6.14% ± 0.67%–5.48% ± 0.76%) and conformation number (0.82 ± 0.02–0.79 ± 0.02), but neither was statistically significant. Removal of OAR constraints from volumetric modulated arc therapy optimization reveals that reduction in dose to OARs is almost exclusively due to the optimized trajectory and not the OAR constraints. Conclusions: The authors’ study indicated that simultaneous couch and gantry motion

Optimized Planning Target Volume for Intact Cervical Cancer

International Nuclear Information System (INIS)

Khan, Alvin; Jensen, Lindsay G.; Sun Shuai; Song, William Y.; Yashar, Catheryn M.; Mundt, Arno J.; Zhang Fuquan; Jiang, Steve B.; Mell, Loren K.

2012-01-01

Purpose: To model interfraction clinical target volume (CTV) variation in patients with intact cervical cancer and design a planning target volume (PTV) that minimizes normal tissue dose while maximizing CTV coverage. Methods and Materials: We analyzed 50 patients undergoing external-beam radiotherapy for intact cervical cancer using daily online cone-beam computed tomography (CBCT). The CBCTs (n = 972) for each patient were rigidly registered to the planning CT. The CTV was delineated on the planning CT (CTV 0 ) and the set of CBCTs ({CTV 1 –CTV 25 }). Manual (n = 98) and automated (n = 668) landmarks were placed over the surface of CTV 0 with reference to defined anatomic structures. Normal vectors were extended from each landmark, and the minimum length required for a given probability of encompassing CTV 1 –CTV 25 was computed. The resulting expansions were used to generate an optimized PTV. Results: The mean (SD; range) normal vector length to ensure 95% coverage was 4.3 mm (2.7 mm; 1–16 mm). The uniform expansion required to ensure 95% probability of CTV coverage was 13 mm. An anisotropic margin of 20 mm anteriorly and posteriorly and 10 mm superiorly, inferiorly, and laterally also would have ensured a 95% probability of CTV coverage. The volume of the 95% optimized PTV (1470 cm 3 ) was significantly lower than both the anisotropic PTV (2220 cm 3 ) and the uniformly expanded PTV (2110 cm 3 ) (p 0 , 5–10 mm along the interfaces of CTV 0 with the bladder and rectum, and 10–14 mm along the anterior surface of CTV 0 at the level of the uterus. Conclusion: Optimizing PTV definition according to surface landmarking resulted in a high probability of CTV coverage with reduced PTV volumes. Our results provide data justifying planning margins to use in practice and clinical trials.

Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

Science.gov (United States)

Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah

2015-01-01

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

Ultrafast treatment plan optimization for volumetric modulated arc therapy (VMAT).

Science.gov (United States)

Men, Chunhua; Romeijn, H Edwin; Jia, Xun; Jiang, Steve B

2010-11-01

To develop a novel aperture-based algorithm for volumetric modulated are therapy (VMAT) treatment plan optimization with high quality and high efficiency. The VMAT optimization problem is formulated as a large-scale convex programming problem solved by a column generation approach. The authors consider a cost function consisting two terms, the first enforcing a desired dose distribution and the second guaranteeing a smooth dose rate variation between successive gantry angles. A gantry rotation is discretized into 180 beam angles and for each beam angle, only one MLC aperture is allowed. The apertures are generated one by one in a sequential way. At each iteration of the column generation method, a deliverable MLC aperture is generated for one of the unoccupied beam angles by solving a subproblem with the consideration of MLC mechanic constraints. A subsequent master problem is then solved to determine the dose rate at all currently generated apertures by minimizing the cost function. When all 180 beam angles are occupied, the optimization completes, yielding a set of deliverable apertures and associated dose rates that produce a high quality plan. The algorithm was preliminarily tested on five prostate and five head-and-neck clinical cases, each with one full gantry rotation without any couch/collimator rotations. High quality VMAT plans have been generated for all ten cases with extremely high efficiency. It takes only 5-8 min on CPU (MATLAB code on an Intel Xeon 2.27 GHz CPU) and 18-31 s on GPU (CUDA code on an NVIDIA Tesla C1060 GPU card) to generate such plans. The authors have developed an aperture-based VMAT optimization algorithm which can generate clinically deliverable high quality treatment plans at very high efficiency.

SU-E-J-89: Motion Effects On Organ Dose in Respiratory Gated Stereotactic Body Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Wang, T; Zhu, L [Georgia Institute of Technology, Atlanta, GA (Georgia); Khan, M; Landry, J; Rajpara, R; Hawk, N [Emory University, Atlanta, GA (United States)

2014-06-01

Purpose: Existing reports on gated radiation therapy focus mainly on optimizing dose delivery to the target structure. This work investigates the motion effects on radiation dose delivered to organs at risk (OAR) in respiratory gated stereotactic body radiation therapy (SBRT). A new algorithmic tool of dose analysis is developed to evaluate the optimality of gating phase for dose sparing on OARs while ensuring adequate target coverage. Methods: Eight patients with pancreatic cancer were treated on a phase I prospective study employing 4DCT-based SBRT. For each patient, 4DCT scans are acquired and sorted into 10 respiratory phases (inhale-exhale- inhale). Treatment planning is performed on the average CT image. The average CT is spatially registered to other phases. The resultant displacement field is then applied on the plan dose map to estimate the actual dose map for each phase. Dose values of each voxel are fitted to a sinusoidal function. Fitting parameters of dose variation, mean delivered dose and optimal gating phase for each voxel over respiration cycle are mapped on the dose volume. Results: The sinusoidal function accurately models the dose change during respiratory motion (mean fitting error 4.6%). In the eight patients, mean dose variation is 3.3 Gy on OARs with maximum of 13.7 Gy. Two patients have about 100cm{sup 3} volumes covered by more than 5 Gy deviation. The mean delivered dose maps are similar to plan dose with slight deformation. The optimal gating phase highly varies across the patient, with phase 5 or 6 on about 60% of the volume, and phase 0 on most of the rest. Conclusion: A new algorithmic tool is developed to conveniently quantify dose deviation on OARs from plan dose during the respiratory cycle. The proposed software facilitates the treatment planning process by providing the optimal respiratory gating phase for dose sparing on each OAR.

A new Monte Carlo-based treatment plan optimization approach for intensity modulated radiation therapy.

Science.gov (United States)

Li, Yongbao; Tian, Zhen; Shi, Feng; Song, Ting; Wu, Zhaoxia; Liu, Yaqiang; Jiang, Steve; Jia, Xun

2015-04-07

Intensity-modulated radiation treatment (IMRT) plan optimization needs beamlet dose distributions. Pencil-beam or superposition/convolution type algorithms are typically used because of their high computational speed. However, inaccurate beamlet dose distributions may mislead the optimization process and hinder the resulting plan quality. To solve this problem, the Monte Carlo (MC) simulation method has been used to compute all beamlet doses prior to the optimization step. The conventional approach samples the same number of particles from each beamlet. Yet this is not the optimal use of MC in this problem. In fact, there are beamlets that have very small intensities after solving the plan optimization problem. For those beamlets, it may be possible to use fewer particles in dose calculations to increase efficiency. Based on this idea, we have developed a new MC-based IMRT plan optimization framework that iteratively performs MC dose calculation and plan optimization. At each dose calculation step, the particle numbers for beamlets were adjusted based on the beamlet intensities obtained through solving the plan optimization problem in the last iteration step. We modified a GPU-based MC dose engine to allow simultaneous computations of a large number of beamlet doses. To test the accuracy of our modified dose engine, we compared the dose from a broad beam and the summed beamlet doses in this beam in an inhomogeneous phantom. Agreement within 1% for the maximum difference and 0.55% for the average difference was observed. We then validated the proposed MC-based optimization schemes in one lung IMRT case. It was found that the conventional scheme required 10(6) particles from each beamlet to achieve an optimization result that was 3% difference in fluence map and 1% difference in dose from the ground truth. In contrast, the proposed scheme achieved the same level of accuracy with on average 1.2 × 10(5) particles per beamlet. Correspondingly, the computation

An adaptive dual-optimal path-planning technique for unmanned air vehicles

Directory of Open Access Journals (Sweden)

Whitfield Clifford A.

2016-01-01

Full Text Available A multi-objective technique for unmanned air vehicle path-planning generation through task allocation has been developed. The dual-optimal path-planning technique generates real-time adaptive flight paths based on available flight windows and environmental influenced objectives. The environmentally-influenced flight condition determines the aircraft optimal orientation within a downstream virtual window of possible vehicle destinations that is based on the vehicle’s kinematics. The intermittent results are then pursued by a dynamic optimization technique to determine the flight path. This path-planning technique is a multi-objective optimization procedure consisting of two goals that do not require additional information to combine the conflicting objectives into a single-objective. The technique was applied to solar-regenerative high altitude long endurance flight which can benefit significantly from an adaptive real-time path-planning technique. The objectives were to determine the minimum power required flight paths while maintaining maximum solar power for continual surveillance over an area of interest (AOI. The simulated path generation technique prolonged the flight duration over a sustained turn loiter flight path by approximately 2 months for a year of flight. The potential for prolonged solar powered flight was consistent for all latitude locations, including 2 months of available flight at 60° latitude, where sustained turn flight was no longer capable.

SU-E-J-181: Effect of Prostate Motion On Combined Brachytherapy and External Beam Dose Based On Daily Motion of the Prostate

Energy Technology Data Exchange (ETDEWEB)

Narayana, V; McLaughlin, P [Providence Cancer Center, Southfield, MI (United States); University of Michigan, Ann Arbor, MI (United States); Ealbaj, J [University of Michigan, Ann Arbor, MI (United States)

2015-06-15

Purpose: In this study, the adequacy of target expansions on the combined external beam and implant dose was examined based on the measured daily motion of the prostate. Methods: Thirty patients received an I–125 prostate implant prescribed to dose of 90Gy. This was followed by external beam to deliver a dose of 90Gyeq (external beam equivalent) to the prostate over 25 to 30 fractions. An ideal IMRT plan was developed by optimizing the external beam dose based on the delivered implant dose. The implant dose was converted to an equivalent external beam dose using the linear quadratic model. Patients were set up on the treatment table by daily orthogonal imaging and aligning the marker seeds in the prostate. Orthogonal images were obtained at the end of treatment to assess prostate intrafraction motion. Based on the observed motion of the markers between the initial and final images, 5 individual plans showing the actual dose delivered to the patient were calculated. A final true dose distribution was established based on summing the implant dose and the 5 external beam plans. Dose to the prostate, seminal vesicles, lymphnodes and normal tissues, rectal wall, urethra and lower sphincter were calculated and compared to ideal. On 18 patients who were sexually active, dose to the corpus cavernosum and internal pudendal artery was also calculated. Results: The average prostate motion in 3 orthogonal directions was less than 1 mm with a standard deviation of less than +2 mm. Dose and volume parameters showed that there was no decrease in dose to the targets and a marginal decrease in dose to in normal tissues. Conclusion: Dose delivered by seed implant moves with the prostate, decreasing the impact of intrafractions dose movement on actual dose delivered. Combined brachytherapy and external beam dose delivered to the prostate was not sensitive to prostate motion.

Optimal planning of gas turbine cogeneration system based on linear programming. Paper no. IGEC-1-ID09

International Nuclear Information System (INIS)

Oh, S.-D.; Kwak, H.-Y.

2005-01-01

An optimal planning for gas turbine cogeneration system has been studied. The planning problem considered in this study is to determine the optimal configuration of the system equipments and optimal operational policy of the system when the annual energy demands of electric power, heat and cooling are given a priori. The main benefit of the optimal planning is to minimize operational costs and to save energy by efficient energy utilization. A mixed-integer linear programming and the branch and bound algorithm have been adopted to obtain the optimal solution. Both the optimal configuration of the system equipments and the optimal operation policy has been obtained based on annual cost method. The planning method employed here may be applied to the planning problem of the cogeneration plant to any specific building or hotel. (author)

Advantages and limitations of navigation-based multicriteria optimization (MCO) for localized prostate cancer IMRT planning

International Nuclear Information System (INIS)

McGarry, Conor K.; Bokrantz, Rasmus; O’Sullivan, Joe M.; Hounsell, Alan R.

2014-01-01

Efficacy of inverse planning is becoming increasingly important for advanced radiotherapy techniques. This study’s aims were to validate multicriteria optimization (MCO) in RayStation (v2.4, RaySearch Laboratories, Sweden) against standard intensity-modulated radiation therapy (IMRT) optimization in Oncentra (v4.1, Nucletron BV, the Netherlands) and characterize dose differences due to conversion of navigated MCO plans into deliverable multileaf collimator apertures. Step-and-shoot IMRT plans were created for 10 patients with localized prostate cancer using both standard optimization and MCO. Acceptable standard IMRT plans with minimal average rectal dose were chosen for comparison with deliverable MCO plans. The trade-off was, for the MCO plans, managed through a user interface that permits continuous navigation between fluence-based plans. Navigated MCO plans were made deliverable at incremental steps along a trajectory between maximal target homogeneity and maximal rectal sparing. Dosimetric differences between navigated and deliverable MCO plans were also quantified. MCO plans, chosen as acceptable under navigated and deliverable conditions resulted in similar rectal sparing compared with standard optimization (33.7 ± 1.8 Gy vs 35.5 ± 4.2 Gy, p = 0.117). The dose differences between navigated and deliverable MCO plans increased as higher priority was placed on rectal avoidance. If the best possible deliverable MCO was chosen, a significant reduction in rectal dose was observed in comparison with standard optimization (30.6 ± 1.4 Gy vs 35.5 ± 4.2 Gy, p = 0.047). Improvements were, however, to some extent, at the expense of less conformal dose distributions, which resulted in significantly higher doses to the bladder for 2 of the 3 tolerance levels. In conclusion, similar IMRT plans can be created for patients with prostate cancer using MCO compared with standard optimization. Limitations exist within MCO regarding conversion of navigated plans to

Fast treatment plan modification with an over-relaxed Cimmino algorithm

International Nuclear Information System (INIS)

Wu Chuan; Jeraj, Robert; Lu Weiguo; Mackie, Thomas R.

2004-01-01

A method to quickly modify a treatment plan in adaptive radiotherapy was proposed and studied. The method is based on a Cimmino-type algorithm in linear programming. The fast convergence speed is achieved by over-relaxing the algorithm relaxation parameter from its sufficient convergence range of (0, 2) to (0, ∞). The algorithm parameters are selected so that the over-relaxed Cimmino (ORC) algorithm can effectively approximate an unconstrained re-optimization process in adaptive radiotherapy. To demonstrate the effectiveness and flexibility of the proposed method in adaptive radiotherapy, two scenarios with different organ motion/deformation of one nasopharyngeal case were presented with comparisons made between this method and the re-optimization method. In both scenarios, the ORC algorithm modified treatment plans have dose distributions that are similar to those given by the re-optimized treatment plans. It takes us using the ORC algorithm to finish a treatment plan modification at least three times faster than the re-optimization procedure compared

Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

Science.gov (United States)

Ausaf, Muhammad Farhan; Gao, Liang; Li, Xinyu

2015-12-01

For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

Feature-based plan adaptation for fast treatment planning in scanned ion beam therapy

International Nuclear Information System (INIS)

Chen Wenjing; Gemmel, Alexander; Rietzel, Eike

2013-01-01

We propose a plan adaptation method for fast treatment plan generation in scanned ion beam therapy. Analysis of optimized treatment plans with carbon ions indicates that the particle number modulation of consecutive rasterspots in depth shows little variation throughout target volumes with convex shape. Thus, we extract a depth-modulation curve (DMC) from existing reference plans and adapt it for creation of new plans in similar treatment situations. The proposed method is tested with seven CT serials of prostate patients and three digital phantom datasets generated with the MATLAB code. Plans are generated with a treatment planning software developed by GSI using single-field uniform dose optimization for all the CT datasets to serve as reference plans and ‘gold standard’. The adapted plans are generated based on the DMC derived from the reference plans of the same patient (intra-patient), different patient (inter-patient) and phantoms (phantom-patient). They are compared with the reference plans and a re-positioning strategy. Generally, in 1 min on a standard PC, either a physical plan or a biological plan can be generated with the adaptive method provided that the new target contour is available. In all the cases, the V95 values of the adapted plans can achieve 97% for either physical or biological plans. V107 is always 0 indicating no overdosage, and target dose homogeneity is above 0.98 in all cases. The dose received by the organs at risk is comparable to the optimized plans. The plan adaptation method has the potential for on-line adaptation to deal with inter-fractional motion, as well as fast off-line treatment planning, with either the prescribed physical dose or the RBE-weighted dose. (paper)

Optimization of the production plan and risk control in Third Qinshan Nuclear Power Co.,Ltd

International Nuclear Information System (INIS)

Zhou Jun

2009-01-01

Some optimized and improved measures have been taken in Third Qinshan Nuclear Power Co., Ltd. (TQNPC) to optimize the routine production plan management, strengthen the maintenance work risk analysis, and improve the plan execution capability. Which involve unified management of generation, refueling, periodic test and maintenance plans; simplifying the defect scale and reducing the intermediate link of defect treatment; intensifying the appraisal on plan execution and adopting star performance evaluation and merit rating measures. In this paper, the above-mentioned improvement and optimization are introduced comprehensively and systematically. (authors)

Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

Science.gov (United States)

Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

2016-01-01

In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

Parametric Optimal Design of a Parallel Schönflies-Motion Robot under Pick-And-Place Trajectory Constraints

DEFF Research Database (Denmark)

Wu, Guanglei; Bai, Shaoping; Hjørnet, Preben

2015-01-01

This paper deals with the parametric optimum design of a parallel Schoenflies-motion robot, named "Ragnar", designed for fast and flexible pick-and-place applications. The robot architecture admits a rectangular workspace, which can utilize the shop-floor space efficiently. In this work......, the parametric models of the transmission quality, elasto-statics and dynamics are established. By taking into consideration of design requirements and pick-and-place trajectory, a comprehensive multi-objective optimization problem is formulated to optimize both kinematic and dynamic performances. The Pareto......-front is obtained, which provides optimal solutions to the robot design. Robot prototyping work based on the optimal results is described....

Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm

DEFF Research Database (Denmark)

Awasthi, Abhishek; Venkitusamy, Karthikeyan; Padmanaban, Sanjeevikumar

2017-01-01

India's ever increasing population has made it necessary to develop alternative modes of transportation with electric vehicles being the most preferred option. The major obstacle is the deteriorating impact on the utility distribution system brought about by improper setup of these charging...... stations. This paper deals with the optimal planning (siting and sizing) of charging station infrastructure in the city of Allahabad, India. This city is one of the upcoming smart cities, where electric vehicle transportation pilot project is going on under Government of India initiative. In this context......, a hybrid algorithm based on genetic algorithm and improved version of conventional particle swarm optimization is utilized for finding optimal placement of charging station in the Allahabad distribution system. The particle swarm optimization algorithm re-optimizes the received sub-optimal solution (site...

Optimal Airport Surface Traffic Planning Using Mixed-Integer Linear Programming

Directory of Open Access Journals (Sweden)

P. C. Roling

2008-01-01

Full Text Available We describe an ongoing research effort pertaining to the development of a surface traffic automation system that will help controllers to better coordinate surface traffic movements related to arrival and departure traffic. More specifically, we describe the concept for a taxi-planning support tool that aims to optimize the routing and scheduling of airport surface traffic in such a way as to deconflict the taxi plans while optimizing delay, total taxi-time, or some other airport efficiency metric. Certain input parameters related to resource demand, such as the expected landing times and the expected pushback times, are rather difficult to predict accurately. Due to uncertainty in the input data driving the taxi-planning process, the taxi-planning tool is designed such that it produces solutions that are robust to uncertainty. The taxi-planning concept presented herein, which is based on mixed-integer linear programming, is designed such that it is able to adapt to perturbations in these input conditions, as well as to account for failure in the actual execution of surface trajectories. The capabilities of the tool are illustrated in a simple hypothetical airport.

Design Optimization of a Magnetically Levitated Electromagnetic Vibration Energy Harvester for Body Motion

Science.gov (United States)

Pancharoen, K.; Zhu, D.; Beeby, S. P.

2016-11-01

This paper presents a magnetically levitated electromagnetic vibration energy harvester based on magnet arrays. It has a nonlinear response that extends the operating bandwidth and enhances the power output of the harvesting device. The harvester is designed to be embedded in a hip prosthesis and harvest energy from low frequency movements (< 5 Hz) associated with human motion. The design optimization is performed using Comsol simulation considering the constraints on size of the harvester and low operating frequency. The output voltage across the optimal load 3.5kΩ generated from hip movement is 0.137 Volts during walking and 0.38 Volts during running. The power output harvested from hip movement during walking and running is 5.35 μW and 41.36 μW respectively..

Walking on a moving surface: energy-optimal walking motions on a shaky bridge and a shaking treadmill can reduce energy costs below normal.

Science.gov (United States)

Joshi, Varun; Srinivasan, Manoj

2015-02-08

Understanding how humans walk on a surface that can move might provide insights into, for instance, whether walking humans prioritize energy use or stability. Here, motivated by the famous human-driven oscillations observed in the London Millennium Bridge, we introduce a minimal mathematical model of a biped, walking on a platform (bridge or treadmill) capable of lateral movement. This biped model consists of a point-mass upper body with legs that can exert force and perform mechanical work on the upper body. Using numerical optimization, we obtain energy-optimal walking motions for this biped, deriving the periodic body and platform motions that minimize a simple metabolic energy cost. When the platform has an externally imposed sinusoidal displacement of appropriate frequency and amplitude, we predict that body motion entrained to platform motion consumes less energy than walking on a fixed surface. When the platform has finite inertia, a mass- spring-damper with similar parameters to the Millennium Bridge, we show that the optimal biped walking motion sustains a large lateral platform oscillation when sufficiently many people walk on the bridge. Here, the biped model reduces walking metabolic cost by storing and recovering energy from the platform, demonstrating energy benefits for two features observed for walking on the Millennium Bridge: crowd synchrony and large lateral oscillations.

iCycle: Integrated, multicriterial beam angle, and profile optimization for generation of coplanar and noncoplanar IMRT plans

International Nuclear Information System (INIS)

Breedveld, Sebastiaan; Storchi, Pascal R. M.; Voet, Peter W. J.; Heijmen, Ben J. M.

2012-01-01

Purpose: To introduce iCycle, a novel algorithm for integrated, multicriterial optimization of beam angles, and intensity modulated radiotherapy (IMRT) profiles. Methods: A multicriterial plan optimization with iCycle is based on a prescription called wish-list, containing hard constraints and objectives with ascribed priorities. Priorities are ordinal parameters used for relative importance ranking of the objectives. The higher an objective priority is, the higher the probability that the corresponding objective will be met. Beam directions are selected from an input set of candidate directions. Input sets can be restricted, e.g., to allow only generation of coplanar plans, or to avoid collisions between patient/couch and the gantry in a noncoplanar setup. Obtaining clinically feasible calculation times was an important design criterium for development of iCycle. This could be realized by sequentially adding beams to the treatment plan in an iterative procedure. Each iteration loop starts with selection of the optimal direction to be added. Then, a Pareto-optimal IMRT plan is generated for the (fixed) beam setup that includes all so far selected directions, using a previously published algorithm for multicriterial optimization of fluence profiles for a fixed beam arrangement Breedveld et al.[Phys. Med. Biol. 54, 7199-7209 (2009)]. To select the next direction, each not yet selected candidate direction is temporarily added to the plan and an optimization problem, derived from the Lagrangian obtained from the just performed optimization for establishing the Pareto-optimal plan, is solved. For each patient, a single one-beam, two-beam, three-beam, etc. Pareto-optimal plan is generated until addition of beams does no longer result in significant plan quality improvement. Plan generation with iCycle is fully automated. Results: Performance and characteristics of iCycle are demonstrated by generating plans for a maxillary sinus case, a cervical cancer patient, and a

Multiobjective optimization with a modified simulated annealing algorithm for external beam radiotherapy treatment planning

International Nuclear Information System (INIS)

Aubry, Jean-Francois; Beaulieu, Frederic; Sevigny, Caroline; Beaulieu, Luc; Tremblay, Daniel

2006-01-01

Inverse planning in external beam radiotherapy often requires a scalar objective function that incorporates importance factors to mimic the planner's preferences between conflicting objectives. Defining those importance factors is not straightforward, and frequently leads to an iterative process in which the importance factors become variables of the optimization problem. In order to avoid this drawback of inverse planning, optimization using algorithms more suited to multiobjective optimization, such as evolutionary algorithms, has been suggested. However, much inverse planning software, including one based on simulated annealing developed at our institution, does not include multiobjective-oriented algorithms. This work investigates the performance of a modified simulated annealing algorithm used to drive aperture-based intensity-modulated radiotherapy inverse planning software in a multiobjective optimization framework. For a few test cases involving gastric cancer patients, the use of this new algorithm leads to an increase in optimization speed of a little more than a factor of 2 over a conventional simulated annealing algorithm, while giving a close approximation of the solutions produced by a standard simulated annealing. A simple graphical user interface designed to facilitate the decision-making process that follows an optimization is also presented

Integrated production-distribution planning optimization models: A review in collaborative networks context

Directory of Open Access Journals (Sweden)

Beatriz Andres

2017-01-01

Full Text Available Researchers in the area of collaborative networks are more and more aware of proposing collaborative approaches to address planning processes, due to the advantages associated when enterprises perform integrated planning models. Collaborative production-distribution planning, among the supply network actors, is considered a proper mechanism to support enterprises on dealing with uncertainties and dynamicity associated to the current markets. Enterprises, and especially SMEs, should be able to overcome the continuous changes of the market by increasing their agility. Carrying out collaborative planning allows enterprises to enhance their readiness and agility for facing the market turbulences. However, SMEs have limited access when incorporating optimization tools to deal with collaborative planning, reducing their ability to respond to the competition. The problem to solve is to provide SMEs affordable solutions to support collaborative planning. In this regard, new optimisation algorithms are required in order to improve the collaboration within the supply network partners. As part of the H2020 Cloud Collaborative Manufacturing Networks (C2NET research project, this paper presents a study on integrated production and distribution plans. The main objective of the research is to identify gaps in current optimization models, proposed to address integrated planning, taking into account the requirements and needs of the industry. Thus, the needs of the companies belonging to the industrial pilots, defined in the C2NET project, are identified; analysing how these needs are covered by the optimization models proposed in the literature, to deal with the integrated production-distribution planning.

Bio-Inspired Optimal Control Framework to Generate Walking Motions for the Humanoid Robot iCub Using Whole Body Models

Directory of Open Access Journals (Sweden)

Yue Hu

2018-02-01

Full Text Available Bipedal locomotion remains one of the major open challenges of humanoid robotics. The common approaches are based on simple reduced model dynamics to generate walking trajectories, often neglecting the whole-body dynamics of the robots. As motions in nature are often considered as optimal with respect to certain criteria, in this work, we present an optimal control-based approach that allows us to generate optimized walking motions using a precise whole-body dynamic model of the robot, in contrast with the common approaches. The optimal control problem is formulated to minimize a set of desired objective functions with respect to physical constraints of the robot and contact constraints of the walking phases; the problem is then solved with a direct multiple shooting method. We apply the formulation with combinations of different objective criteria to the model of a reduced version of the iCub humanoid robot of 15 internal DOF. The obtained trajectories are executed on the real robot, and we carry out a discussion on the differences between the outcomes of this approach with the classic approaches.

Combing VFH with bezier for motion planning of an autonomous vehicle

Science.gov (United States)

Ye, Feng; Yang, Jing; Ma, Chao; Rong, Haijun

2017-08-01

Vector Field Histogram (VFH) is a method for mobile robot obstacle avoidance. However, due to the nonholonomic constraints of the vehicle, the algorithm is seldom applied to autonomous vehicles. Especially when we expect the vehicle to reach target location in a certain direction, the algorithm is often unsatisfactory. Fortunately, the Bezier Curve is defined by the states of the starting point and the target point. We can use this feature to make the vehicle in the expected direction. Therefore, we propose an algorithm to combine the Bezier Curve with the VFH algorithm, to search for the collision-free states with the VFH search method, and to select the optimal trajectory point with the Bezier Curve as the reference line. This means that we will improve the cost function in the VFH algorithm by comparing the distance between candidate directions and reference line. Finally, select the closest direction to the reference line to be the optimal motion direction.

AI-guided parameter optimization in inverse treatment planning

International Nuclear Information System (INIS)

Yan Hui; Yin Fangfang; Guan Huaiqun; Kim, Jae Ho

2003-01-01

An artificial intelligence (AI)-guided inverse planning system was developed to optimize the combination of parameters in the objective function for intensity-modulated radiation therapy (IMRT). In this system, the empirical knowledge of inverse planning was formulated with fuzzy if-then rules, which then guide the parameter modification based on the on-line calculated dose. Three kinds of parameters (weighting factor, dose specification, and dose prescription) were automatically modified using the fuzzy inference system (FIS). The performance of the AI-guided inverse planning system (AIGIPS) was examined using the simulated and clinical examples. Preliminary results indicate that the expected dose distribution was automatically achieved using the AI-guided inverse planning system, with the complicated compromising between different parameters accomplished by the fuzzy inference technique. The AIGIPS provides a highly promising method to replace the current trial-and-error approach

Guaranteed epsilon-optimal treatment plans with the minimum number of beams for stereotactic body radiation therapy

International Nuclear Information System (INIS)

Yarmand, Hamed; Winey, Brian; Craft, David

2013-01-01

Stereotactic body radiation therapy (SBRT) is characterized by delivering a high amount of dose in a short period of time. In SBRT the dose is delivered using open fields (e.g., beam’s-eye-view) known as ‘apertures’. Mathematical methods can be used for optimizing treatment planning for delivery of sufficient dose to the cancerous cells while keeping the dose to surrounding organs at risk (OARs) minimal. Two important elements of a treatment plan are quality and delivery time. Quality of a plan is measured based on the target coverage and dose to OARs. Delivery time heavily depends on the number of beams used in the plan as the setup times for different beam directions constitute a large portion of the delivery time. Therefore the ideal plan, in which all potential beams can be used, will be associated with a long impractical delivery time. We use the dose to OARs in the ideal plan to find the plan with the minimum number of beams which is guaranteed to be epsilon-optimal (i.e., a predetermined maximum deviation from the ideal plan is guaranteed). Since the treatment plan optimization is inherently a multi-criteria-optimization problem, the planner can navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus OARs sparing, and then use the proposed technique to reduce the number of beams while guaranteeing epsilon-optimality. We use mixed integer programming (MIP) for optimization. To reduce the computation time for the resultant MIP, we use two heuristics: a beam elimination scheme and a family of heuristic cuts, known as ‘neighbor cuts’, based on the concept of ‘adjacent beams’. We show the effectiveness of the proposed technique on two clinical cases, a liver and a lung case. Based on our technique we propose an algorithm for fast generation of epsilon-optimal plans. (paper)

New human-centered linear and nonlinear motion cueing algorithms for control of simulator motion systems

Science.gov (United States)

Telban, Robert J.

While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. To address this, new human-centered motion cueing algorithms were developed. A revised "optimal algorithm" uses time-invariant filters developed by optimal control, incorporating human vestibular system models. The "nonlinear algorithm" is a novel approach that is also formulated by optimal control, but can also be updated in real time. It incorporates a new integrated visual-vestibular perception model that includes both visual and vestibular sensation and the interaction between the stimuli. A time-varying control law requires the matrix Riccati equation to be solved in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. As a result of unsatisfactory sensation, an augmented turbulence cue was added to the vertical mode for both the optimal and nonlinear algorithms. The relative effectiveness of the algorithms, in simulating aircraft maneuvers, was assessed with an eleven-subject piloted performance test conducted on the NASA Langley Visual Motion Simulator (VMS). Two methods, the quasi-objective NASA Task Load Index (TLX), and power spectral density analysis of pilot control, were used to assess pilot workload. TLX analysis reveals, in most cases, less workload and variation among pilots with the nonlinear algorithm. Control input

SVC Planning in Large–scale Power Systems via a Hybrid Optimization Method

DEFF Research Database (Denmark)

Yang, Guang ya; Majumder, Rajat; Xu, Zhao

2009-01-01

The research on allocation of FACTS devices has attracted quite a lot interests from various aspects. In this paper, a hybrid model is proposed to optimise the number, location as well as the parameter settings of static Var compensator (SVC) deployed in large–scale power systems. The model...... utilises the result of vulnerability assessment for determining the candidate locations. A hybrid optimisation method including two stages is proposed to find out the optimal solution of SVC in large– scale planning problem. In the first stage, a conventional genetic algorithm (GA) is exploited to generate...... a candidate solution pool. Then in the second stage, the candidates are presented to a linear planning model to investigate the system optimal loadability, hence the optimal solution for SVC planning can be achieved. The method is presented to IEEE 300–bus system....

Leveraging respiratory organ motion for non-invasive tumor treatment devices: a feasibility study

Science.gov (United States)

Möri, Nadia; Jud, Christoph; Salomir, Rares; Cattin, Philippe C.

2016-06-01

In noninvasive abdominal tumor treatment, research has focused on minimizing organ motion either by gating, breath holding or tracking of the target. The paradigm shift proposed in this study takes advantage of the respiratory organ motion to passively scan the tumor. In the proposed self-scanning method, the focal point of the HIFU device is held fixed for a given time, while it passively scans the tumor due to breathing motion. The aim of this paper is to present a treatment planning method for such a system and show by simulation its feasibility. The presented planning method minimizes treatment time and ensures complete tumor ablation under free-breathing. We simulated our method on realistic motion patterns from a patient specific statistical respiratory model. With our method, we achieved a shorter treatment time than with the gold-standard motion-compensation approach. The main advantage of the proposed method is that electrically steering of the focal spot is no longer needed. As a consequence, it is much easier to find an optimal solution for both avoiding near field heating and covering the whole tumor. However, the reduced complexity on the beam forming comes at the price of an increased complexity on the planning side as well as a reduced efficiency in the energy distribution. Although we simulate the approach on HIFU, the idea of self-scanning passes over to other tumor treatment modalities such as proton therapy or classical radiation therapy.

Relationship of Imaging Frequency and Planning Margin to Account for Intrafraction Prostate Motion: Analysis Based on Real-Time Monitoring Data

International Nuclear Information System (INIS)

Curtis, William; Khan, Mohammad; Magnelli, Anthony; Stephans, Kevin; Tendulkar, Rahul; Xia, Ping

2013-01-01

Purpose: Correction for intrafraction prostate motion becomes important for hypofraction treatment of prostate cancer. The purpose of this study was to estimate an ideal planning margin to account for intrafraction prostate motion as a function of imaging and repositioning frequency in the absence of continuous prostate motion monitoring. Methods and Materials: For 31 patients receiving intensity modulated radiation therapy treatment, prostate positions sampled at 10 Hz during treatment using the Calypso system were analyzed. Using these data, we simulated multiple, less frequent imaging protocols, including intervals of every 10, 15, 20, 30, 45, 60, 90, 120, 180, and 240 seconds. For each imaging protocol, the prostate displacement at the imaging time was corrected by subtracting prostate shifts from the subsequent displacements in that fraction. Furthermore, we conducted a principal component analysis to quantify the direction of prostate motion. Results: Averaging histograms of every 240 and 60 seconds for all patients, vector displacements of the prostate were, respectively, within 3 and 2 mm for 95% of the treatment time. A vector margin of 1 mm achieved 91.2% coverage of the prostate with 30 second imaging. The principal component analysis for all fractions showed the largest variance in prostate position in the midsagittal plane at 54° from the anterior direction, indicating that anterosuperior to inferoposterior is the direction of greatest motion. The smallest prostate motion is in the left-right direction. Conclusions: The magnitudes of intrafraction prostate motion along the superior-inferior and anterior-posterior directions are comparable, and the smallest motion is in the left-right direction. In the absence of continuous prostate motion monitoring, and under ideal circumstances, 1-, 2-, and 3-mm vector planning margins require a respective imaging frequency of every 15, 60, and 240 to account for intrafraction prostate motion while achieving

Applied Railway Optimization in Production Planning at DSB-S-tog - Tasks, Tools and Challenges

DEFF Research Database (Denmark)

Clausen, Jens

2007-01-01

these conflicting goals. S-tog has therefore on the strategic level decided to use software with optimization capabilities in the planning processes. We describe the current status for each activity using optimization or simulation as a tool: Timetable evaluation, rolling stock planning, and crew scheduling...... to the customers, and has concurrently been met with demands for higher efficiency in the daily operation. The plans of timetable, rolling stock and crew must hence allow for a high level of customer service, be efficient, and be robust against disturbances of operations. It is a highly non-trivial task to meet....... In addition we describe on-going efforts in using mathematical models in activities such as timetable design and work-force planning. We also identify some organizatorial key factors, which have paved the way for extended use of optimization methods in railway production planning....

Simulation-optimization model for production planning in the blood supply chain.

Science.gov (United States)

Osorio, Andres F; Brailsford, Sally C; Smith, Honora K; Forero-Matiz, Sonia P; Camacho-Rodríguez, Bernardo A

2017-12-01

Production planning in the blood supply chain is a challenging task. Many complex factors such as uncertain supply and demand, blood group proportions, shelf life constraints and different collection and production methods have to be taken into account, and thus advanced methodologies are required for decision making. This paper presents an integrated simulation-optimization model to support both strategic and operational decisions in production planning. Discrete-event simulation is used to represent the flows through the supply chain, incorporating collection, production, storing and distribution. On the other hand, an integer linear optimization model running over a rolling planning horizon is used to support daily decisions, such as the required number of donors, collection methods and production planning. This approach is evaluated using real data from a blood center in Colombia. The results show that, using the proposed model, key indicators such as shortages, outdated units, donors required and cost are improved.

Robust optimization methods for cardiac sparing in tangential breast IMRT

Energy Technology Data Exchange (ETDEWEB)

Mahmoudzadeh, Houra, E-mail: houra@mie.utoronto.ca [Mechanical and Industrial Engineering Department, University of Toronto, Toronto, Ontario M5S 3G8 (Canada); Lee, Jenny [Radiation Medicine Program, UHN Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9 (Canada); Chan, Timothy C. Y. [Mechanical and Industrial Engineering Department, University of Toronto, Toronto, Ontario M5S 3G8, Canada and Techna Institute for the Advancement of Technology for Health, Toronto, Ontario M5G 1P5 (Canada); Purdie, Thomas G. [Radiation Medicine Program, UHN Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9 (Canada); Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3S2 (Canada); Techna Institute for the Advancement of Technology for Health, Toronto, Ontario M5G 1P5 (Canada)

2015-05-15

Purpose: In left-sided tangential breast intensity modulated radiation therapy (IMRT), the heart may enter the radiation field and receive excessive radiation while the patient is breathing. The patient’s breathing pattern is often irregular and unpredictable. We verify the clinical applicability of a heart-sparing robust optimization approach for breast IMRT. We compare robust optimized plans with clinical plans at free-breathing and clinical plans at deep inspiration breath-hold (DIBH) using active breathing control (ABC). Methods: Eight patients were included in the study with each patient simulated using 4D-CT. The 4D-CT image acquisition generated ten breathing phase datasets. An average scan was constructed using all the phase datasets. Two of the eight patients were also imaged at breath-hold using ABC. The 4D-CT datasets were used to calculate the accumulated dose for robust optimized and clinical plans based on deformable registration. We generated a set of simulated breathing probability mass functions, which represent the fraction of time patients spend in different breathing phases. The robust optimization method was applied to each patient using a set of dose-influence matrices extracted from the 4D-CT data and a model of the breathing motion uncertainty. The goal of the optimization models was to minimize the dose to the heart while ensuring dose constraints on the target were achieved under breathing motion uncertainty. Results: Robust optimized plans were improved or equivalent to the clinical plans in terms of heart sparing for all patients studied. The robust method reduced the accumulated heart dose (D10cc) by up to 801 cGy compared to the clinical method while also improving the coverage of the accumulated whole breast target volume. On average, the robust method reduced the heart dose (D10cc) by 364 cGy and improved the optBreast dose (D99%) by 477 cGy. In addition, the robust method had smaller deviations from the planned dose to the

Rectal Balloon for the Immobilization of the Prostate Internal Motion

International Nuclear Information System (INIS)

Lee, Sang Kyu; Beak, Jong Geal; Kim, Joo Ho; Jeon, Byong Chul; Cho, Jeong Hee; Kim, Dong Wook; Song, Tae Soo; Cho, Jae Ho; Na, Soo Kyong

2005-01-01

The using of endo-rectal balloon has proposed as optimal method that minimized the motion of prostate and the dose of rectum wall volume for treated prostate cancer patients, so we make the customized rectal balloon device. In this study, we analyzed the efficiency of the Self-customized rectal balloon in the aspects of its reproducibility. In 5 patients, for treatment planning, each patient was acquired CT slice images in state of with and without rectal balloon. Also they had CT scanning same repeated third times in during radiation treatment (IMRT). In each case, we analyzed the deviation of rectal balloon position and verified the isodose distribution of rectum wall at closed prostate. Using the rectal balloon, we minimized the planning target volume (PTV) by decreased the internal motion of prostate and overcome the dose limit of radiation therapy in prostate cancer by increased the gap between the rectum wall and high dose region. The using of rectal balloon, although, was reluctant to treat by patients. View a point of immobilization of prostate internal motion and dose escalation of GTV (gross tumor volume), its using consider large efficient for treated prostate cancer patients.

Development of a fast optimization preview in radiation treatment planning

International Nuclear Information System (INIS)

Hoeffner, J.; Decker, P.; Schmidt, E.L.; Herbig, W.; Rittler, J.; Weiss, P.

1996-01-01

Usually, the speed of convergence of some iterative algorithms is restricted to a bounded relaxation parameter. Exploiting the special altering behavior of the weighting factors at each step, many iteration steps are avoided by overrelaxing this relaxation parameter. Therefore, the relaxation parameter is increased as long as the optimization result is improved. This can be performed without loss of accuracy. Our optimization technique is demonstrated by the case of a right lung carcinoma. The solution space for this case is 36 isocentric X-ray beams evenly spaced at 10 . Each beam is restricted to 23 MV X-ray fields with a planning target volume matched by irregular field shapes, similar to that produced by a multileaf collimator. Four organs at risk plus the planning target volume are considered in the optimization process. The convergence behavior of the optimization algorithm is shown by overrelaxing the relaxation parameter in comparison to conventional relaxation parameter control. The new approach offers the ability to get a fast preview of the expected final result. If the clinician is in agreement with the preview, the algorithm is continued and achieves the result proven by the Cimmino optimization algorithm. In the other case, if the clinician doesn't agree with the preview, he will be able to change the optimization parameters (e.g. field entry points) and to restart the algorithm. (orig./MG) [de

Multi-Objective Motion Control Optimization for the Bridge Crane System

Directory of Open Access Journals (Sweden)

Renxin Xiao

2018-03-01

Full Text Available A novel control algorithm combining the linear quadratic regulator (LQR control and trajectory planning (TP is proposed for the control of an underactuated crane system, targeting position adjustment and swing suppression. The TP is employed to control the swing angle within certain constraints, and the LQR is applied to achieve anti-disturbance. In order to improve the accuracy of the position control, a differential-integral control loop is applied. The weighted LQR matrices representing priorities of the state variables for the bridge crane motion are searched by the multi-objective genetic algorithm (MOGA. The stability proof is provided in order to validate the effectiveness of the proposed algorithm. Numerous simulation and experimental validations justify the feasibility of the proposed method.

94: Treatment plan optimization for conformal therapy

International Nuclear Information System (INIS)

Rosen, I.I.; Lane, R.G.

1987-01-01

Computer-controlled conformal radiation therapy techniques can deliver complex treatments utilizing large numbers of beams, gantry angles and beam shapes. Linear programming is well-suited for planning conformal treatments. Given a list of available treatment beams, linear programming calculates the relative weights of the beams such that the objective function is optimized and doses to constraint points are within the prescribed limits. 5 refs.; 3 figs

Optimization-based decision support systems for planning problems in processing industries

NARCIS (Netherlands)

Claassen, G.D.H.

2014-01-01

Summary

Optimization-based decision support systems for planning problems in processing industries

Nowadays, efficient planning of material flows within and between supply chains is of vital importance and has become one of the most challenging problems for decision support in

Using Motion Planning to Determine the Existence of an Accessible Route in a CAD Environment

Science.gov (United States)

Pan, Xiaoshan; Han, Charles S.; Law, Kincho H.

2010-01-01

We describe an algorithm based on motion-planning techniques to determine the existence of an accessible route through a facility for a wheeled mobility device. The algorithm is based on LaValle's work on rapidly exploring random trees and is enhanced to take into consideration the particularities of the accessible route domain. Specifically, the…

Automated Planning of Tangential Breast Intensity-Modulated Radiotherapy Using Heuristic Optimization

International Nuclear Information System (INIS)

Purdie, Thomas G.; Dinniwell, Robert E.; Letourneau, Daniel; Hill, Christine; Sharpe, Michael B.

2011-01-01

Purpose: To present an automated technique for two-field tangential breast intensity-modulated radiotherapy (IMRT) treatment planning. Method and Materials: A total of 158 planned patients with Stage 0, I, and II breast cancer treated using whole-breast IMRT were retrospectively replanned using automated treatment planning tools. The tools developed are integrated into the existing clinical treatment planning system (Pinnacle 3 ) and are designed to perform the manual volume delineation, beam placement, and IMRT treatment planning steps carried out by the treatment planning radiation therapist. The automated algorithm, using only the radio-opaque markers placed at CT simulation as inputs, optimizes the tangential beam parameters to geometrically minimize the amount of lung and heart treated while covering the whole-breast volume. The IMRT parameters are optimized according to the automatically delineated whole-breast volume. Results: The mean time to generate a complete treatment plan was 6 min, 50 s ± 1 min 12 s. For the automated plans, 157 of 158 plans (99%) were deemed clinically acceptable, and 138 of 158 plans (87%) were deemed clinically improved or equal to the corresponding clinical plan when reviewed in a randomized, double-blinded study by one experienced breast radiation oncologist. In addition, overall the automated plans were dosimetrically equivalent to the clinical plans when scored for target coverage and lung and heart doses. Conclusion: We have developed robust and efficient automated tools for fully inversed planned tangential breast IMRT planning that can be readily integrated into clinical practice. The tools produce clinically acceptable plans using only the common anatomic landmarks from the CT simulation process as an input. We anticipate the tools will improve patient access to high-quality IMRT treatment by simplifying the planning process and will reduce the effort and cost of incorporating more advanced planning into clinical practice.

An interactive beam-weight optimization tool for three-dimensional radiotherapy treatment planning

International Nuclear Information System (INIS)

Burba, S.; Gardey, K.; Nadobny, J.; Stalling, D.; Seebass, M.; Beier, J.; Wust, P.; Budach, V.; Felix, R.

1997-01-01

Purpose: A computer software tool has been developed to aid the treatment planner in selecting beam weights for three-dimensional radiotherapy treatment planning. An approach to plan optimization has been made that is based on the use of an iterative feasibility search algorithm combined with a quadratic convergence method that seeks a set of beam weights which satisfies all the dose constraints set by the planner. Materials and Methods: A FORTRAN module for dose calculation for radiotherapy (a VOXELPLAN modification) has been integrated into an object-oriented Silicon Graphics TM platform in an IRIS Inventor environment on basis of the OpenGL which up to now has been exclusively used for the calculation of E-field distributions in hyperthermia (HyperPlan TM ). After the successful calculation and representation of the dose distribution in the Silicon Graphics TM platform, an algorithm involving the minimization method according to the principle of quadratic convergence was developed for optimizing beam weights of a number of pre-calculated fields. The verification of the algorithms for dose calculation and dose optimization has been realized by use of a standardized interface to the program VIRTUOS as well as by the collapsed cone algorithm implemented in the commercial treatment planning system Helax TMS TM . Results: The search algorithm allows the planner to incorporate relative importance weightings to target volumes and anatomical structures, specifying, for example, that a dose constraint to the spinal cord is much more crucial to the overall evaluation of a treatment plan than a dose constraint to otherwise uninvolved soft tissue. In most cases the applied minimization method according to the model of Davidon-Fletcher-Powell showed ultimate fast convergence for a general function f(x) with continuous second derivatives and fast convergence for a positive definite quadratic function. In other cases, however, the absence of an acceptable solution may indicate

WiMax network planning and optimization

CERN Document Server

Zhang, Yan

2009-01-01

This book offers a comprehensive explanation on how to dimension, plan, and optimize WiMAX networks. The first part of the text introduces WiMAX networks architecture, physical layer, standard, protocols, security mechanisms, and highly related radio access technologies. It covers system framework, topology, capacity, mobility management, handoff management, congestion control, medium access control (MAC), scheduling, Quality of Service (QoS), and WiMAX mesh networks and security. Enabling easy understanding of key concepts and technologies, the second part presents practical examples and illu

Study on optimization of normal plant outage work plan for nuclear power plants

International Nuclear Information System (INIS)

Aoki, Takayuki; Kodama, Noriko; Takase, Kentaro; Miya, Kenzo

2011-01-01

This paper discusses maintenance optimization in maintenance implementation stage following maintenance planning stage in nuclear power plants and proposes a methodology to get an optimum maintenance work plan. As a result of consideration, the followings were obtained. (1) The quantitative evaluation methodology for optimizing maintenance work plan in nuclear power plants was developed. (2) Utilizing the above methodology, a simulation analysis of maintenance work planning for BWR's PLR and RHR systems in a normal plant outage was performed. Maintenance cost calculation in several cases was carried out on the condition of smoothening man loading over the plant outage schedule as much as possible. (3) As a result of the simulation, the economical work plans having a flat man loading over the plant outage schedule were obtained. (author)

Commercial Aircraft Trajectory Planning based on Multiphase Mixed-Integer Optimal Control

OpenAIRE

Soler Arnedo, Manuel Fernando

2017-01-01

The main goal of this dissertation is to develop optimal control techniques for aircraft trajectory planning looking at reduction of fuel consumption, emissions and overfly charges in flight plans. The calculation of a flight plan involves the consideration of multiple factors. They can be classified as either continuous or discrete, and include nonlinear aircraft performance, atmospheric conditions, wind conditions, airspace structure, amount of departure fuel, and operational...

Optimized LTE Cell Planning with Varying Spatial and Temporal User Densities

KAUST Repository

Ghazzai, Hakim; Yaacoub, Elias; Alouini, Mohamed-Slim; Dawy, Zaher; Abu Dayya, Adnan

2015-01-01

Base station deployment in cellular networks is one of the fundamental problems in network design. This paper proposes a novel method for the cell planning problem for the fourth generation (4G) cellular networks using meta-heuristic algorithms. In this approach, we aim to satisfy both cell coverage and capacity constraints simultaneously by formulating an optimization problem that captures practical planning aspects. The starting point of the planning process is defined through a dimensioning exercise that captures both coverage and capacity constraints. Afterwards, we implement a meta-heuristic algorithm based on swarm intelligence (e.g., particle swarm optimization or the recently-proposed grey wolf optimizer) to find suboptimal base station locations that satisfy both problem constraints in the area of interest which can be divided into several subareas with different spatial user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant base stations. We also perform Monte Carlo simulations to study the performance of the proposed scheme and compute the average number of users in outage. Next, the problems of green planning with regards to temporal traffic variation and planning with location constraints due to tight limits on electromagnetic radiations are addressed, using the proposed method. Finally, in our simulation results, we apply our proposed approach for different scenarios with different subareas and user distributions and show that the desired network quality of service targets are always reached even for large-scale problems.

Optimized LTE Cell Planning with Varying Spatial and Temporal User Densities

KAUST Repository

Ghazzai, Hakim

2015-03-09

Base station deployment in cellular networks is one of the fundamental problems in network design. This paper proposes a novel method for the cell planning problem for the fourth generation (4G) cellular networks using meta-heuristic algorithms. In this approach, we aim to satisfy both cell coverage and capacity constraints simultaneously by formulating an optimization problem that captures practical planning aspects. The starting point of the planning process is defined through a dimensioning exercise that captures both coverage and capacity constraints. Afterwards, we implement a meta-heuristic algorithm based on swarm intelligence (e.g., particle swarm optimization or the recently-proposed grey wolf optimizer) to find suboptimal base station locations that satisfy both problem constraints in the area of interest which can be divided into several subareas with different spatial user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant base stations. We also perform Monte Carlo simulations to study the performance of the proposed scheme and compute the average number of users in outage. Next, the problems of green planning with regards to temporal traffic variation and planning with location constraints due to tight limits on electromagnetic radiations are addressed, using the proposed method. Finally, in our simulation results, we apply our proposed approach for different scenarios with different subareas and user distributions and show that the desired network quality of service targets are always reached even for large-scale problems.

Human motion simulation predictive dynamics

CERN Document Server

Abdel-Malek, Karim

2013-01-01

Simulate realistic human motion in a virtual world with an optimization-based approach to motion prediction. With this approach, motion is governed by human performance measures, such as speed and energy, which act as objective functions to be optimized. Constraints on joint torques and angles are imposed quite easily. Predicting motion in this way allows one to use avatars to study how and why humans move the way they do, given specific scenarios. It also enables avatars to react to infinitely many scenarios with substantial autonomy. With this approach it is possible to predict dynamic motion without having to integrate equations of motion -- rather than solving equations of motion, this approach solves for a continuous time-dependent curve characterizing joint variables (also called joint profiles) for every degree of freedom. Introduces rigorous mathematical methods for digital human modelling and simulation Focuses on understanding and representing spatial relationships (3D) of biomechanics Develops an i...

Technical Note: A novel leaf sequencing optimization algorithm which considers previous underdose and overdose events for MLC tracking radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Wisotzky, Eric, E-mail: eric.wisotzky@charite.de, E-mail: eric.wisotzky@ipk.fraunhofer.de; O’Brien, Ricky; Keall, Paul J., E-mail: paul.keall@sydney.edu.au [Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, NSW 2006 (Australia)

2016-01-15

Purpose: Multileaf collimator (MLC) tracking radiotherapy is complex as the beam pattern needs to be modified due to the planned intensity modulation as well as the real-time target motion. The target motion cannot be planned; therefore, the modified beam pattern differs from the original plan and the MLC sequence needs to be recomputed online. Current MLC tracking algorithms use a greedy heuristic in that they optimize for a given time, but ignore past errors. To overcome this problem, the authors have developed and improved an algorithm that minimizes large underdose and overdose regions. Additionally, previous underdose and overdose events are taken into account to avoid regions with high quantity of dose events. Methods: The authors improved the existing MLC motion control algorithm by introducing a cumulative underdose/overdose map. This map represents the actual projection of the planned tumor shape and logs occurring dose events at each specific regions. These events have an impact on the dose cost calculation and reduce recurrence of dose events at each region. The authors studied the improvement of the new temporal optimization algorithm in terms of the L1-norm minimization of the sum of overdose and underdose compared to not accounting for previous dose events. For evaluation, the authors simulated the delivery of 5 conformal and 14 intensity-modulated radiotherapy (IMRT)-plans with 7 3D patient measured tumor motion traces. Results: Simulations with conformal shapes showed an improvement of L1-norm up to 8.5% after 100 MLC modification steps. Experiments showed comparable improvements with the same type of treatment plans. Conclusions: A novel leaf sequencing optimization algorithm which considers previous dose events for MLC tracking radiotherapy has been developed and investigated. Reductions in underdose/overdose are observed for conformal and IMRT delivery.

An optimal control approach to manpower planning problem

Directory of Open Access Journals (Sweden)

H. W. J. Lee

2001-01-01

Full Text Available A manpower planning problem is studied in this paper. The model includes scheduling different types of workers over different tasks, employing and terminating different types of workers, and assigning different types of workers to various trainning programmes. The aim is to find an optimal way to do all these while keeping the time-varying demand for minimum number of workers working on each different tasks satisfied. The problem is posed as an optimal discrete-valued control problem in discrete time. A novel numerical scheme is proposed to solve the problem, and an illustrative example is provided.

Intelligent robot action planning

Energy Technology Data Exchange (ETDEWEB)

Vamos, T; Siegler, A

1982-01-01

Action planning methods used in intelligent robot control are discussed. Planning is accomplished through environment understanding, environment representation, task understanding and planning, motion analysis and man-machine communication. These fields are analysed in detail. The frames of an intelligent motion planning system are presented. Graphic simulation of the robot's environment and motion is used to support the planning. 14 references.

Considering FACTS in Optimal Transmission Expansion Planning

Directory of Open Access Journals (Sweden)

K. Soleimani

2017-10-01

Full Text Available The expansion of power transmission systems is an important part of the expansion of power systems that requires enormous investment costs. Since the construction of new transmission lines is very expensive, it is necessary to choose the most efficient expansion plan that ensures system security with a minimal number of new lines. In this paper, the role of Flexible AC Transmission System (FACTS devices in the effective operation and expansion planning of transmission systems is examined. Effort was taken to implement a method based on sensitivity analysis to select the optimal number and location of FACTS devices, lines and other elements of the transmission system. Using this method, the transmission expansion plan for a 9 and a 39 bus power system was performed with and without the presence of FACTS with the use of DPL environment in Digsilent software 15.1. Results show that the use of these devices reduces the need for new transmission lines and minimizes the investment cost.

Integrating robust timetabling in line plan optimization for railway systems

DEFF Research Database (Denmark)

Burggraeve, Sofie; Bull, Simon Henry; Vansteenwegen, Pieter

2017-01-01

We propose a heuristic algorithm to build a railway line plan from scratch that minimizes passenger travel time and operator cost and for which a feasible and robust timetable exists. A line planning module and a timetabling module work iteratively and interactively. The line planning module......, but is constrained by limited shunt capacity. While the operator and passenger cost remain close to those of the initially and (for these costs) optimally built line plan, the timetable corresponding to the finally developed robust line plan significantly improves the minimum buffer time, and thus the robustness...... creates an initial line plan. The timetabling module evaluates the line plan and identifies a critical line based on minimum buffer times between train pairs. The line planning module proposes a new line plan in which the time length of the critical line is modified in order to provide more flexibility...

Respiration-correlated spiral CT: A method of measuring respiratory-induced anatomic motion for radiation treatment planning

International Nuclear Information System (INIS)

Ford, E.C.; Mageras, G.S.; Yorke, E.; Ling, C.C.

2003-01-01

We describe a method for generating CT images at multiple respiratory phases with a single spiral CT scan, referred to as respiratory-correlated spiral CT (RCCT). RCCT relies on a respiration wave form supplied by an external patient monitor. During acquisition this wave form is recorded along with the initiation time of the CT scan, so as to 'time stamp' each reconstructed slice with the phase of the respiratory cycle. By selecting the appropriate slices, a full CT image set is generated at several phases, typically 7-11 per cycle. The CT parameters are chosen to optimize the temporal resolution while minimizing the spatial gap between slices at successive respiratory cycles. Using a pitch of 0.5, a gantry rotation period of 1.5 s, and a 180 degree sign reconstruction algorithm results in ∼5 mm slice spacing at a given phase for typical respiration periods, and a respiratory motion within each slice that is acceptably small, particularly near end expiration or end inspiration where gated radiotherapy is to occur. We have performed validation measurements on a phantom with a moving sphere designed to simulate respiration-induced tumor motion. RCCT scans of the phantom at respiratory periods of 4, 5, and 6 s show good agreement of the sphere's motion with that observed under fluoroscopic imaging. The positional deviations in the sphere's centroid between RCCT and fluoroscopy are 1.1±0.9 mm in the transaxial direction (average over all scans at all phases ±1 s.d.) and 1.2±1.0 mm in the longitudinal direction. Reconstructed volumes match those expected on the basis of stationary-phantom scans to within 5% in all cases. The surface distortions of the reconstructed sphere, as quantified by deviations from a mathematical reference sphere, are similar to those from a stationary phantom scan and are correlated with the speed of the phantom. A RCCT scan of the phantom undergoing irregular motion, demonstrates that successful reconstruction can be achieved even with

Optimizing production and imperfect preventive maintenance planning's integration in failure-prone manufacturing systems

International Nuclear Information System (INIS)

Aghezzaf, El-Houssaine; Khatab, Abdelhakim; Tam, Phuoc Le

2016-01-01

This paper investigates the issue of integrating production and maintenance planning in a failure-prone manufacturing system. It is assumed that the system's operating state is stochastically predictable, in terms of its operating age, and that it can accordingly be preventively maintained during preplanned periods. Preventive maintenance is assumed to be imperfect, that is when performed, it brings the manufacturing system to an operating state that lies between ‘as bad as old’ and ‘as good as new’. Only an overhauling of the system brings it to a ‘as good as new’ operating state again. A practical integrated production and preventive maintenance planning model, that takes into account the system's manufacturing capacity and its operational reliability state, is developed. The model is naturally formulated as a mixed-integer non-linear optimization problem, for which an extended mixed-integer linear reformulation is proposed. This reformulation, while it solves the proposed integrated planning problem to optimality, remains quite demanding in terms of computational time. A fix-and-optimize procedure, that takes advantage of some properties of the original model, is then proposed. The reformulation and the fix-and-optimize procedure are tested on some test instances adapted from those available in the literature. The results show that the proposed fix-and-optimize procedure performs quite well and opens new research direction for future improvements. - Highlights: • Integration of production planning and imperfect preventive maintenance is explored. • Imperfect maintenance is modeled using a fitting age reduction hybrid hazard rate. • A practical approximate optimization model for this integration is proposed. • The resulting naturally MINL optimization model is reformulated and solved as a MILP. • An effective fix-and-optimize procedure is proposed for large instances of this MILP.

Beyond bixels: Generalizing the optimization parameters for intensity modulated radiation therapy

International Nuclear Information System (INIS)

Markman, Jerry; Low, Daniel A.; Beavis, Andrew W.; Deasy, Joseph O.

2002-01-01

Intensity modulated radiation therapy (IMRT) treatment planning systems optimize fluence distributions by subdividing the fluence distribution into rectangular bixels. The algorithms typically optimize the fluence intensity directly, often leading to fluence distributions with sharp discontinuities. These discontinuities may yield difficulties in delivery of the fluence distribution, leading to inaccurate dose delivery. We have developed a method for decoupling the bixel intensities from the optimization parameters; either by introducing optimization control points from which the bixel intensities are interpolated or by parametrizing the fluence distribution using basis functions. In either case, the number of optimization search parameters is reduced from the direct bixel optimization method. To illustrate the concept, the technique is applied to two-dimensional idealized head and neck treatment plans. The interpolation algorithms investigated were nearest-neighbor, linear and cubic spline, and radial basis functions serve as the basis function test. The interpolation and basis function optimization techniques were compared against the direct bixel calculation. The number of optimization parameters were significantly reduced relative to the bixel optimization, and this was evident in the reduction of computation time of as much as 58% from the full bixel optimization. The dose distributions obtained using the reduced optimization parameter sets were very similar to the full bixel optimization when examined by dose distributions, statistics, and dose-volume histograms. To evaluate the sensitivity of the fluence calculations to spatial misalignment caused either by delivery errors or patient motion, the doses were recomputed with a 1 mm shift in each beam and compared to the unshifted distributions. Except for the nearest-neighbor algorithm, the reduced optimization parameter dose distributions were generally less sensitive to spatial shifts than the bixel

Motion patterns and phase-transition of a defender-intruder problem and optimal interception strategy of the defender

Science.gov (United States)

Wang, Jiangliu; Li, Wei

2015-10-01

In this paper, we consider a defense-intrusion interaction, in which an intruder is attracted by a protected stationary target but repulsed by a defender; while the defender tries to move towards an appropriate interception position (IP) between the intruder and the target in order to intercept the intruder and expel the intruder away from the target as maximum as possible. Intuitionally, to keep the intruder further away, one may wonder that: is it a better strategy for the defender trying to approach the intruder as near as possible? Unexpectedly and interestingly enough, this is not always the case. We first introduce the flexibility for IP selection, then investigate the system dynamics and the stable motion patterns, and characterize the phase-transition surface for the motion patterns. We show that, the phase-transition surface just defines the optimal interception strategy of the defender for IP selection; and from the perspective of mobility of agents, the optimal strategy just depends on relative mobility of the two agents.

Open source Modeling and optimization tools for Planning

Energy Technology Data Exchange (ETDEWEB)

Peles, S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-02-10

Open source modeling and optimization tools for planning The existing tools and software used for planning and analysis in California are either expensive, difficult to use, or not generally accessible to a large number of participants. These limitations restrict the availability of participants for larger scale energy and grid studies in the state. The proposed initiative would build upon federal and state investments in open source software, and create and improve open source tools for use in the state planning and analysis activities. Computational analysis and simulation frameworks in development at national labs and universities can be brought forward to complement existing tools. An open source platform would provide a path for novel techniques and strategies to be brought into the larger community and reviewed by a broad set of stakeholders.

Optimal farm plans for sustainable environmental and economic ...

African Journals Online (AJOL)

The optimal farm plans indicated that the cassava/maize intercrop gave the best results in Ijemo-Fadipe and Ajura, while the cassava/melon and sole cassava enterprises were best in Ijale-Papa and Ilewo-Orile respectively. Operating expenses was found to be the most limiting factors in all the villages. The study concluded ...

A complex systems approach to planning, optimization and decision making for energy networks