Robot-Assisted Training Early After Cardiac Surgery.

Science.gov (United States)

Schoenrath, Felix; Markendorf, Susanne; Brauchlin, Andreas E; Seifert, Burkhardt; Wilhelm, Markus J; Czerny, Martin; Riener, Robert; Falk, Volkmar; Schmied, Christian M

2015-07-01

To assess feasibility and safety of a robot-assisted gait therapy with the Lokomat® system in patients early after open heart surgery. Within days after open heart surgery 10 patients were subjected to postoperative Lokomat® training (Intervention group, IG) whereas 20 patients served as controls undergoing standard postoperative physiotherapy (Control group, CG). All patients underwent six-minute walk test and evaluation of the muscular strength of the lower limbs by measuring quadriceps peak force. The primary safety end-point was freedom from any device-related wound healing disturbance. Patients underwent clinical follow-up after one month. Both training methods resulted in an improvement of walking distance (IG [median, interquartile range, p-value]: +119 m, 70-201 m, p = 0.005; CG: 105 m, 57-152.5m, p force (IG left: +5 N, 3.8 7 N, p = 0.005; IG right: +3.5 N, 1.5-8.8 N, p = 0.011; CG left: +5.5 N, 4-9 N, p training were comparable to early postoperative standard in hospital training (median changes in walking distance in percent, p = 0.81; median changes in quadriceps peak force in percent, left: p = 0.97, right p = 0.61). No deep sternal wound infection or any adverse event occurred in the robot-assisted training group. Robot-assisted gait therapy with the Lokomat® system is feasible and safe in patients early after median sternotomy. Results with robot-assisted training were comparable to standard in hospital training. An adapted and combined aerobic and resistance training intervention with augmented feedback may result in benefits in walking distance and lower limb muscle strength (ClinicalTrials.gov number, NCT 02146196). © 2015 Wiley Periodicals, Inc.

An Organic Computing Approach to Self-organising Robot Ensembles

Directory of Open Access Journals (Sweden)

Sebastian Albrecht von Mammen

2016-11-01

Full Text Available Similar to the Autonomous Computing initiative, that has mainly been advancing techniques for self-optimisation focussing on computing systems and infrastructures, Organic Computing (OC has been driving the development of system design concepts and algorithms for self-adaptive systems at large. Examples of application domains include, for instance, traffic management and control, cloud services, communication protocols, and robotic systems. Such an OC system typically consists of a potentially large set of autonomous and self-managed entities, where each entity acts with a local decision horizon. By means of cooperation of the individual entities, the behaviour of the entire ensemble system is derived. In this article, we present our work on how autonomous, adaptive robot ensembles can benefit from OC technology. Our elaborations are aligned with the different layers of an observer/controller framework which provides the foundation for the individuals' adaptivity at system design-level. Relying on an extended Learning Classifier System (XCS in combination with adequate simulation techniques, this basic system design empowers robot individuals to improve their individual and collaborative performances, e.g. by means of adapting to changing goals and conditions.Not only for the sake of generalisability, but also because of its enormous transformative potential, we stage our research in the domain of robot ensembles that are typically comprised of several quad-rotors and that organise themselves to fulfil spatial tasks such as maintenance of building facades or the collaborative search for mobile targets. Our elaborations detail the architectural concept, provide examples of individual self-optimisation as well as of the optimisation of collaborative efforts, and we show how the user can control the ensembles at multiple levels of abstraction. We conclude with a summary of our approach and an outlook on possible future steps.

Training in Robotic Surgery-an Overview.

Science.gov (United States)

Sridhar, Ashwin N; Briggs, Tim P; Kelly, John D; Nathan, Senthil

2017-08-01

There has been a rapid and widespread adoption of the robotic surgical system with a lag in the development of a comprehensive training and credentialing framework. A literature search on robotic surgical training techniques and benchmarks was conducted to provide an evidence-based road map for the development of a robotic surgical skills for the novice robotic surgeon. A structured training curriculum is suggested incorporating evidence-based training techniques and benchmarks for progress. This usually involves sequential progression from observation, case assisting, acquisition of basic robotic skills in the dry and wet lab setting along with achievement of individual and team-based non-technical skills, modular console training under supervision, and finally independent practice. Robotic surgical training must be based on demonstration of proficiency and safety in executing basic robotic skills and procedural tasks prior to independent practice.

[Simulation-based robot-assisted surgical training].

Science.gov (United States)

Kolontarev, K B; Govorov, A V; Rasner, P I; Sheptunov, S A; Prilepskaya, E A; Maltsev, E G; Pushkar, D Yu

2015-12-01

Since the first use of robotic surgical system in 2000, the robot-assisted technology has gained wide popularity throughout the world. Robot-assisted surgical training is a complex issue that requires significant efforts from students and teacher. During the last two decades, simulation-based training had received active development due to wide-spread occurrence and popularization of laparoscopic and robot-assisted surgical techniques. We performed a systematic review to identify the currently available simulators for robot-assisted surgery. We searched the Medline and Pubmed, English sources of literature data, using the following key words and phrases: "robotics", "robotic surgery", "computer assisted surgery", "simulation", "computer simulation", "virtual reality", "surgical training", and "surgical education". There were identified 565 publications, which meet the key words and phrases; 19 publications were selected for the final analysis. It was established that simulation-based training is the most promising teaching tool that can be used in the training of the next generation robotic surgeons. Today the use of simulators to train surgeons is validated. Price of devices is an obvious barrier for inclusion in the program for training of robotic surgeons, but the lack of this tool will result in a sharp increase in the duration of specialists training.

Novel Adaptive Forward Neural MIMO NARX Model for the Identification of Industrial 3-DOF Robot Arm Kinematics

Directory of Open Access Journals (Sweden)

Ho Pham Huy Anh

2012-10-01

Full Text Available In this paper, a novel forward adaptive neural MIMO NARX model is used for modelling and identifying the forward kinematics of an industrial 3-DOF robot arm system. The nonlinear features of the forward kinematics of the industrial robot arm drive are thoroughly modelled based on the forward adaptive neural NARX model-based identification process using experimental input-output training data. This paper proposes a novel use of a back propagation (BP algorithm to generate the forward neural MIMO NARX (FNMN model for the forward kinematics of the industrial 3-DOF robot arm. The results show that the proposed adaptive neural NARX model trained by a Back Propagation learning algorithm yields outstanding performance and perfect accuracy.

Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation.

Science.gov (United States)

Bauer, Robert; Fels, Meike; Royter, Vladislav; Raco, Valerio; Gharabaghi, Alireza

2016-09-01

Considering self-rated mental effort during neurofeedback may improve training of brain self-regulation. Twenty-one healthy, right-handed subjects performed kinesthetic motor imagery of opening their left hand, while threshold-based classification of beta-band desynchronization resulted in proprioceptive robotic feedback. The experiment consisted of two blocks in a cross-over design. The participants rated their perceived mental effort nine times per block. In the adaptive block, the threshold was adjusted on the basis of these ratings whereas adjustments were carried out at random in the other block. Electroencephalography was used to examine the cortical activation patterns during the training sessions. The perceived mental effort was correlated with the difficulty threshold of neurofeedback training. Adaptive threshold-setting reduced mental effort and increased the classification accuracy and positive predictive value. This was paralleled by an inter-hemispheric cortical activation pattern in low frequency bands connecting the right frontal and left parietal areas. Optimal balance of mental effort was achieved at thresholds significantly higher than maximum classification accuracy. Rating of mental effort is a feasible approach for effective threshold-adaptation during neurofeedback training. Closed-loop adaptation of the neurofeedback difficulty level facilitates reinforcement learning of brain self-regulation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Adaptive Synchronization of Robotic Sensor Networks

OpenAIRE

Yıldırım, Kasım Sinan; Gürcan, Önder

2014-01-01

The main focus of recent time synchronization research is developing power-efficient synchronization methods that meet pre-defined accuracy requirements. However, an aspect that has been often overlooked is the high dynamics of the network topology due to the mobility of the nodes. Employing existing flooding-based and peer-to-peer synchronization methods, are networked robots still be able to adapt themselves and self-adjust their logical clocks under mobile network dynamics? In this paper, ...

Towards Using a Generic Robot as Training Partner

DEFF Research Database (Denmark)

Sørensen, Anders Stengaard; Savarimuthu, Thiusius Rajeeth; Nielsen, Jacob

2014-01-01

In this paper, we demonstrate how a generic industrial robot can be used as a training partner, for upper limb training. The motion path and human/robot interaction of a non-generic upper-arm training robot is transferred to a generic industrial robot arm, and we demonstrate that the robot arm can...... implement the same type of interaction, but can expand the training regime to include both upper arm and shoulder training. We compare the generic robot to two affordable but custom-built training robots, and outline interesting directions for future work based on these training robots....

Challenges in adapting imitation and reinforcement learning to compliant robots

Directory of Open Access Journals (Sweden)

Calinon Sylvain

2011-12-01

Full Text Available There is an exponential increase of the range of tasks that robots are forecasted to accomplish. (Reprogramming these robots becomes a critical issue for their commercialization and for their applications to real-world scenarios in which users without expertise in robotics wish to adapt the robot to their needs. This paper addresses the problem of designing userfriendly human-robot interfaces to transfer skills in a fast and efficient manner. This paper presents recent work conducted at the Learning and Interaction group at ADVR-IIT, ranging from skill acquisition through kinesthetic teaching to self-refinement strategies initiated from demonstrations. Our group started to explore the use of imitation and exploration strategies that can take advantage of the compliant capabilities of recent robot hardware and control architectures.

Adaptive heterogeneous multi-robot teams

Energy Technology Data Exchange (ETDEWEB)

Parker, L.E.

1998-11-01

This research addresses the problem of achieving fault tolerant cooperation within small- to medium-sized teams of heterogeneous mobile robots. The author describes a novel behavior-based, fully distributed architecture, called ALLIANCE, that utilizes adaptive action selection to achieve fault tolerant cooperative control in robot missions involving loosely coupled, largely independent tasks. The robots in this architecture possess a variety of high-level functions that they can perform during a mission, and must at all times select an appropriate action based on the requirements of the mission, the activities of other robots, the current environmental conditions, and their own internal states. Since such cooperative teams often work in dynamic and unpredictable environments, the software architecture allows the team members to respond robustly and reliably to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. After presenting ALLIANCE, the author describes in detail the experimental results of an implementation of this architecture on a team of physical mobile robots performing a cooperative box pushing demonstration. These experiments illustrate the ability of ALLIANCE to achieve adaptive, fault-tolerant cooperative control amidst dynamic changes in the capabilities of the robot team.

Soft Ultrathin Electronics Innervated Adaptive Fully Soft Robots.

Science.gov (United States)

Wang, Chengjun; Sim, Kyoseung; Chen, Jin; Kim, Hojin; Rao, Zhoulyu; Li, Yuhang; Chen, Weiqiu; Song, Jizhou; Verduzco, Rafael; Yu, Cunjiang

2018-03-01

Soft robots outperform the conventional hard robots on significantly enhanced safety, adaptability, and complex motions. The development of fully soft robots, especially fully from smart soft materials to mimic soft animals, is still nascent. In addition, to date, existing soft robots cannot adapt themselves to the surrounding environment, i.e., sensing and adaptive motion or response, like animals. Here, compliant ultrathin sensing and actuating electronics innervated fully soft robots that can sense the environment and perform soft bodied crawling adaptively, mimicking an inchworm, are reported. The soft robots are constructed with actuators of open-mesh shaped ultrathin deformable heaters, sensors of single-crystal Si optoelectronic photodetectors, and thermally responsive artificial muscle of carbon-black-doped liquid-crystal elastomer (LCE-CB) nanocomposite. The results demonstrate that adaptive crawling locomotion can be realized through the conjugation of sensing and actuation, where the sensors sense the environment and actuators respond correspondingly to control the locomotion autonomously through regulating the deformation of LCE-CB bimorphs and the locomotion of the robots. The strategy of innervating soft sensing and actuating electronics with artificial muscles paves the way for the development of smart autonomous soft robots. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Self-organization via active exploration in robotic applications

Science.gov (United States)

Ogmen, H.; Prakash, R. V.

1992-01-01

We describe a neural network based robotic system. Unlike traditional robotic systems, our approach focussed on non-stationary problems. We indicate that self-organization capability is necessary for any system to operate successfully in a non-stationary environment. We suggest that self-organization should be based on an active exploration process. We investigated neural architectures having novelty sensitivity, selective attention, reinforcement learning, habit formation, flexible criteria categorization properties and analyzed the resulting behavior (consisting of an intelligent initiation of exploration) by computer simulations. While various computer vision researchers acknowledged recently the importance of active processes (Swain and Stricker, 1991), the proposed approaches within the new framework still suffer from a lack of self-organization (Aloimonos and Bandyopadhyay, 1987; Bajcsy, 1988). A self-organizing, neural network based robot (MAVIN) has been recently proposed (Baloch and Waxman, 1991). This robot has the capability of position, size rotation invariant pattern categorization, recognition and pavlovian conditioning. Our robot does not have initially invariant processing properties. The reason for this is the emphasis we put on active exploration. We maintain the point of view that such invariant properties emerge from an internalization of exploratory sensory-motor activity. Rather than coding the equilibria of such mental capabilities, we are seeking to capture its dynamics to understand on the one hand how the emergence of such invariances is possible and on the other hand the dynamics that lead to these invariances. The second point is crucial for an adaptive robot to acquire new invariances in non-stationary environments, as demonstrated by the inverting glass experiments of Helmholtz. We will introduce Pavlovian conditioning circuits in our future work for the precise objective of achieving the generation, coordination, and internalization

Self-organized control in cooperative robots using a pattern formation principle

International Nuclear Information System (INIS)

Starke, Jens; Ellsaesser, Carmen; Fukuda, Toshio

2011-01-01

Self-organized modular approaches proved in nature to be robust and optimal and are a promising strategy to control future concepts of flexible and modular manufacturing processes. We show how this can be applied to a model of flexible manufacturing based on time-dependent robot-target assignment problems where robot teams have to serve manufacturing targets such that an objective function is optimized. Feasibility of the self-organized solutions can be guaranteed even for unpredictable situations like sudden changes in the demands or breakdowns of robots. As example an uncrewed space mission is visualized in a simulation where robots build a space station. - Highlights: → Adapting a pattern formation principle to control cooperative robots in a robust way. → Flexible manufacturing systems are modelled by time-dependent assignment problems. → Coupled selection equations guarantee feasibility of solutions. → Solution structure (permutations) is not destroyed by inhomogeneous growth rates. → Example of an uncrewed space mission shows effectivity and robustness.

Adaptive Behavior for Mobile Robots

Science.gov (United States)

Huntsberger, Terrance

2009-01-01

The term "System for Mobility and Access to Rough Terrain" (SMART) denotes a theoretical framework, a control architecture, and an algorithm that implements the framework and architecture, for enabling a land-mobile robot to adapt to changing conditions. SMART is intended to enable the robot to recognize adverse terrain conditions beyond its optimal operational envelope, and, in response, to intelligently reconfigure itself (e.g., adjust suspension heights or baseline distances between suspension points) or adapt its driving techniques (e.g., engage in a crabbing motion as a switchback technique for ascending steep terrain). Conceived for original application aboard Mars rovers and similar autonomous or semi-autonomous mobile robots used in exploration of remote planets, SMART could also be applied to autonomous terrestrial vehicles to be used for search, rescue, and/or exploration on rough terrain.

Raven surgical robot training in preparation for da vinci.

Science.gov (United States)

Glassman, Deanna; White, Lee; Lewis, Andrew; King, Hawkeye; Clarke, Alicia; Glassman, Thomas; Comstock, Bryan; Hannaford, Blake; Lendvay, Thomas S

2014-01-01

The rapid adoption of robotic assisted surgery challenges the pace at which adequate robotic training can occur due to access limitations to the da Vinci robot. Thirty medical students completed a randomized controlled trial evaluating whether the Raven robot could be used as an alternative training tool for the Fundamentals of Laparoscopic Surgery (FLS) block transfer task on the da Vinci robot. Two groups, one trained on the da Vinci and one trained on the Raven, were tested on a criterion FLS block transfer task on the da Vinci. After robotic FLS block transfer proficiency training there was no statistically significant difference between path length (p=0.39) and economy of motion scores (p=0.06) between the two groups, but those trained on the da Vinci did have faster task times (p=0.01). These results provide evidence for the value of using the Raven robot for training prior to using the da Vinci surgical system for similar tasks.

Robot-assisted adaptive training: custom force fields for teaching movement patterns.

Science.gov (United States)

Patton, James L; Mussa-Ivaldi, Ferdinando A

2004-04-01

Based on recent studies of neuro-adaptive control, we tested a new iterative algorithm to generate custom training forces to "trick" subjects into altering their target-directed reaching movements to a prechosen movement as an after-effect of adaptation. The prechosen movement goal, a sinusoidal-shaped path from start to end point, was never explicitly conveyed to the subject. We hypothesized that the adaptation would cause an alteration in the feedforward command that would result in the prechosen movement. Our results showed that when forces were suddenly removed after a training period of 330 movements, trajectories were significantly shifted toward the prechosen movement. However, de-adaptation occurred (i.e., the after-effect "washed out") in the 50-75 movements that followed the removal of the training forces. A second experiment suppressed vision of hand location and found a detectable reduction in the washout of after-effects, suggesting that visual feedback of error critically influences learning. A final experiment demonstrated that after-effects were also present in the neighborhood of training--44% of original directional shift was seen in adjacent, unpracticed movement directions to targets that were 60 degrees different from the targets used for training. These results demonstrate the potential for these methods for teaching motor skills and for neuro-rehabilitation of brain-injured patients. This is a form of "implicit learning," because unlike explicit training methods, subjects learn movements with minimal instructions, no knowledge of, and little attention to the trajectory.

Elements of Autonomous Self-Reconfigurable Robots

DEFF Research Database (Denmark)

Christensen, David Johan

In this thesis, we study several central elements of autonomous self-reconfigurable modular robots. Unlike conventional robots such robots are: i) Modular, since robots are assembled from numerous robotic modules. ii) Reconfigurable, since the modules can be combined in a variety of ways. iii) Self......-reconfigurable, since the modules themselves are able to change how they are combined. iv) Autonomous, since robots control themselves without human guidance. Such robots are attractive to study since they in theory have several desirable characteristics, such as versatility, reliability and cheapness. In practice...... however, it is challenging to realize such characteristics since state-of-the-art systems and solutions suffer from several inherent technical and theoretical problems and limitations. In this thesis, we address these challenges by exploring four central elements of autonomous self-reconfigurable modular...

Adaptive learning fuzzy control of a mobile robot

International Nuclear Information System (INIS)

Tsukada, Akira; Suzuki, Katsuo; Fujii, Yoshio; Shinohara, Yoshikuni

1989-11-01

In this report a problem is studied to construct a fuzzy controller for a mobile robot to move autonomously along a given reference direction curve, for which control rules are generated and acquired through an adaptive learning process. An adaptive learning fuzzy controller has been developed for a mobile robot. Good properties of the controller are shown through the travelling experiments of the mobile robot. (author)

Locomotion training of legged robots using hybrid machine learning techniques

Science.gov (United States)

Simon, William E.; Doerschuk, Peggy I.; Zhang, Wen-Ran; Li, Andrew L.

1995-01-01

In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible

Portraits of self-organization in fish schools interacting with robots

Science.gov (United States)

Aureli, M.; Fiorilli, F.; Porfiri, M.

2012-05-01

In this paper, we propose an enabling computational and theoretical framework for the analysis of experimental instances of collective behavior in response to external stimuli. In particular, this work addresses the characterization of aggregation and interaction phenomena in robot-animal groups through the exemplary analysis of fish schooling in the vicinity of a biomimetic robot. We adapt global observables from statistical mechanics to capture the main features of the shoal collective motion and its response to the robot from experimental observations. We investigate the shoal behavior by using a diffusion mapping analysis performed on these global observables that also informs the definition of relevant portraits of self-organization.

Novel Adaptive Forward Neural MIMO NARX Model for the Identification of Industrial 3-DOF Robot Arm Kinematics

OpenAIRE

Ho Pham Huy Anh; Nguyen Thanh Nam

2012-01-01

In this paper, a novel forward adaptive neural MIMO NARX model is used for modelling and identifying the forward kinematics of an industrial 3‐DOF robot arm system. The nonlinear features of the forward kinematics of the industrial robot arm drive are thoroughly modelled based on the forward adaptive neural NARX model‐based identification process using experimental input‐output training data. This paper proposes a novel use of a back propagation (BP) algorithm to generate the forward neural M...

Physiological Self-Regulation and Adaptive Automation

Science.gov (United States)

Prinzell, Lawrence J.; Pope, Alan T.; Freeman, Frederick G.

2007-01-01

Adaptive automation has been proposed as a solution to current problems of human-automation interaction. Past research has shown the potential of this advanced form of automation to enhance pilot engagement and lower cognitive workload. However, there have been concerns voiced regarding issues, such as automation surprises, associated with the use of adaptive automation. This study examined the use of psychophysiological self-regulation training with adaptive automation that may help pilots deal with these problems through the enhancement of cognitive resource management skills. Eighteen participants were assigned to 3 groups (self-regulation training, false feedback, and control) and performed resource management, monitoring, and tracking tasks from the Multiple Attribute Task Battery. The tracking task was cycled between 3 levels of task difficulty (automatic, adaptive aiding, manual) on the basis of the electroencephalogram-derived engagement index. The other two tasks remained in automatic mode that had a single automation failure. Those participants who had received self-regulation training performed significantly better and reported lower National Aeronautics and Space Administration Task Load Index scores than participants in the false feedback and control groups. The theoretical and practical implications of these results for adaptive automation are discussed.

Robot-assisted laparoscopic skills development: formal versus informal training.

Science.gov (United States)

Benson, Aaron D; Kramer, Brandan A; Boehler, Margaret; Schwind, Cathy J; Schwartz, Bradley F

2010-08-01

The learning curve for robotic surgery is not completely defined, and ideal training components have not yet been identified. We attempted to determine whether skill development would be accelerated with formal, organized instruction in robotic surgical techniques versus informal practice alone. Forty-three medical students naive to robotic surgery were randomized into two groups and tested on three tasks using the robotic platform. Between the testing sessions, the students were given equally timed practice sessions. The formal training group participated in an organized, formal training session with instruction from an attending robotic surgeon, whereas the informal training group participated in an equally timed unstructured practice session with the robot. The results were compared based on technical score and time to completion of each task. There was no difference between groups in prepractice testing for any task. In postpractice testing, there was no difference between groups for the ring transfer tasks. However, for the suture placement and knot-tying task, the technical score of the formal training group was significantly better than that of the informal training group (p formal training may not be necessary for basic skills, formal instruction for more advanced skills, such as suture placement and knot tying, is important in developing skills needed for effective robotic surgery. These findings may be important in formulating potential skills labs or training courses for robotic surgery.

An adaptable Boolean net trainable to control a computing robot

International Nuclear Information System (INIS)

Lauria, F. E.; Prevete, R.; Milo, M.; Visco, S.

1999-01-01

We discuss a method to implement in a Boolean neural network a Hebbian rule so to obtain an adaptable universal control system. We start by presenting both the Boolean neural net and the Hebbian rule we have considered. Then we discuss, first, the problems arising when the latter is naively implemented in a Boolean neural net, second, the method consenting us to overcome them and the ensuing adaptable Boolean neural net paradigm. Next, we present the adaptable Boolean neural net as an intelligent control system, actually controlling a writing robot, and discuss how to train it in the execution of the elementary arithmetic operations on operands represented by numerals with an arbitrary number of digits

Individualized robot-assisted training for MS- and stroke patients in I-TRAVLE

Directory of Open Access Journals (Sweden)

Bastiaens Hanne

2011-12-01

Full Text Available Persons with central nervous deficits, such as MS and stroke patients, can benefit a lot from suitable training approaches that enhance their ability to perform activities in daily life. Personalized training, in accordance with the individual capabilities of the patient is a key issue in this context. We propose several techniques for individualization, including adaptive training games. Evaluations with patients and therapists reveal appreciation for the resulting Individualized, Technology-supported and RobotAssisted Virtual Learning Environments (I-TRAVLE system.

Pose Estimation and Adaptive Robot Behaviour for Human-Robot Interaction

DEFF Research Database (Denmark)

Svenstrup, Mikael; Hansen, Søren Tranberg; Andersen, Hans Jørgen

2009-01-01

Abstract—This paper introduces a new method to determine a person’s pose based on laser range measurements. Such estimates are typically a prerequisite for any human-aware robot navigation, which is the basis for effective and timeextended interaction between a mobile robot and a human. The robot......’s pose. The resulting pose estimates are used to identify humans who wish to be approached and interacted with. The interaction motion of the robot is based on adaptive potential functions centered around the person that respect the persons social spaces. The method is tested in experiments...

Biologically-Inspired Adaptive Obstacle Negotiation Behavior of Hexapod Robots

DEFF Research Database (Denmark)

Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural...... learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal (conditioned stimulus, CS) and a late, reflex signal...... (unconditioned stimulus, UCS), both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully...

Biologically-Inspired Adaptive Obstacle Negotiation Behavior of Hexapod Robots

Directory of Open Access Journals (Sweden)

Dennis eGoldschmidt

2014-01-01

Full Text Available Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal (conditioned stimulus, CS and a late, reflex signal (unconditioned stimulus, UCS, both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II. The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robot's leg length in simulation and 75% in a real environment.

Virtual Reality Simulator Systems in Robotic Surgical Training.

Science.gov (United States)

Mangano, Alberto; Gheza, Federico; Giulianotti, Pier Cristoforo

2018-06-01

The number of robotic surgical procedures has been increasing worldwide. It is important to maximize the cost-effectiveness of robotic surgical training and safely reduce the time needed for trainees to reach proficiency. The use of preliminary lab training in robotic skills is a good strategy for the rapid acquisition of further, standardized robotic skills. Such training can be done either by using a simulator or by exercises in a dry or wet lab. While the use of an actual robotic surgical system for training may be problematic (high cost, lack of availability), virtual reality (VR) simulators can overcome many of these obstacles. However, there is still a lack of standardization. Although VR training systems have improved, they cannot yet replace experience in a wet lab. In particular, simulated scenarios are not yet close enough to a real operative experience. Indeed, there is a difference between technical skills (i.e., mechanical ability to perform a simulated task) and surgical competence (i.e., ability to perform a real surgical operation). Thus, while a VR simulator can replace a dry lab, it cannot yet replace training in a wet lab or operative training in actual patients. However, in the near future, it is expected that VR surgical simulators will be able to provide total reality simulation and replace training in a wet lab. More research is needed to produce more wide-ranging, trans-specialty robotic curricula.

Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

Directory of Open Access Journals (Sweden)

Chunmei Liu

2016-01-01

Full Text Available This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour.

Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

Science.gov (United States)

2016-01-01

This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour. PMID:27379165

Robotics Programs: Automation Training in Disguise.

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Rehg, James A.

1985-01-01

Questions and answers from the book "Guidelines for Robotics Program Development" are presented, addressing some of the major issues confronted by the person setting the direction for a robotics training program. (CT)

Feasibility and acceptance of a robotic surgery ergonomic training program.

Science.gov (United States)

Franasiak, Jason; Craven, Renatta; Mosaly, Prithima; Gehrig, Paola A

2014-01-01

Assessment of ergonomic strain during robotic surgery indicates there is a need for intervention. However, limited data exist detailing the feasibility and acceptance of ergonomic training (ET) for robotic surgeons. This prospective, observational pilot study evaluates the implementation of an evidence-based ET module. A two-part survey was conducted. The first survey assessed robotic strain using the Nordic Musculoskeletal Questionnaire (NMQ). Participants were given the option to participate in either an online or an in-person ET session. The ET was derived from Occupational Safety and Health Administration guidelines and developed by a human factors engineer experienced with health care ergonomics. After ET, a follow-up survey including the NMQ and an assessment of the ET were completed. The survey was sent to 67 robotic surgeons. Forty-two (62.7%) responded, including 18 residents, 8 fellows, and 16 attending physicians. Forty-five percent experienced strain resulting from performing robotic surgery and 26.3% reported persistent strain. Only 16.6% of surgeons reported prior ET in robotic surgery. Thirty-five (78%) surgeons elected to have in-person ET, which was successfully arranged for 32 surgeons (91.4%). Thirty-seven surgeons (88.1%) completed the follow-up survey. All surgeons participating in the in-person ET found it helpful and felt formal ET should be standard, 88% changed their practice as a result of the training, and 74% of those reporting strain noticed a decrease after their ET. Thus, at a high-volume robotics center, evidence-based ET was easily implemented, well-received, changed some surgeons' practice, and decreased self-reported strain related to robotic surgery.

Robot training of upper limb in multiple sclerosis: comparing protocols with or without manipulative task components.

Science.gov (United States)

Carpinella, Ilaria; Cattaneo, Davide; Bertoni, Rita; Ferrarin, Maurizio

2012-05-01

In this pilot study, we compared two protocols for robot-based rehabilitation of upper limb in multiple sclerosis (MS): a protocol involving reaching tasks (RT) requiring arm transport only and a protocol requiring both objects' reaching and manipulation (RMT). Twenty-two MS subjects were assigned to RT or RMT group. Both protocols consisted of eight sessions. During RT training, subjects moved the handle of a planar robotic manipulandum toward circular targets displayed on a screen. RMT protocol required patients to reach and manipulate real objects, by moving the robotic arm equipped with a handle which left the hand free for distal tasks. In both trainings, the robot generated resistive and perturbing forces. Subjects were evaluated with clinical and instrumental tests. The results confirmed that MS patients maintained the ability to adapt to the robot-generated forces and that the rate of motor learning increased across sessions. Robot-therapy significantly reduced arm tremor and improved arm kinematics and functional ability. Compared to RT, RMT protocol induced a significantly larger improvement in movements involving grasp (improvement in Grasp ARAT sub-score: RMT 77.4%, RT 29.5%, p=0.035) but not precision grip. Future studies are needed to evaluate if longer trainings and the use of robotic handles would significantly improve also fine manipulation.

General surgery training and robotics: Are residents improving their skills?

Science.gov (United States)

Finnerty, Brendan M; Afaneh, Cheguevara; Aronova, Anna; Fahey, Thomas J; Zarnegar, Rasa

2016-02-01

While robotic-assisted operations have become more prevalent, many general surgery residencies do not have a formal robotic training curriculum. We sought to ascertain how well current general surgery training permits acquisition of robotic skills by comparing robotic simulation performance across various training levels. Thirty-six participants were categorized by level of surgical training: eight medical students (MS), ten junior residents (JR), ten mid-level residents (MLR), and eight senior residents (SR). Participants performed three simulation tasks on the da Vinci (®) Skills Simulator (MatchBoard, EnergyDissection, SutureSponge). Each task's scores (0-100) and cumulative scores (0-300) were compared between groups. There were no differences in sex, hand dominance, video gaming history, or prior robotic experience between groups; however, SR was the oldest (p Robotic skillsets acquired during general surgery residency show minimal improvement during the course of training, although laparoscopic experience is correlated with advanced robotic task performance. Changes in residency curricula or pursuit of fellowship training may be warranted for surgeons seeking proficiency.

Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial.

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Rowe, Justin B; Chan, Vicky; Ingemanson, Morgan L; Cramer, Steven C; Wolbrecht, Eric T; Reinkensmeyer, David J

2017-08-01

Robots that physically assist movement are increasingly used in rehabilitation therapy after stroke, yet some studies suggest robotic assistance discourages effort and reduces motor learning. To determine the therapeutic effects of high and low levels of robotic assistance during finger training. We designed a protocol that varied the amount of robotic assistance while controlling the number, amplitude, and exerted effort of training movements. Participants (n = 30) with a chronic stroke and moderate hemiparesis (average Box and Blocks Test 32 ± 18 and upper extremity Fugl-Meyer score 46 ± 12) actively moved their index and middle fingers to targets to play a musical game similar to GuitarHero 3 h/wk for 3 weeks. The participants were randomized to receive high assistance (causing 82% success at hitting targets) or low assistance (55% success). Participants performed ~8000 movements during 9 training sessions. Both groups improved significantly at the 1-month follow-up on functional and impairment-based motor outcomes, on depression scores, and on self-efficacy of hand function, with no difference between groups in the primary endpoint (change in Box and Blocks). High assistance boosted motivation, as well as secondary motor outcomes (Fugl-Meyer and Lateral Pinch Strength)-particularly for individuals with more severe finger motor deficits. Individuals with impaired finger proprioception at baseline benefited less from the training. Robot-assisted training can promote key psychological outcomes known to modulate motor learning and retention. Furthermore, the therapeutic effectiveness of robotic assistance appears to derive at least in part from proprioceptive stimulation, consistent with a Hebbian plasticity model.

Development and Implementation of an End-Effector Upper Limb Rehabilitation Robot for Hemiplegic Patients with Line and Circle Tracking Training

Directory of Open Access Journals (Sweden)

Yali Liu

2017-01-01

Full Text Available Numerous robots have been widely used to deliver rehabilitative training for hemiplegic patients to improve their functional ability. Because of the complexity and diversity of upper limb motion, customization of training patterns is one key factor during upper limb rehabilitation training. Most of the current rehabilitation robots cannot intelligently provide adaptive training parameters, and they have not been widely used in clinical rehabilitation. This article proposes a new end-effector upper limb rehabilitation robot, which is a two-link robotic arm with two active degrees of freedom. This work investigated the kinematics and dynamics of the robot system, the control system, and the realization of different rehabilitation therapies. We also explored the influence of constraint in rehabilitation therapies on interaction force and muscle activation. The deviation of the trajectory of the end effector and the required trajectory was less than 1 mm during the tasks, which demonstrated the movement accuracy of the robot. Besides, results also demonstrated the constraint exerted by the robot provided benefits for hemiplegic patients by changing muscle activation in the way similar to the movement pattern of the healthy subjects, which indicated that the robot can improve the patient’s functional ability by training the normal movement pattern.

Evolutionary online behaviour learning and adaptation in real robots.

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Silva, Fernando; Correia, Luís; Christensen, Anders Lyhne

2017-07-01

Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.

Proficiency training on a virtual reality robotic surgical skills curriculum.

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Bric, Justin; Connolly, Michael; Kastenmeier, Andrew; Goldblatt, Matthew; Gould, Jon C

2014-12-01

The clinical application of robotic surgery is increasing. The skills necessary to perform robotic surgery are unique from those required in open and laparoscopic surgery. A validated laparoscopic surgical skills curriculum (Fundamentals of Laparoscopic Surgery or FLS™) has transformed the way surgeons acquire laparoscopic skills. There is a need for a similar skills training and assessment tool for robotic surgery. Our research group previously developed and validated a robotic training curriculum in a virtual reality (VR) simulator. We hypothesized that novice robotic surgeons could achieve proficiency levels defined by more experienced robotic surgeons on the VR robotic curriculum, and that this would result in improved performance on the actual daVinci Surgical System™. 25 medical students with no prior robotic surgery experience were recruited. Prior to VR training, subjects performed 2 FLS tasks 3 times each (Peg Transfer, Intracorporeal Knot Tying) using the daVinci Surgical System™ docked to a video trainer box. Task performance for the FLS tasks was scored objectively. Subjects then practiced on the VR simulator (daVinci Skills Simulator) until proficiency levels on all 5 tasks were achieved before completing a post-training assessment of the 2 FLS tasks on the daVinci Surgical System™ in the video trainer box. All subjects to complete the study (1 dropped out) reached proficiency levels on all VR tasks in an average of 71 (± 21.7) attempts, accumulating 164.3 (± 55.7) minutes of console training time. There was a significant improvement in performance on the robotic FLS tasks following completion of the VR training curriculum. Novice robotic surgeons are able to attain proficiency levels on a VR simulator. This leads to improved performance in the daVinci surgical platform on simulated tasks. Training to proficiency on a VR robotic surgery simulator is an efficient and viable method for acquiring robotic surgical skills.

General surgery residents' perception of robot-assisted procedures during surgical training.

Science.gov (United States)

Farivar, Behzad S; Flannagan, Molly; Leitman, I Michael

2015-01-01

With the continued expansion of robotically assisted procedures, general surgery residents continue to receive more exposure to this new technology as part of their training. There are currently no guidelines or standardized training requirements for robot-assisted procedures during general surgical residency. The aim of this study was to assess the effect of this new technology on general surgery training from the residents' perspective. An anonymous, national, web-based survey was conducted on residents enrolled in general surgery training in 2013. The survey was sent to 240 Accreditation Council for Graduate Medical Education-approved general surgery training programs. Overall, 64% of the responding residents were men and had an average age of 29 years. Half of the responses were from postgraduate year 1 (PGY1) and PGY2 residents, and the remainder was from the PGY3 level and above. Overall, 50% of the responses were from university training programs, 32% from university-affiliated programs, and 18% from community-based programs. More than 96% of residents noted the availability of the surgical robot system at their training institution. Overall, 63% of residents indicated that they had participated in robotic surgical cases. Most responded that they had assisted in 10 or fewer robotic cases with the most frequent activities being assisting with robotic trocar placement and docking and undocking the robot. Only 18% reported experience with operating the robotic console. More senior residents (PGY3 and above) were involved in robotic cases compared with junior residents (78% vs 48%, p robotic case. Approximately 64% of residents reported that formal training in robotic surgery was important in residency training and 46% of residents indicated that robotic-assisted cases interfered with resident learning. Only 11% felt that robotic-assisted cases would replace conventional laparoscopic surgery in the future. This study illustrates that although the most residents

An adaptive inverse kinematics algorithm for robot manipulators

Science.gov (United States)

Colbaugh, R.; Glass, K.; Seraji, H.

1990-01-01

An adaptive algorithm for solving the inverse kinematics problem for robot manipulators is presented. The algorithm is derived using model reference adaptive control (MRAC) theory and is computationally efficient for online applications. The scheme requires no a priori knowledge of the kinematics of the robot if Cartesian end-effector sensing is available, and it requires knowledge of only the forward kinematics if joint position sensing is used. Computer simulation results are given for the redundant seven-DOF robotics research arm, demonstrating that the proposed algorithm yields accurate joint angle trajectories for a given end-effector position/orientation trajectory.

Research on a New Bilateral Self-locking Mechanism for an Inchworm Micro In-pipe Robot with Large Traction

Directory of Open Access Journals (Sweden)

Junhong Yang

2014-10-01

Full Text Available In this paper, we present an innovative bilaterally-controllable self-locking mechanism that can be applied to the micro in-pipe robot. The background and state of the art of the inchworm micro in-pipe robot is briefly described in the very beginning of the paper, where the main factors that influence the traction ability are also discussed. Afterwards, the micro in-pipe robots’ propulsion principle based on a unidirectional self-locking mechanism is discussed. Then, several kinds of self-locking mechanisms are compared, and a new bilaterally-controllable self-locking mechanism is proposed. By implementing the self-locking mechanism, the robot's tractive force is no longer restricted by the friction force, and both two-way motion and position locking for the robot can be achieved. Finally, the traction experiment is conducted using a prototype robot with the new bilaterally-controllable self-locking mechanism. Test results show that this new self-locking mechanism can adapt itself to a diameter of >17~>20 mm and has a blocking force up to 25N, and the maximum tractive force of the in-pipe robot based on such a locking mechanism is 12N under the maximum velocity of 10mm/s.

Active robotic training improves locomotor function in a stroke survivor

Directory of Open Access Journals (Sweden)

Krishnan Chandramouli

2012-08-01

Full Text Available Abstract Background Clinical outcomes after robotic training are often not superior to conventional therapy. One key factor responsible for this is the use of control strategies that provide substantial guidance. This strategy not only leads to a reduction in volitional physical effort, but also interferes with motor relearning. Methods We tested the feasibility of a novel training approach (active robotic training using a powered gait orthosis (Lokomat in mitigating post-stroke gait impairments of a 52-year-old male stroke survivor. This gait training paradigm combined patient-cooperative robot-aided walking with a target-tracking task. The training lasted for 4-weeks (12 visits, 3 × per week. The subject’s neuromotor performance and recovery were evaluated using biomechanical, neuromuscular and clinical measures recorded at various time-points (pre-training, post-training, and 6-weeks after training. Results Active robotic training resulted in considerable increase in target-tracking accuracy and reduction in the kinematic variability of ankle trajectory during robot-aided treadmill walking. These improvements also transferred to overground walking as characterized by larger propulsive forces and more symmetric ground reaction forces (GRFs. Training also resulted in improvements in muscle coordination, which resembled patterns observed in healthy controls. These changes were accompanied by a reduction in motor cortical excitability (MCE of the vastus medialis, medial hamstrings, and gluteus medius muscles during treadmill walking. Importantly, active robotic training resulted in substantial improvements in several standard clinical and functional parameters. These improvements persisted during the follow-up evaluation at 6 weeks. Conclusions The results indicate that active robotic training appears to be a promising way of facilitating gait and physical function in moderately impaired stroke survivors.

Residency Training in Robotic General Surgery: A Survey of Program Directors.

Science.gov (United States)

George, Lea C; O'Neill, Rebecca; Merchant, Aziz M

2018-01-01

Robotic surgery continues to expand in minimally invasive surgery; however, the literature is insufficient to understand the current training process for general surgery residents. Therefore, the objectives of this study were to identify the current approach to and perspectives on robotic surgery training. An electronic survey was distributed to general surgery program directors identified by the Accreditation Council for Graduate Medical Education website. Multiple choice and open-ended questions regarding current practices and opinions on robotic surgery training in general surgery residency programs were used. 20 program directors were surveyed, a majority being from medium-sized programs (4-7 graduating residents per year). Most respondents (73.68%) had a formal robotic surgery curriculum at their institution, with 63.16% incorporating simulation training. Approximately half of the respondents believe that more time should be dedicated to robotic surgery training (52.63%), with simulation training prior to console use (84.21%). About two-thirds of the respondents (63.16%) believe that a formal robotic surgery curriculum should be established as a part of general surgery residency, with more than half believing that exposure should occur in postgraduate year one (55%). A formal robotics curriculum with simulation training and early surgical exposure for general surgery residents should be given consideration in surgical residency training.

A soft robot capable of 2D mobility and self-sensing for obstacle detection and avoidance

Science.gov (United States)

Qin, Lei; Tang, Yucheng; Gupta, Ujjaval; Zhu, Jian

2018-04-01

Soft robots have shown great potential for surveillance applications due to their interesting attributes including inherent flexibility, extreme adaptability, and excellent ability to move in confined spaces. High mobility combined with the sensing systems that can detect obstacles plays a significant role in performing surveillance tasks. Extensive studies have been conducted on movement mechanisms of traditional hard-bodied robots to increase their mobility. However, there are limited efforts in the literature to explore the mobility of soft robots. In addition, little attempt has been made to study the obstacle-detection capability of a soft mobile robot. In this paper, we develop a soft mobile robot capable of high mobility and self-sensing for obstacle detection and avoidance. This robot, consisting of a dielectric elastomer actuator as the robot body and four electroadhesion actuators as the robot feet, can generate 2D mobility, i.e. translations and turning in a 2D plane, by programming the actuation sequence of the robot body and feet. Furthermore, we develop a self-sensing method which models the robot body as a deformable capacitor. By measuring the real-time capacitance of the robot body, the robot can detect an obstacle when the peak capacitance drops suddenly. This sensing method utilizes the robot body itself instead of external sensors to achieve detection of obstacles, which greatly reduces the weight and complexity of the robot system. The 2D mobility and self-sensing capability ensure the success of obstacle detection and avoidance, which paves the way for the development of lightweight and intelligent soft mobile robots.

Direct adaptive control of a PUMA 560 industrial robot

Science.gov (United States)

Seraji, Homayoun; Lee, Thomas; Delpech, Michel

1989-01-01

The implementation and experimental validation of a new direct adaptive control scheme on a PUMA 560 industrial robot is described. The testbed facility consists of a Unimation PUMA 560 six-jointed robot and controller, and a DEC MicroVAX II computer which hosts the Robot Control C Library software. The control algorithm is implemented on the MicroVAX which acts as a digital controller for the PUMA robot, and the Unimation controller is effectively bypassed and used merely as an I/O device to interface the MicroVAX to the joint motors. The control algorithm for each robot joint consists of an auxiliary signal generated by a constant-gain Proportional plus Integral plus Derivative (PID) controller, and an adaptive position-velocity (PD) feedback controller with adjustable gains. The adaptive independent joint controllers compensate for the inter-joint couplings and achieve accurate trajectory tracking without the need for the complex dynamic model and parameter values of the robot. Extensive experimental results on PUMA joint control are presented to confirm the feasibility of the proposed scheme, in spite of strong interactions between joint motions. Experimental results validate the capabilities of the proposed control scheme. The control scheme is extremely simple and computationally very fast for concurrent processing with high sampling rates.

Evolving self-assembly in autonomous homogeneous robots: experiments with two physical robots.

Science.gov (United States)

Ampatzis, Christos; Tuci, Elio; Trianni, Vito; Christensen, Anders Lyhne; Dorigo, Marco

2009-01-01

This research work illustrates an approach to the design of controllers for self-assembling robots in which the self-assembly is initiated and regulated by perceptual cues that are brought forth by the physical robots through their dynamical interactions. More specifically, we present a homogeneous control system that can achieve assembly between two modules (two fully autonomous robots) of a mobile self-reconfigurable system without a priori introduced behavioral or morphological heterogeneities. The controllers are dynamic neural networks evolved in simulation that directly control all the actuators of the two robots. The neurocontrollers cause the dynamic specialization of the robots by allocating roles between them based solely on their interaction. We show that the best evolved controller proves to be successful when tested on a real hardware platform, the swarm-bot. The performance achieved is similar to the one achieved by existing modular or behavior-based approaches, also due to the effect of an emergent recovery mechanism that was neither explicitly rewarded by the fitness function, nor observed during the evolutionary simulation. Our results suggest that direct access to the orientations or intentions of the other agents is not a necessary condition for robot coordination: Our robots coordinate without direct or explicit communication, contrary to what is assumed by most research works in collective robotics. This work also contributes to strengthening the evidence that evolutionary robotics is a design methodology that can tackle real-world tasks demanding fine sensory-motor coordination.

Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics

Directory of Open Access Journals (Sweden)

Morone G

2017-05-01

Full Text Available Giovanni Morone,1,2 Stefano Paolucci,1,2 Andrea Cherubini,3 Domenico De Angelis,1 Vincenzo Venturiero,1 Paola Coiro,1 Marco Iosa1,2 1Private Inpatient Unit, 2Clinical Laboratory of Experimental Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy; 3Department of Robotics, LIRMM UM-CNRS, Montpellier, France Abstract: In this review, we give a brief outline of robot-mediated gait training for stroke patients, as an important emerging field in rehabilitation. Technological innovations are allowing rehabilitation to move toward more integrated processes, with improved efficiency and less long-term impairments. In particular, robot-mediated neurorehabilitation is a rapidly advancing field, which uses robotic systems to define new methods for treating neurological injuries, especially stroke. The use of robots in gait training can enhance rehabilitation, but it needs to be used according to well-defined neuroscientific principles. The field of robot-mediated neurorehabilitation brings challenges to both bioengineering and clinical practice. This article reviews the state of the art (including commercially available systems and perspectives of robotics in poststroke rehabilitation for walking recovery. A critical revision, including the problems at stake regarding robotic clinical use, is also presented. Keywords: exoskeleton, neurorehabilitation, robot-assisted walking training, wearable robot, activities of daily living, motor learning, plasticity

Trunk Robot Rehabilitation Training with Active Stepping Reorganizes and Enriches Trunk Motor Cortex Representations in Spinal Transected Rats

Science.gov (United States)

Oza, Chintan S.

2015-01-01

Trunk motor control is crucial for postural stability and propulsion after low thoracic spinal cord injury (SCI) in animals and humans. Robotic rehabilitation aimed at trunk shows promise in SCI animal models and patients. However, little is known about the effect of SCI and robot rehabilitation of trunk on cortical motor representations. We previously showed reorganization of trunk motor cortex after adult SCI. Non-stepping training also exacerbated some SCI-driven plastic changes. Here we examine effects of robot rehabilitation that promotes recovery of hindlimb weight support functions on trunk motor cortex representations. Adult rats spinal transected as neonates (NTX rats) at the T9/10 level significantly improve function with our robot rehabilitation paradigm, whereas treadmill-only trained do not. We used intracortical microstimulation to map motor cortex in two NTX groups: (1) treadmill trained (control group); and (2) robot-assisted treadmill trained (improved function group). We found significant robot rehabilitation-driven changes in motor cortex: (1) caudal trunk motor areas expanded; (2) trunk coactivation at cortex sites increased; (3) richness of trunk cortex motor representations, as examined by cumulative entropy and mutual information for different trunk representations, increased; (4) trunk motor representations in the cortex moved toward more normal topography; and (5) trunk and forelimb motor representations that SCI-driven plasticity and compensations had caused to overlap were segregated. We conclude that effective robot rehabilitation training induces significant reorganization of trunk motor cortex and partially reverses some plastic changes that may be adaptive in non-stepping paraplegia after SCI. PMID:25948267

Robotic technologies in surgical oncology training and practice.

Science.gov (United States)

Orvieto, Marcelo A; Marchetti, Pablo; Castillo, Octavio A; Coelho, Rafael F; Chauhan, Sanket; Rocco, Bernardo; Ardila, Bobby; Mathe, Mary; Patel, Vipul R

2011-09-01

The modern-day surgeon is frequently exposed to new technologies and instrumentation. Robotic surgery (RS) has evolved as a minimally invasive technique aimed to improve clinical outcomes. RS has the potential to alleviate the inherent limitations of laparoscopic surgery such as two dimensional imaging, limited instrument movement and intrinsic human tremor. Since the first reported robot-assisted surgical procedure performed in 1985, the technology has dramatically evolved and currently multiple surgical specialties have incorporated RS into their daily clinical armamentarium. With this exponential growth, it should not come as a surprise the ever growing requirement for surgeons trained in RS as well as the interest from residents to receive robotic exposure during their training. For this reason, the establishment of set criteria for adequate and standardized training and credentialing of surgical residents, fellows and those trained surgeons wishing to perform RS has become a priority. In this rapidly evolving field, we herein review the past, present and future of robotic technologies and its penetration into different surgical specialties. Copyright © 2010 Elsevier Ltd. All rights reserved.

Residency Training in Robotic General Surgery: A Survey of Program Directors

Directory of Open Access Journals (Sweden)

Lea C. George

2018-01-01

Full Text Available Objective. Robotic surgery continues to expand in minimally invasive surgery; however, the literature is insufficient to understand the current training process for general surgery residents. Therefore, the objectives of this study were to identify the current approach to and perspectives on robotic surgery training. Methods. An electronic survey was distributed to general surgery program directors identified by the Accreditation Council for Graduate Medical Education website. Multiple choice and open-ended questions regarding current practices and opinions on robotic surgery training in general surgery residency programs were used. Results. 20 program directors were surveyed, a majority being from medium-sized programs (4–7 graduating residents per year. Most respondents (73.68% had a formal robotic surgery curriculum at their institution, with 63.16% incorporating simulation training. Approximately half of the respondents believe that more time should be dedicated to robotic surgery training (52.63%, with simulation training prior to console use (84.21%. About two-thirds of the respondents (63.16% believe that a formal robotic surgery curriculum should be established as a part of general surgery residency, with more than half believing that exposure should occur in postgraduate year one (55%. Conclusion. A formal robotics curriculum with simulation training and early surgical exposure for general surgery residents should be given consideration in surgical residency training.

Reduction of freezing of gait in Parkinson's disease by repetitive robot-assisted treadmill training: a pilot study

Directory of Open Access Journals (Sweden)

Friedman Joseph H

2010-10-01

Full Text Available Abstract Background Parkinson's disease is a chronic, neurodegenerative disease characterized by gait abnormalities. Freezing of gait (FOG, an episodic inability to generate effective stepping, is reported as one of the most disabling and distressing parkinsonian symptoms. While there are no specific therapies to treat FOG, some external physical cues may alleviate these types of motor disruptions. The purpose of this study was to examine the potential effect of continuous physical cueing using robot-assisted sensorimotor gait training on reducing FOG episodes and improving gait. Methods Four individuals with Parkinson's disease and FOG symptoms received ten 30-minute sessions of robot-assisted gait training (Lokomat to facilitate repetitive, rhythmic, and alternating bilateral lower extremity movements. Outcomes included the FOG-Questionnaire, a clinician-rated video FOG score, spatiotemporal measures of gait, and the Parkinson's Disease Questionnaire-39 quality of life measure. Results All participants showed a reduction in FOG both by self-report and clinician-rated scoring upon completion of training. Improvements were also observed in gait velocity, stride length, rhythmicity, and coordination. Conclusions This pilot study suggests that robot-assisted gait training may be a feasible and effective method of reducing FOG and improving gait. Videotaped scoring of FOG has the potential advantage of providing additional data to complement FOG self-report.

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

Training in robotics: The learning curve and contemporary concepts in training.

Science.gov (United States)

Bach, Christian; Miernik, Arkadiusz; Schönthaler, Martin

2014-03-01

To define the learning curve of robot-assisted laparoscopic surgery for prostatectomy (RALP) and upper tract procedures, and show the differences between the classical approach to training and the new concept of parallel learning. This mini-review is based on the results of a Medline search using the keywords 'da Vinci', 'robot-assisted laparoscopic surgery', 'training', 'teaching' and 'learning curve'. For RALP and robot-assisted upper tract surgery, a learning curve of 8-150 procedures is quoted, with most articles proposing that 30-40 cases are needed to carry out the procedure safely. There is no consensus about which endpoints should be measured. In the traditional proctored training model, the surgeon learns the procedure linearly, following the sequential order of the surgical steps. A more recent approach is to specify the relative difficulty of each step and to train the surgeon simultaneously in several steps of equal difficulty. The entire procedure is only performed after all the steps are mastered in a timely manner. Recently, a 'warm-up' before robotic surgery has been shown to be beneficial for successful surgery in the operating room. There is no clear definition of the duration of the effective learning curve for RALP and robotic upper tract surgery. The concept of stepwise, parallel learning has the potential to accelerate the learning process and to make sure that initial cases are not too long. It can also be assumed that a preoperative 'warm up' could help significantly to improve the progress of the trainee.

Lattice Automata for Control of Self-Reconfigurable Robots

DEFF Research Database (Denmark)

Støy, Kasper

2015-01-01

are extreme versatility and robustness. The organisation of self-reconfigurable robots in a lattice structure and the emphasis on local communication between modules mean that lattice automata are a useful basis for control of self-reconfigurable robots. However, there are significant differences which arise...... mainly from the physical nature of self-reconfigurable robots as opposed to the virtual nature of lattice automata. The problems resulting from these differences are mutual exclusion, handling motion constraints of modules, and unrealistic assumption about global, spatial orientation. Despite...... these problems the self-reconfigurable robot community has successfully applied lattice automata to simple control problems. However, for more complex problems hybrid solutions based on lattice automata and distributed algorithms are used. Hence, lattice automata have shown to have potential for the control...

Adaptive Control Methods for Soft Robots

Data.gov (United States)

National Aeronautics and Space Administration — I propose to develop methods for soft and inflatable robots that will allow the control system to adapt and change control parameters based on changing conditions...

Pioneer robot testing and training status

International Nuclear Information System (INIS)

Herndon, J.; Nosovsky, A.; Garin, E.; Goncharov, B.; Neretin, Y.

2001-01-01

The U. S. Department of Energy developed the Pioneer Robot and provided it to the Chornobyl Nuclear Power Plant (ChNPP) within the framework of international technical assistance. At the Pioneer Robot has been transferred to ChNPP ownership for broad use in ChNPP activities related to decommissioning and emergency response, as well as in Unit Shelter. Oak Ridge National Laboratory is working with ChNPP and SLIRT to test the Pioneer Robot operation in a broader scope, and to provide additional operational training

From dV-Trainer to Real Robotic Console: The Limitations of Robotic Skill Training.

Science.gov (United States)

Yang, Kun; Zhen, Hang; Hubert, Nicolas; Perez, Manuela; Wang, Xing Huan; Hubert, Jacques

To investigate operators' performance quality, mental stress, and ergonomic habits through a training curriculum on robotic simulators. Forty volunteers without robotic surgery experience were recruited to practice 2 exercises on a dV-Trainer (dVT) for 14 hours. The simulator software (M-score a ) provided an automatic evaluation of the overall score for the surgeons' performance. Each participant provided a subjective difficulty score (validity to be proven) for each exercise. Their ergonomic habits were evaluated based on the workspace range and armrest load-validated criteria for evaluating the proficiency of using the armrest. They then repeated the same tasks on a da Vinci Surgical Skill Simulator for a final-level test. Their final scores were compared with their initial scores and the scores of 5 experts on the da Vinci Surgical Skill Simulator. A total of 14 hours of training on the dVT significantly improved the surgeons' performance scores to the expert level with a significantly reduced workload, but their ergonomic score was still far from the expert level. Sufficient training on the dVT improves novices' performance, reduces psychological stress, and inculcates better ergonomic habits. Among the evaluated criteria, novices had the most difficulty in achieving expert levels of ergonomic skills. The training benefits of robotic surgery simulators should be determined with quantified variables. The detection of the limitations during robotic training curricula could guide the targeted training and improve the training effect. Copyright © 2017. Published by Elsevier Inc.

Trunk robot rehabilitation training with active stepping reorganizes and enriches trunk motor cortex representations in spinal transected rats.

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Oza, Chintan S; Giszter, Simon F

2015-05-06

Trunk motor control is crucial for postural stability and propulsion after low thoracic spinal cord injury (SCI) in animals and humans. Robotic rehabilitation aimed at trunk shows promise in SCI animal models and patients. However, little is known about the effect of SCI and robot rehabilitation of trunk on cortical motor representations. We previously showed reorganization of trunk motor cortex after adult SCI. Non-stepping training also exacerbated some SCI-driven plastic changes. Here we examine effects of robot rehabilitation that promotes recovery of hindlimb weight support functions on trunk motor cortex representations. Adult rats spinal transected as neonates (NTX rats) at the T9/10 level significantly improve function with our robot rehabilitation paradigm, whereas treadmill-only trained do not. We used intracortical microstimulation to map motor cortex in two NTX groups: (1) treadmill trained (control group); and (2) robot-assisted treadmill trained (improved function group). We found significant robot rehabilitation-driven changes in motor cortex: (1) caudal trunk motor areas expanded; (2) trunk coactivation at cortex sites increased; (3) richness of trunk cortex motor representations, as examined by cumulative entropy and mutual information for different trunk representations, increased; (4) trunk motor representations in the cortex moved toward more normal topography; and (5) trunk and forelimb motor representations that SCI-driven plasticity and compensations had caused to overlap were segregated. We conclude that effective robot rehabilitation training induces significant reorganization of trunk motor cortex and partially reverses some plastic changes that may be adaptive in non-stepping paraplegia after SCI. Copyright © 2015 the authors 0270-6474/15/357174-16$15.00/0.

Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics.

Science.gov (United States)

Morone, Giovanni; Paolucci, Stefano; Cherubini, Andrea; De Angelis, Domenico; Venturiero, Vincenzo; Coiro, Paola; Iosa, Marco

2017-01-01

In this review, we give a brief outline of robot-mediated gait training for stroke patients, as an important emerging field in rehabilitation. Technological innovations are allowing rehabilitation to move toward more integrated processes, with improved efficiency and less long-term impairments. In particular, robot-mediated neurorehabilitation is a rapidly advancing field, which uses robotic systems to define new methods for treating neurological injuries, especially stroke. The use of robots in gait training can enhance rehabilitation, but it needs to be used according to well-defined neuroscientific principles. The field of robot-mediated neurorehabilitation brings challenges to both bioengineering and clinical practice. This article reviews the state of the art (including commercially available systems) and perspectives of robotics in poststroke rehabilitation for walking recovery. A critical revision, including the problems at stake regarding robotic clinical use, is also presented.

Performance adaptive training control strategy for recovering wrist movements in stroke patients: a preliminary, feasibility study

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Sandini Giulio

2009-12-01

Full Text Available Abstract Background In the last two decades robot training in neuromotor rehabilitation was mainly focused on shoulder-elbow movements. Few devices were designed and clinically tested for training coordinated movements of the wrist, which are crucial for achieving even the basic level of motor competence that is necessary for carrying out ADLs (activities of daily life. Moreover, most systems of robot therapy use point-to-point reaching movements which tend to emphasize the pathological tendency of stroke patients to break down goal-directed movements into a number of jerky sub-movements. For this reason we designed a wrist robot with a range of motion comparable to that of normal subjects and implemented a self-adapting training protocol for tracking smoothly moving targets in order to facilitate the emergence of smoothness in the motor control patterns and maximize the recovery of the normal RoM (range of motion of the different DoFs (degrees of Freedom. Methods The IIT-wrist robot is a 3 DoFs light exoskeleton device, with direct-drive of each DoF and a human-like range of motion for Flexion/Extension (FE, Abduction/Adduction (AA and Pronation/Supination (PS. Subjects were asked to track a variable-frequency oscillating target using only one wrist DoF at time, in such a way to carry out a progressive splinting therapy. The RoM of each DoF was angularly scanned in a staircase-like fashion, from the "easier" to the "more difficult" angular position. An Adaptive Controller evaluated online performance parameters and modulated both the assistance and the difficulty of the task in order to facilitate smoother and more precise motor command patterns. Results Three stroke subjects volunteered to participate in a preliminary test session aimed at verify the acceptability of the device and the feasibility of the designed protocol. All of them were able to perform the required task. The wrist active RoM of motion was evaluated for each patient at the

Short-term locomotor adaptation to a robotic ankle exoskeleton does not alter soleus Hoffmann reflex amplitude.

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Kao, Pei-Chun; Lewis, Cara L; Ferris, Daniel P

2010-07-26

To improve design of robotic lower limb exoskeletons for gait rehabilitation, it is critical to identify neural mechanisms that govern locomotor adaptation to robotic assistance. Previously, we demonstrated soleus muscle recruitment decreased by approximately 35% when walking with a pneumatically-powered ankle exoskeleton providing plantar flexor torque under soleus proportional myoelectric control. Since a substantial portion of soleus activation during walking results from the stretch reflex, increased reflex inhibition is one potential mechanism for reducing soleus recruitment when walking with exoskeleton assistance. This is clinically relevant because many neurologically impaired populations have hyperactive stretch reflexes and training to reduce the reflexes could lead to substantial improvements in their motor ability. The purpose of this study was to quantify soleus Hoffmann (H-) reflex responses during powered versus unpowered walking. We tested soleus H-reflex responses in neurologically intact subjects (n=8) that had trained walking with the soleus controlled robotic ankle exoskeleton. Soleus H-reflex was tested at the mid and late stance while subjects walked with the exoskeleton on the treadmill at 1.25 m/s, first without power (first unpowered), then with power (powered), and finally without power again (second unpowered). We also collected joint kinematics and electromyography. When the robotic plantar flexor torque was provided, subjects walked with lower soleus electromyographic (EMG) activation (27-48%) and had concomitant reductions in H-reflex amplitude (12-24%) compared to the first unpowered condition. The H-reflex amplitude in proportion to the background soleus EMG during powered walking was not significantly different from the two unpowered conditions. These findings suggest that the nervous system does not inhibit the soleus H-reflex in response to short-term adaption to exoskeleton assistance. Future studies should determine if the

Robot-assisted training of the kinesthetic sense: enhancing proprioception after stroke.

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De Santis, Dalia; Zenzeri, Jacopo; Casadio, Maura; Masia, Lorenzo; Riva, Assunta; Morasso, Pietro; Squeri, Valentina

2014-01-01

Proprioception has a crucial role in promoting or hindering motor learning. In particular, an intact position sense strongly correlates with the chances of recovery after stroke. A great majority of neurological patients present both motor dysfunctions and impairments in kinesthesia, but traditional robot and virtual reality training techniques focus either in recovering motor functions or in assessing proprioceptive deficits. An open challenge is to implement effective and reliable tests and training protocols for proprioception that go beyond the mere position sense evaluation and exploit the intrinsic bidirectionality of the kinesthetic sense, which refers to both sense of position and sense of movement. Modulated haptic interaction has a leading role in promoting sensorimotor integration, and it is a natural way to enhance volitional effort. Therefore, we designed a preliminary clinical study to test a new proprioception-based motor training technique for augmenting kinesthetic awareness via haptic feedback. The feedback was provided by a robotic manipulandum and the test involved seven chronic hemiparetic subjects over 3 weeks. The protocol included evaluation sessions that consisted of a psychometric estimate of the subject's kinesthetic sensation, and training sessions, in which the subject executed planar reaching movements in the absence of vision and under a minimally assistive haptic guidance made by sequences of graded force pulses. The bidirectional haptic interaction between the subject and the robot was optimally adapted to each participant in order to achieve a uniform task difficulty over the workspace. All the subjects consistently improved in the perceptual scores as a consequence of training. Moreover, they could minimize the level of haptic guidance in time. Results suggest that the proposed method is effective in enhancing kinesthetic acuity, but the level of impairment may affect the ability of subjects to retain their improvement in time.

Robot-assisted training of the kinesthetic sense: enhancing proprioception after stroke

Directory of Open Access Journals (Sweden)

Dalia eDe Santis

2015-01-01

Full Text Available Proprioception has a crucial role in promoting or hindering motor learning. In particular, an intact position sense strongly correlates with the chances of recovery after stroke. A great majority of neurological patients present both motor dysfunctions and impairments in kinesthesia, but traditional robot and virtual reality training techniques focus either in recovering motor functions or in assessing proprioceptive deficits. An open challenge is to implement effective and reliable tests and training protocols for proprioception that go beyond the mere position sense evaluation and exploit the intrinsic bidirectionality of the kinesthetic sense, which refers to both sense of position and sense of movement. Modulated haptic interaction has a leading role in promoting sensorimotor integration and it is a natural way to enhance volitional effort. Therefore, we designed a preliminary clinical study to test a new proprioception-based motor training technique for augmenting kinesthetic awareness via haptic feedback. The feedback was provided by a robotic manipulandum and the test involved 7 chronic hemiparetic subjects over three weeks. The protocol included evaluation sessions, that consisted of a psychometric estimate of the subject’s kinesthetic sensation, and training sessions, in which the subject executed planar reaching movements in the absence of vision and under a minimally assistive haptic guidance made by sequences of graded force pulses. The bidirectional haptic interaction between the subject and the robot was optimally adapted to each participant in order to achieve a uniform task difficulty over the workspace. All the subjects consistently improved in the perceptual scores as a consequence of training. Moreover, they could minimize the level of haptic guidance in time. Results suggest that the proposed method is effective in enhancing kinesthetic acuity, but the level of impairment may affect the ability of subjects to retain their

Combining Environment-Driven Adaptation and Task-Driven Optimisation in Evolutionary Robotics

NARCIS (Netherlands)

Haasdijk, E.W.; Bredeche, Nicolas; Eiben, A.E.

2014-01-01

Embodied evolutionary robotics is a sub-field of evolutionary robotics that employs evolutionary algorithms on the robotic hardware itself, during the operational period, i.e., in an on-line fashion. This enables robotic systems that continuously adapt, and are therefore capable of (re-)adjusting

Self-Imitation and Environmental Scaffolding for Robot Teaching

Directory of Open Access Journals (Sweden)

Joe Saunders

2007-03-01

Full Text Available Imitative learning and learning by observation are social mechanisms that allow a robot to acquire knowledge from a human or another robot. However to be able to obtain skills in this way the robot faces many complex issues, one of which is that of finding solutions to the correspondence problem. Evolutionary predecessors to observational imitation may have been self-imitation where an agent avoids the complexities of the correspondence problem by learning and replicating actions it has experienced through the manipulation of its body. We investigate how a robotic control and teaching system using self-imitation can be constructed with reference to psychological models of motor control and ideas from social scaffolding seen in animals. Within these scaffolded environments sets of competencies can be built by constructing hierarchical state/action memory maps of the robot's interaction within that environment. The scaffolding process provides a mechanism to enable learning to be scaled up. The resulting system allows a human trainer to teach a robot new skills and modify skills that the robot may possess. Additionally the system allows the robot to notify the trainer when it is being taught skills it already has in its repertoire and to direct and focus its attention and sensor resources to relevant parts of the skill being executed. We argue that these mechanisms may be a first step towards the transformation from self-imitation to observational imitation. The system is validated on a physical pioneer robot that is taught using self-imitation to track, follow and point to a patterned object.

Self-imitation and Environmental Scaffolding for Robot Teaching

Directory of Open Access Journals (Sweden)

Chrystopher L. Nehaniv

2008-11-01

Full Text Available Imitative learning and learning by observation are social mechanisms that allow a robot to acquire knowledge from a human or another robot. However to be able to obtain skills in this way the robot faces many complex issues, one of which is that of finding solutions to the correspondence problem. Evolutionary predecessors to observational imitation may have been self-imitation where an agent avoids the complexities of the correspondence problem by learning and replicating actions it has experienced through the manipulation of its body. We investigate how a robotic control and teaching system using self-imitation can be constructed with reference to psychological models of motor control and ideas from social scaffolding seen in animals. Within these scaffolded environments sets of competencies can be built by constructing hierarchical state/action memory maps of the robot's interaction within that environment. The scaffolding process provides a mechanism to enable learning to be scaled up. The resulting system allows a human trainer to teach a robot new skills and modify skills that the robot may possess. Additionally the system allows the robot to notify the trainer when it is being taught skills it already has in its repertoire and to direct and focus its attention and sensor resources to relevant parts of the skill being executed. We argue that these mechanisms may be a first step towards the transformation from self-imitation to observational imitation. The system is validated on a physical pioneer robot that is taught using self-imitation to track, follow and point to a patterned object.

Neuromorphic Audio-Visual Sensor Fusion on a Sound-Localising Robot

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Vincent Yue-Sek Chan

2012-02-01

Full Text Available This paper presents the first robotic system featuring audio-visual sensor fusion with neuromorphic sensors. We combine a pair of silicon cochleae and a silicon retina on a robotic platform to allow the robot to learn sound localisation through self-motion and visual feedback, using an adaptive ITD-based sound localisation algorithm. After training, the robot can localise sound sources (white or pink noise in a reverberant environment with an RMS error of 4 to 5 degrees in azimuth. In the second part of the paper, we investigate the source binding problem. An experiment is conducted to test the effectiveness of matching an audio event with a corresponding visual event based on their onset time. The results show that this technique can be quite effective, despite its simplicity.

Disturbance-Estimated Adaptive Backstepping Sliding Mode Control of a Pneumatic Muscles-Driven Ankle Rehabilitation Robot

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Qingsong Ai

2017-12-01

Full Text Available A rehabilitation robot plays an important role in relieving the therapists’ burden and helping patients with ankle injuries to perform more accurate and effective rehabilitation training. However, a majority of current ankle rehabilitation robots are rigid and have drawbacks in terms of complex structure, poor flexibility and lack of safety. Taking advantages of pneumatic muscles’ good flexibility and light weight, we developed a novel two degrees of freedom (2-DOF parallel compliant ankle rehabilitation robot actuated by pneumatic muscles (PMs. To solve the PM’s nonlinear characteristics during operation and to tackle the human-robot uncertainties in rehabilitation, an adaptive backstepping sliding mode control (ABS-SMC method is proposed in this paper. The human-robot external disturbance can be estimated by an observer, who is then used to adjust the robot output to accommodate external changes. The system stability is guaranteed by the Lyapunov stability theorem. Experimental results on the compliant ankle rehabilitation robot show that the proposed ABS-SMC is able to estimate the external disturbance online and adjust the control output in real time during operation, resulting in a higher trajectory tracking accuracy and better response performance especially in dynamic conditions.

Disturbance-Estimated Adaptive Backstepping Sliding Mode Control of a Pneumatic Muscles-Driven Ankle Rehabilitation Robot.

Science.gov (United States)

Ai, Qingsong; Zhu, Chengxiang; Zuo, Jie; Meng, Wei; Liu, Quan; Xie, Sheng Q; Yang, Ming

2017-12-28

A rehabilitation robot plays an important role in relieving the therapists' burden and helping patients with ankle injuries to perform more accurate and effective rehabilitation training. However, a majority of current ankle rehabilitation robots are rigid and have drawbacks in terms of complex structure, poor flexibility and lack of safety. Taking advantages of pneumatic muscles' good flexibility and light weight, we developed a novel two degrees of freedom (2-DOF) parallel compliant ankle rehabilitation robot actuated by pneumatic muscles (PMs). To solve the PM's nonlinear characteristics during operation and to tackle the human-robot uncertainties in rehabilitation, an adaptive backstepping sliding mode control (ABS-SMC) method is proposed in this paper. The human-robot external disturbance can be estimated by an observer, who is then used to adjust the robot output to accommodate external changes. The system stability is guaranteed by the Lyapunov stability theorem. Experimental results on the compliant ankle rehabilitation robot show that the proposed ABS-SMC is able to estimate the external disturbance online and adjust the control output in real time during operation, resulting in a higher trajectory tracking accuracy and better response performance especially in dynamic conditions.

Peculiarities of domestic and foreign experience of teachers preparation to training robotics

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Наталья Александровна Ионкина

2018-12-01

Full Text Available Robotics within the subject “Technology” is included in the curriculum of Russian schools. This fact transforms robotics from the subject of additional education into a full-fledged academic subject of the school curriculum. The introduction of robotics into the curriculum of Russian schools requires significant changes in the system of training teachers who will teach students this discipline. Training of teachers for the training of students in robotics is carried out, both in the framework of programs for the preparation of masters in pedagogical universities, and within the framework of various refresher courses. Different countries carry out such training in different ways. In some countries, the training of teachers of robotics is financed by the state, in others by private initiatives. The mission of most foreign educational organizations is to use the motivational effects of robotics to activate schoolchildren and involve them in STEM-education. Many manufacturing companies not only sell robotic equipment, but also prepare methodological and training materials for the implementation of STEM-education technology, as well as create electronic educational resources, training programs, online lessons, evaluation materials and much more. Teaching teachers and schoolchildren, while it is based on the equipment that produces such companies.

Self-Adaptive Correction of Heading Direction in Stair Climbing for Tracked Mobile Robots Using Visual Servoing Approach

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Ji, Peng; Song, Aiguo; Song, Zimo; Liu, Yuqing; Jiang, Guohua; Zhao, Guopu

2017-02-01

In this paper, we describe a heading direction correction algorithm for a tracked mobile robot. To save hardware resources as far as possible, the mobile robot’s wrist camera is used as the only sensor, which is rotated to face stairs. An ensemble heading deviation detector is proposed to help the mobile robot correct its heading direction. To improve the generalization ability, a multi-scale Gabor filter is used to process the input image previously. Final deviation result is acquired by applying the majority vote strategy on all the classifiers’ results. The experimental results show that our detector is able to enable the mobile robot to correct its heading direction adaptively while it is climbing the stairs.

Controlling patient participation during robot-assisted gait training

Science.gov (United States)

2011-01-01

Background The overall goal of this paper was to investigate approaches to controlling active participation in stroke patients during robot-assisted gait therapy. Although active physical participation during gait rehabilitation after stroke was shown to improve therapy outcome, some patients can behave passively during rehabilitation, not maximally benefiting from the gait training. Up to now, there has not been an effective method for forcing patient activity to the desired level that would most benefit stroke patients with a broad variety of cognitive and biomechanical impairments. Methods Patient activity was quantified in two ways: by heart rate (HR), a physiological parameter that reflected physical effort during body weight supported treadmill training, and by a weighted sum of the interaction torques (WIT) between robot and patient, recorded from hip and knee joints of both legs. We recorded data in three experiments, each with five stroke patients, and controlled HR and WIT to a desired temporal profile. Depending on the patient's cognitive capabilities, two different approaches were taken: either by allowing voluntary patient effort via visual instructions or by forcing the patient to vary physical effort by adapting the treadmill speed. Results We successfully controlled patient activity quantified by WIT and by HR to a desired level. The setup was thereby individually adaptable to the specific cognitive and biomechanical needs of each patient. Conclusion Based on the three successful approaches to controlling patient participation, we propose a metric which enables clinicians to select the best strategy for each patient, according to the patient's physical and cognitive capabilities. Our framework will enable therapists to challenge the patient to more activity by automatically controlling the patient effort to a desired level. We expect that the increase in activity will lead to improved rehabilitation outcome. PMID:21429200

Training robotic surgery in urology: experience and opinions of robot urologists

NARCIS (Netherlands)

Brinkman, W.M.; Schout, B.M.A.; Rietbergen, J.B.; de Vries, A.H.; van der Poel, H.G.; Koldewijn, E.L.; Witjes, JA; Van Merrienboer, J.J.G.

2015-01-01

Background: To answer the research questions: (a) what were the training pathways followed by the first generation of robot urologists; and (b) what are their opinions on the ideal training for the future generation? Methods: Data were gathered with a questionnaire and semi-structured interviews in

Can a virtual reality surgical simulation training provide a self-driven and mentor-free skills learning? Investigation of the practical influence of the performance metrics from the virtual reality robotic surgery simulator on the skill learning and associated cognitive workloads.

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Lee, Gyusung I; Lee, Mija R

2018-01-01

While it is often claimed that virtual reality (VR) training system can offer self-directed and mentor-free skill learning using the system's performance metrics (PM), no studies have yet provided evidence-based confirmation. This experimental study investigated what extent to which trainees achieved their self-learning with a current VR simulator and whether additional mentoring improved skill learning, skill transfer and cognitive workloads in robotic surgery simulation training. Thirty-two surgical trainees were randomly assigned to either the Control-Group (CG) or Experiment-Group (EG). While the CG participants reviewed the PM at their discretion, the EG participants had explanations about PM and instructions on how to improve scores. Each subject completed a 5-week training using four simulation tasks. Pre- and post-training data were collected using both a simulator and robot. Peri-training data were collected after each session. Skill learning, time spent on PM (TPM), and cognitive workloads were compared between groups. After the simulation training, CG showed substantially lower simulation task scores (82.9 ± 6.0) compared with EG (93.2 ± 4.8). Both groups demonstrated improved physical model tasks performance with the actual robot, but the EG had a greater improvement in two tasks. The EG exhibited lower global mental workload/distress, higher engagement, and a better understanding regarding using PM to improve performance. The EG's TPM was initially long but substantially shortened as the group became familiar with PM. Our study demonstrated that the current VR simulator offered limited self-skill learning and additional mentoring still played an important role in improving the robotic surgery simulation training.

Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton.

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Koller, Jeffrey R; Jacobs, Daniel A; Ferris, Daniel P; Remy, C David

2015-11-04

Robotic ankle exoskeletons can provide assistance to users and reduce metabolic power during walking. Our research group has investigated the use of proportional myoelectric control for controlling robotic ankle exoskeletons. Previously, these controllers have relied on a constant gain to map user's muscle activity to actuation control signals. A constant gain may act as a constraint on the user, so we designed a controller that dynamically adapts the gain to the user's myoelectric amplitude. We hypothesized that an adaptive gain proportional myoelectric controller would reduce metabolic energy expenditure compared to walking with the ankle exoskeleton unpowered because users could choose their preferred control gain. We tested eight healthy subjects walking with the adaptive gain proportional myoelectric controller with bilateral ankle exoskeletons. The adaptive gain was updated each stride such that on average the user's peak muscle activity was mapped to maximal power output of the exoskeleton. All subjects participated in three identical training sessions where they walked on a treadmill for 50 minutes (30 minutes of which the exoskeleton was powered) at 1.2 ms(-1). We calculated and analyzed metabolic energy consumption, muscle recruitment, inverse kinematics, inverse dynamics, and exoskeleton mechanics. Using our controller, subjects achieved a metabolic reduction similar to that seen in previous work in about a third of the training time. The resulting controller gain was lower than that seen in previous work (β=1.50±0.14 versus a constant β=2). The adapted gain allowed users more total ankle joint power than that of unassisted walking, increasing ankle power in exchange for a decrease in hip power. Our findings indicate that humans prefer to walk with greater ankle mechanical power output than their unassisted gait when provided with an ankle exoskeleton using an adaptive controller. This suggests that robotic assistance from an exoskeleton can allow

Increased reward in ankle robotics training enhances motor control and cortical efficiency in stroke.

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Goodman, Ronald N; Rietschel, Jeremy C; Roy, Anindo; Jung, Brian C; Diaz, Jason; Macko, Richard F; Forrester, Larry W

2014-01-01

Robotics is rapidly emerging as a viable approach to enhance motor recovery after disabling stroke. Current principles of cognitive motor learning recognize a positive relationship between reward and motor learning. Yet no prior studies have established explicitly whether reward improves the rate or efficacy of robotics-assisted rehabilitation or produces neurophysiologic adaptations associated with motor learning. We conducted a 3 wk, 9-session clinical pilot with 10 people with chronic hemiparetic stroke, randomly assigned to train with an impedance-controlled ankle robot (anklebot) under either high reward (HR) or low reward conditions. The 1 h training sessions entailed playing a seated video game by moving the paretic ankle to hit moving onscreen targets with the anklebot only providing assistance as needed. Assessments included paretic ankle motor control, learning curves, electroencephalograpy (EEG) coherence and spectral power during unassisted trials, and gait function. While both groups exhibited changes in EEG, the HR group had faster learning curves (p = 0.05), smoother movements (p training may accelerate motor learning for restoring mobility.

A Domain-Specific Language for Programming Self-Reconfigurable Robots

DEFF Research Database (Denmark)

Schultz, Ulrik Pagh; Christensen, David Johan; Støy, Kasper

2007-01-01

. Programming a modular, self-reconfigurable robot is however a complicated task: the robot is essentially a real-time, distributed embedded system, where control and communication paths often are tightly coupled to the current physical configuration of the robot. To facilitate the task of programming modular......, self-reconfigurable robots, we have developed a declarative, role-based language that allows the programmer to define roles and behavior independently of the concrete physical structure of the robot. Roles are compiled to mobile code fragments that distribute themselves over the physical structure...

Design and Development Issues for Educational Robotics Training Camps

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Ucgul, Memet; Cagiltay, Kursat

2014-01-01

The aim of this study is to explore critical design issues for educational robotics training camps and to describe how these factors should be implemented in the development of such camps. For this purpose, two robotics training camps were organized for elementary school students. The first camp had 30 children attendees, and the second had 22. As…

Biofeedback for robotic gait rehabilitation

Directory of Open Access Journals (Sweden)

Colombo Gery

2007-01-01

Full Text Available Abstract Background Development and increasing acceptance of rehabilitation robots as well as advances in technology allow new forms of therapy for patients with neurological disorders. Robot-assisted gait therapy can increase the training duration and the intensity for the patients while reducing the physical strain for the therapist. Optimal training effects during gait therapy generally depend on appropriate feedback about performance. Compared to manual treadmill therapy, there is a loss of physical interaction between therapist and patient with robotic gait retraining. Thus, it is difficult for the therapist to assess the necessary feedback and instructions. The aim of this study was to define a biofeedback system for a gait training robot and test its usability in subjects without neurological disorders. Methods To provide an overview of biofeedback and motivation methods applied in gait rehabilitation, previous publications and results from our own research are reviewed. A biofeedback method is presented showing how a rehabilitation robot can assess the patients' performance and deliver augmented feedback. For validation, three subjects without neurological disorders walked in a rehabilitation robot for treadmill training. Several training parameters, such as body weight support and treadmill speed, were varied to assess the robustness of the biofeedback calculation to confounding factors. Results The biofeedback values correlated well with the different activity levels of the subjects. Changes in body weight support and treadmill velocity had a minor effect on the biofeedback values. The synchronization of the robot and the treadmill affected the biofeedback values describing the stance phase. Conclusion Robot-aided assessment and feedback can extend and improve robot-aided training devices. The presented method estimates the patients' gait performance with the use of the robot's existing sensors, and displays the resulting biofeedback

Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.

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Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu

2015-08-01

This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.

An Ultralightweight and Living Legged Robot.

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Vo Doan, Tat Thang; Tan, Melvin Y W; Bui, Xuan Hien; Sato, Hirotaka

2018-02-01

In this study, we describe the most ultralightweight living legged robot to date that makes it a strong candidate for a search and rescue mission. The robot is a living beetle with a wireless electronic backpack stimulator mounted on its thorax. Inheriting from the living insect, the robot employs a compliant body made of soft actuators, rigid exoskeletons, and flexure hinges. Such structure would allow the robot to easily adapt to any complex terrain due to the benefit of soft interface, self-balance, and self-adaptation of the insect without any complex controller. The antenna stimulation enables the robot to perform not only left/right turning but also backward walking and even cessation of walking. We were also able to grade the turning and backward walking speeds by changing the stimulation frequency. The power required to drive the robot is low as the power consumption of the antenna stimulation is in the order of hundreds of microwatts. In contrast to the traditional legged robots, this robot is of low cost, easy to construct, simple to control, and has ultralow power consumption.

Automation, robotics, and inflight training for manned Mars missions

Science.gov (United States)

Holt, Alan C.

1986-01-01

The automation, robotics, and inflight training requirements of manned Mars missions will be supported by similar capabilities developed for the space station program. Evolutionary space station onboard training facilities will allow the crewmembers to minimize the amount of training received on the ground by providing extensive onboard access to system and experiment malfunction procedures, maintenance procedures, repair procedures, and associated video sequences. Considerable on-the-job training will also be conducted for space station management, mobile remote manipulator operations, proximity operations with the Orbital Maneuvering Vehicle (and later the Orbit Transfer Vehicle), and telerobotics and mobile robots. A similar approach could be used for manned Mars mission training with significant additions such as high fidelity image generation and simulation systems such as holographic projection systems for Mars landing, ascent, and rendezvous training. In addition, a substantial increase in the use of automation and robotics for hazardous and tedious tasks would be expected for Mars mission. Mobile robots may be used to assist in the assembly, test and checkout of the Mars spacecraft, in the handling of nuclear components and hazardous chemical propellent transfer operations, in major spacecraft repair tasks which might be needed (repair of a micrometeroid penetration, for example), in the construction of a Mars base, and for routine maintenance of the base when unmanned.

Robotic laparoscopic surgery: cost and training.

Science.gov (United States)

Amodeo, A; Linares Quevedo, A; Joseph, J V; Belgrano, E; Patel, H R H

2009-06-01

The advantages of minimally invasive surgery are well accepted. Shorter hospital stays, decreased postoperative pain, rapid return to preoperative activity, decreased postoperative ileus, and preserved immune function are among the benefits of the laparoscopic approach. However, the instruments of laparoscopy afford surgeons limited precision and poor ergonomics, and their use is associated with a significant learning curve and the amount of time and energy necessary to develop and maintain such advanced laparoscopic skills is not insignificant. The robotic surgery allows all laparoscopists to perform advanced laparoscopic procedures with greater ease. The potential advantages of surgical robotic systems include making advanced laparoscopic surgical procedures accessible to surgeons who do not have advanced video endoscopic training and broadening the scope of surgical procedures that can be performed using the laparoscopic method. The wristed instruments, x10 magnifications, tremor filtering, scaling of movements and three-dimensional view allow the urologist to perform the intricate dissection and anastomosis with high precision. The robot is not, however, without significant disadvantages as compared with traditional laparoscopy. These include greater expense and consumption of operating room resources such as space and the availability of skilled technical staff, complete elimination of tactile feedback, and more limited options for trocar placement. The current cost of the da Vinci system is $ 1.2 million and annual maintenance is $ 138000. Many studies suggest that depreciation and maintenance costs can be minimised if the number of robotic cases is increased. The high cost of purchasing and maintaining the instruments of the robotic system is one of its many disadvantages. The availability of the robotic systems to only a limited number of centres reduces surgical training opportunities. Hospital administrators and surgeons must define the reasons for

Adaptive Nonlinear Tracking for Robotic Walking

Czech Academy of Sciences Publication Activity Database

Dolinský, Kamil; Čelikovský, Sergej

2012-01-01

Roč. 1, č. 1 (2012), s. 28-35 ISSN 2223-7038 Institutional research plan: CEZ:AV0Z10750506 Keywords : Adaptive control * Kalman filter * walking robots Subject RIV: BC - Control Systems Theory http://lib.physcon.ru/doc?id=9e51935aa5bc

Sistem Kontrol Robot Arm 5 DOF Berbasis Pengenalan Pola Suara Menggunakan Mel-Frequency Cepstrum Coefficients (MFCC dan Adaptive Neuro-Fuzzy Inference System (ANFIS

Directory of Open Access Journals (Sweden)

WS Mada Sanjaya

2016-12-01

Full Text Available Telah dilakukan penelitian yang menggambarkan implementasi pengenalan pola suara untuk mengontrol gerak robot arm 5 DoF dalam mengambil dan menyimpan benda. Dalam penelitian ini metode yang digunakan adalah Mel-Frequency Cepstrum Coefficients (MFCC dan Adaptive Neuro-Fuzzy Inferense System (ANFIS. Metode MFCC digunakan untuk ekstraksi ciri sinyal suara, sedangkan ANFIS digunakan sebagai metode pembelajaran untuk pengenalan pola suara. Pada proses pembelajaran ANFIS data latih yang digunakan sebanyak 6 ciri. Data suara terlatih dan data suara tak terlatih digunakan untuk pengujian sistem pengenalan pola suara. Hasil pengujian menunjukkan tingkat keberhasilan, untuk data suara terlatih sebesar 87,77% dan data tak terlatih sebesar 78,53%. Sistem pengenalan pola suara ini telah diaplikasikan dengan baik untuk mengerakan robot arm 5 DoF berbasis mikrokontroler Arduino. Have been implemented of sound pattern recognition to control 5 DoF of Arm Robot to pick and place an object. In this research used Mel-Frequency Cepstrum Coefficients (MFCC and Adaptive Neuro-Fuzzy Interferense System (ANFIS methods. MFCC method used for features extraction of sound signal, meanwhile ANFIS used to learn sound pattern recognition. On ANFIS method data learning use 6 features. Trained and not trained data used to examine the system of sound pattern identification. The result show the succesfull level, for trained data 87.77% and for not trained data 78.53%. Sound pattern identification system was appliedto controlled 5 DoF arm robot based Arduino microcontroller.

A Case-Study for Life-Long Learning and Adaptation in Cooperative Robot Teams

International Nuclear Information System (INIS)

Parker, L.E.

1999-01-01

While considerable progress has been made in recent years toward the development of multi-robot teams, much work remains to be done before these teams are used widely in real-world applications. Two particular needs toward this end are the development of mechanisms that enable robot teams to generate cooperative behaviors on their own, and the development of techniques that allow these teams to autonomously adapt their behavior over time as the environment or the robot team changes. This paper proposes the use of the Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) application as a rich domain for studying the issues of multi-robot learning and adaptation. After discussing the need for learning and adaptation in multi-robot teams, this paper describes the CMOMMT application and its relevance to multi-robot learning. We discuss the results of the previously- developed, hand-generated algorithm for CMOMMT and the potential for learning that was discovered from the hand-generated approach. We then describe the early work that has been done (by us and others) to generate multi- robot learning techniques for the CMOMMT application, as well as our ongoing research to develop approaches that give performance as good, or better, than the hand-generated approach. The ultimate goal of this research is to develop techniques for multi-robot learning and adaptation in the CMOMMT application domain that will generalize to cooperative robot applications in other domains, thus making the practical use of multi-robot teams in a wide variety of real-world applications much closer to reality

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction

Directory of Open Access Journals (Sweden)

Juan M. Gandarias

2018-02-01

Full Text Available The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM. Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more, with a lower mean of pressure values (up to 72% less than when using a rigid sensor, with a softer grip, which is needed in physical human–robot interaction (pHRI. A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78% with a rigid sensor.

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human-Robot Interaction.

Science.gov (United States)

Gandarias, Juan M; Gómez-de-Gabriel, Jesús M; García-Cerezo, Alfonso J

2018-02-26

The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human-robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor.

People-Centered Development of a Smart Learning Ecosystem of Adaptive Robots

DEFF Research Database (Denmark)

Fischer, Daniel Kjær Bonde; Kristiansen, Jakob; Mariager, Casper

2019-01-01

Robots are currently moving out of the laboratory and company floor into more human and social contexts including care, rehabilitation and education. While those robots are usually envisioned as a kind of social interaction partner, we suggest a different approach, where robots become adaptive...

A discrete-time adaptive control scheme for robot manipulators

Science.gov (United States)

Tarokh, M.

1990-01-01

A discrete-time model reference adaptive control scheme is developed for trajectory tracking of robot manipulators. The scheme utilizes feedback, feedforward, and auxiliary signals, obtained from joint angle measurement through simple expressions. Hyperstability theory is utilized to derive the adaptation laws for the controller gain matrices. It is shown that trajectory tracking is achieved despite gross robot parameter variation and uncertainties. The method offers considerable design flexibility and enables the designer to improve the performance of the control system by adjusting free design parameters. The discrete-time adaptation algorithm is extremely simple and is therefore suitable for real-time implementation. Simulations and experimental results are given to demonstrate the performance of the scheme.

Using Robotics and Game Design to Enhance Children's Self-Efficacy, STEM Attitudes, and Computational Thinking Skills

Science.gov (United States)

Leonard, Jacqueline; Buss, Alan; Gamboa, Ruben; Mitchell, Monica; Fashola, Olatokunbo S.; Hubert, Tarcia; Almughyirah, Sultan

2016-12-01

This paper describes the findings of a pilot study that used robotics and game design to develop middle school students' computational thinking strategies. One hundred and twenty-four students engaged in LEGO® EV3 robotics and created games using Scalable Game Design software. The results of the study revealed students' pre-post self-efficacy scores on the construct of computer use declined significantly, while the constructs of videogaming and computer gaming remained unchanged. When these constructs were analyzed by type of learning environment, self-efficacy on videogaming increased significantly in the combined robotics/gaming environment compared with the gaming-only context. Student attitudes toward STEM, however, did not change significantly as a result of the study. Finally, children's computational thinking (CT) strategies varied by method of instruction as students who participated in holistic game development (i.e., Project First) had higher CT ratings. This study contributes to the STEM education literature on the use of robotics and game design to influence self-efficacy in technology and CT, while informing the research team about the adaptations needed to ensure project fidelity during the remaining years of the study.

Motor and psychosocial impact of robot-assisted gait training in a real-world rehabilitation setting: A pilot study.

Directory of Open Access Journals (Sweden)

Cira Fundarò

Full Text Available In the last decade robotic devices have been applied in rehabilitation to overcome walking disability in neurologic diseases with promising results. Robot assisted gait training (RAGT using the Lokomat seems not only to improve gait parameters but also the perception of well-being. Data on the psychosocial patient-robot impact are limited, in particular in the real-world of RAGT, in the rehabilitation setting. During rehabilitation training, the Lokomat can be considered an "assistive device for movement". This allowed the use of the Psychosocial Impact of Assistive Device Scale- PIADS to describe patient interaction with the Lokomat. The primary aim of this pilot study was to evaluate the psychosocial impact of the Lokomat in an in-patient rehabilitation setting using the PIADS; secondary aims were to assess whether the psychosocial impact of RAGT is different between pathological sub-groups and if the Lokomat influenced functional variables (Functional Independence Measure scale-FIM and parameters provided by the Lokomat itself. Thirty-nine consecutive patients (69% males, 54.0±18.0 years eligible for Lokomat training, with etiologically heterogeneous walking disabilities (Parkinson's Disease, n = 10; Spinal Cord Injury, n = 21; Ictus Event, n = 8 were enrolled. Patients were assessed with the FIM before and after rehabilitation with Lokomat, and the PIADS was administered after the rehabilitative period with Lokomat. Overall the PIADS score was positive (35.8±21.6, as well as the three sub-scales, pertaining to "ability", "adaptability" and "self-esteem" (17.2±10.4, 8.9±5.5 and 10.1±6.6 respectively with no between-group differences. All patients significantly improved in gait measure and motor FIM scale (difference after-before treatment values: 11.7±9.8 and 11.2±10.3 respectively, increased treadmill speed (0.4 ± 0.2m/s, reduced body weight support (-14.0±9.5% and guidance force (-13.1 ± 10.7%. This pilot study indicates that

An Exploratory Investigation into the Effects of Adaptation in Child-Robot Interaction

Science.gov (United States)

Salter, Tamie; Michaud, François; Létourneau, Dominic

The work presented in this paper describes an exploratory investigation into the potential effects of a robot exhibiting an adaptive behaviour in reaction to a child’s interaction. In our laboratory we develop robotic devices for a diverse range of children that differ in age, gender and ability, which includes children that are diagnosed with cognitive difficulties. As all children vary in their personalities and styles of interaction, it would follow that adaptation could bring many benefits. In this abstract we give our initial examination of a series of trials which explore the effects of a fully autonomous rolling robot exhibiting adaptation (through changes in motion and sound) compared to it exhibiting pre-programmed behaviours. We investigate sensor readings on-board the robot that record the level of ‘interaction’ that the robot receives when a child plays with it and also we discuss the results from analysing video footage looking at the social aspect of the trial.

Development of a self-maintenance radiation-tolerant robot

International Nuclear Information System (INIS)

Shimomura, Y.; Takahashi, H.; Tsuboi, Y.; Komatsu, K.

2004-01-01

The purpose of this research is to develop robot which dose not lose the function in radiation fields, and is able to get self-diagnosis and self-repair in the case of failure. The fundamental operation element and operational process algorithm are discussed. Utilizations of gas-micro electronics, which is easy to handle in comparison with vacuum field and to amplify with high speed by use of electron avalanche, are planed. The fundamental researches on radiation-tolerant robot which is not destructed by cosmic ray fields are carried out. The action of basic logic elements is ascertained. Self-repair type logic operations are considered. The self-repair type logic needs for to diagnosis abnormality of elements intellectually and repair by itself. Module failure diagnosis and repair plan technique based on qualitative inference are placed on the center of self-repair type logic. Self-maintenance robot can be actualized by modularization and divergence processing of diagnosis. (M. Suetake)

Self-Healing and Damage Resilience for Soft Robotics: A Review

Directory of Open Access Journals (Sweden)

R. Adam Bilodeau

2017-10-01

Full Text Available Advances in soft robotics will be crucial to the next generation of robot–human interfaces. Soft material systems embed safety at the material level, providing additional safeguards that will expedite their placement alongside humans and other biological systems. However, in order to function in unpredictable, uncontrolled environments alongside biological systems, soft robotic systems should be as robust in their ability to recover from damage as their biological counterparts. There exists a great deal of work on self-healing materials, particularly polymeric and elastomeric materials that can self-heal through a wide variety of tools and techniques. Fortunately, most emerging soft robotic systems are constructed from polymeric or elastomeric materials, so this work can be of immediate benefit to the soft robotics community. Though the field of soft robotics is still nascent as a whole, self-healing and damage resilient systems are beginning to be incorporated into three key support pillars that are enabling the future of soft robotics: actuators, structures, and sensors. This article reviews the state-of-the-art in damage resilience and self-healing materials and devices as applied to these three pillars. This review also discusses future applications for soft robots that incorporate self-healing capabilities.

Implementation of robust adaptive control for robotic manipulator using TMS320C30

International Nuclear Information System (INIS)

Han, S. H.

1996-01-01

A new adaptive digital control scheme for the robotic manipulator is proposed in this paper. Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion for robotic manipulators. In the proposed scheme, adaptation laws are derived from the improved Lyapunov second stability analysis based on the adaptive feedforward and feedback controller and PI type time-varying control elements. The control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot. (author)

Current state of virtual reality simulation in robotic surgery training: a review.

Science.gov (United States)

Bric, Justin D; Lumbard, Derek C; Frelich, Matthew J; Gould, Jon C

2016-06-01

Worldwide, the annual number of robotic surgical procedures continues to increase. Robotic surgical skills are unique from those used in either open or laparoscopic surgery. The acquisition of a basic robotic surgical skill set may be best accomplished in the simulation laboratory. We sought to review the current literature pertaining to the use of virtual reality (VR) simulation in the acquisition of robotic surgical skills on the da Vinci Surgical System. A PubMed search was conducted between December 2014 and January 2015 utilizing the following keywords: virtual reality, robotic surgery, da Vinci, da Vinci skills simulator, SimSurgery Educational Platform, Mimic dV-Trainer, and Robotic Surgery Simulator. Articles were included if they were published between 2007 and 2015, utilized VR simulation for the da Vinci Surgical System, and utilized a commercially available VR platform. The initial search criteria returned 227 published articles. After all inclusion and exclusion criteria were applied, a total of 47 peer-reviewed manuscripts were included in the final review. There are many benefits to utilizing VR simulation for robotic skills acquisition. Four commercially available simulators have been demonstrated to be capable of assessing robotic skill. Three of the four simulators demonstrate the ability of a VR training curriculum to improve basic robotic skills, with proficiency-based training being the most effective training style. The skills obtained on a VR training curriculum are comparable with those obtained on dry laboratory simulation. The future of VR simulation includes utilization in assessment for re-credentialing purposes, advanced procedural-based training, and as a warm-up tool prior to surgery.

Robot-Assisted Training for People With Spinal Cord Injury: A Meta-Analysis.

Science.gov (United States)

Cheung, Eddy Y Y; Ng, Thomas K W; Yu, Kevin K K; Kwan, Rachel L C; Cheing, Gladys L Y

2017-11-01

To investigate the effects of robot-assisted training on the recovery of people with spinal cord injury (SCI). Randomized controlled trials (RCTs) or quasi-RCTs involving people with SCI that compared robot-assisted upper limbs or lower limbs training with a control of other treatment approach or no treatment. We included studies involving people with complete or incomplete SCIs. We searched MEDLINE, CINAHL, Cochrane Central Register of Controlled Trials (Cochrane Library), and Embase to August 2016. Bibliographies of relevant articles on the effect of body-weight-supported treadmill training on subjects with SCI were screened to avoid missing relevant articles from the search of databases. All kinds of objective assessments concerning physical ability, mobility, and/or functional ability were included. Assessments could be clinical tests (ie, 6-minute walk test, FIM) or laboratory tests (ie, gait analysis). Subjective outcome measures were excluded from this review. Eleven RCT studies involving 443 subjects were included in the study. Meta-analysis was performed on the included studies. Walking independence (3.73; 95% confidence interval [CI], -4.92 to -2.53; P<.00001; I 2 =38%) and endurance (53.32m; 95% CI, -73.15 to -33.48; P<.00001; I 2 =0%) were found to have better improvement in robot-assisted training groups. Lower limb robot-assisted training was also found to be as effective as other types of body-weight-supported training. There is a lack of upper limb robot-assisted training studies; therefore, performing a meta-analysis was not possible. Robot-assisted training is an adjunct therapy for physical and functional recovery for patients with SCI. Future high-quality studies are warranted to investigate the effects of robot-assisted training on functional and cardiopulmonary recovery of patients with SCI. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Sambot II: A self-assembly modular swarm robot

Science.gov (United States)

Zhang, Yuchao; Wei, Hongxing; Yang, Bo; Jiang, Cancan

2018-04-01

The new generation of self-assembly modular swarm robot Sambot II, based on the original generation of self-assembly modular swarm robot Sambot, adopting laser and camera module for information collecting, is introduced in this manuscript. The visual control algorithm of Sambot II is detailed and feasibility of the algorithm is verified by the laser and camera experiments. At the end of this manuscript, autonomous docking experiments of two Sambot II robots are presented. The results of experiments are showed and analyzed to verify the feasibility of whole scheme of Sambot II.

Hybrid gait training with an overground robot for people with incomplete spinal cord injury: a pilot study.

Science.gov (United States)

Del-Ama, Antonio J; Gil-Agudo, Angel; Pons, José L; Moreno, Juan C

2014-01-01

Locomotor training has proved to provide beneficial effect in terms of mobility in incomplete paraplegic patients. Neuroprosthetic technology can contribute to increase the efficacy of a training paradigm in the promotion of a locomotor pattern. Robotic exoskeletons can be used to manage the unavoidable loss of performance of artificially driven muscles. Hybrid exoskeletons blend complementary robotic and neuro-prosthetic technologies. The aim of this pilot study was to determine the effects of hybrid gait training in three case studies with persons with incomplete spinal cord injury (iSCI) in terms of locomotion performance during assisted gait, patient-robot adaptations, impact on ambulation and assessment of lower limb muscle strength and spasticity. Participants with iSCI received interventions with a hybrid bilateral exoskeleton for 4 days. Assessment of gait function revealed that patients improved the 6 min and 10 m walking tests after the intervention, and further improvements were observed 1 week after the intervention. Muscle examination revealed improvements in knee and hip sagittal muscle balance scores and decreased score in ankle extensor balance. It is concluded that improvements in biomechanical function of the knee joint after the tested overground hybrid gait trainer are coherent with improvements in gait performance.

Sociable mobile robots through self-maintained energy

DEFF Research Database (Denmark)

Ngo, Trung Dung; Schiøler, Henrik

2006-01-01

society, collecting and sharing are experimentally recognized as the highest property. This paper issues an approach to sociable robots using self-maintained energy in robot society, which is naturally inspired from swarm behavior of honey-bee and ant. Typically, autonomous mobile robots are usually......Research of sociable robots has emphasized interaction and coordination of mobile robots with inspiration from natural behavior of birds, insects, and fish: flocking, foraging, collecting, sharing and so forth. However, the animal behaviors are looking for food towards survival. In an animal...... equipped with a finite energy, thus they can operate in a finite time. To overcome the limitation, we describe practical deployment of a group of mobile robot with the possibility of carrying and exchanging fuel, e.g. battery to other robots. Early implementation that includes modular hardware and control...

Human-Robot Teaming: From Space Robotics to Self-Driving Cars

Science.gov (United States)

Fong, Terry

2017-01-01

In this talk, I describe how NASA Ames has been developing and testing robots for space exploration. In our research, we have focused on studying how human-robot teams can increase the performance, reduce the cost, and increase the success of space missions. A key tenet of our work is that humans and robots should support one another in order to compensate for limitations of manual control and autonomy. This principle has broad applicability beyond space exploration. Thus, I will conclude by discussing how we have worked with Nissan to apply our methods to self-driving cars, enabling humans to support autonomous vehicles operating in unpredictable and difficult situations.

A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot

Directory of Open Access Journals (Sweden)

Xiangfeng Zeng

2017-01-01

Full Text Available Objective. This study aims to establish a steady-state visual evoked potential- (SSVEP- based passive training protocol on an ankle rehabilitation robot and validate its feasibility. Method. This paper combines SSVEP signals and the virtual reality circumstance through constructing information transmission loops between brains and ankle robots. The robot can judge motion intentions of subjects and trigger the training when subjects pay their attention on one of the four flickering circles. The virtual reality training circumstance provides real-time visual feedback of ankle rotation. Result. All five subjects succeeded in conducting ankle training based on the SSVEP-triggered training strategy following their motion intentions. The lowest success rate is 80%, and the highest one is 100%. The lowest information transfer rate (ITR is 11.5 bits/min when the biggest one of the robots for this proposed training is set as 24 bits/min. Conclusion. The proposed training strategy is feasible and promising to be combined with a robot for ankle rehabilitation. Future work will focus on adopting more advanced data process techniques to improve the reliability of intention detection and investigating how patients respond to such a training strategy.

Can robotic surgery be done efficiently while training residents?

Science.gov (United States)

Honaker, Michael Drew; Paton, Beverly L; Stefanidis, Dimitrios; Schiffern, Lynnette M

2015-01-01

Robotic surgery is a rapidly growing area in surgery. In an era of emphasis on cost reduction, the question becomes how do you train residents in robotic surgery? The aim of this study was to determine if there was a difference in operative time and complications when comparing general surgery residents learning robotic cholecystectomies to those learning standard laparoscopic cholecystectomies. A retrospective analysis of adult patients undergoing robotic and laparoscopic cholecystectomy by surgical residents between March 2013 and February 2014 was conducted. Demographic data, operative factors, length of stay (LOS), and complications were examined. Univariate and multivariate analyses were performed. The significance was set at p robotic cholecystectomy group and 40 in the laparoscopic group). Age, diagnosis, and American Society of Anesthesiologists score were not significantly different between groups. There was only 1 complication in the standard laparoscopic group in which a patient had to be taken back to surgery because of an incarcerated port site. LOS was significantly higher in the standard laparoscopic group (mean = 2.28) than in the robotic group (mean = 0.56; p robotic group (mean = 97.00 minutes; p = 0.4455). When intraoperative cholangiogram was evaluated, OR time was shorter in the robotic group. Robotic training in general surgery residency does not amount to extra OR time. LOS in our study was significantly longer in the standard laparoscopic group. Copyright © 2015 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Adaptation of the IBM ECR [electric cantilever robot] robot to plutonium processing applications

International Nuclear Information System (INIS)

Armantrout, G.A.; Pedrotti, L.R.; Halter, E.A.; Crossfield, M.

1990-12-01

The changing regulatory climate in the US is adding increasing incentive to reduce operator dose and TRU waste for DOE plutonium processing operations. To help achieve that goal the authors have begun adapting a small commercial overhead gantry robot, the IBM electric cantilever robot (ECR), to plutonium processing applications. Steps are being taken to harden this robot to withstand the dry, often abrasive, environment within a plutonium glove box and to protect the electronic components against alpha radiation. A mock-up processing system for the reduction of the oxide to a metal was prepared and successfully demonstrated. Design of a working prototype is now underway using the results of this mock-up study. 7 figs., 4 tabs

Sociable Robots Through Self-Maintained Energy

Directory of Open Access Journals (Sweden)

Trung Dung Ngo

2006-12-01

Full Text Available Research of autonomous mobile robots has mostly emphasized interaction and coordination that are natually inspired from biological behavior of birds, insects, and fish: flocking, foraging, collecting, and sharing. However, most research has been only focused on autonomous behaviors in order to perform robots like animals, whereas it is lacked of determinant to those behaviours: energy. Approaching to clusted amimal and the higher, collective and sharing food among individuals are major activity to keep society being. This paper issues an approach to sociable robots using self-maintained energy in cooperative mobile robots, which is dominantly inspired from swarm behavior of collecting and sharing food of honey-bee and ant. Autonomous mobile robots are usually equipped with a finite energy, thus they can operate in a finite time. To overcome the finitude, we describe practical deployment of mobile robots that are capable of carrying and exchanging fuel to other robots. Mechanism implementation including modular hardware and control architecture to demonstrate the capabicities of the approach is presented. Subsequently, the battery exchange algorithm basically based on probabilistic modeling of total energy on each robot located in its local vicinity is described. The paper is concluded with challenging works of chain of mobile robots, rescue, repair, and relation of heterogeneous robots.

Sociable Robots through Self-maintained Energy

Directory of Open Access Journals (Sweden)

Henrik Schioler

2008-11-01

Full Text Available Research of autonomous mobile robots has mostly emphasized interaction and coordination that are natually inspired from biological behavior of birds, insects, and fish: flocking, foraging, collecting, and sharing. However, most research has been only focused on autonomous behaviors in order to perform robots like animals, whereas it is lacked of determinant to those behaviours: energy. Approaching to clusted amimal and the higher, collective and sharing food among individuals are major activity to keep society being. This paper issues an approach to sociable robots using self-maintained energy in cooperative mobile robots, which is dominantly inspired from swarm behavior of collecting and sharing food of honey-bee and ant. Autonomous mobile robots are usually equipped with a finite energy, thus they can operate in a finite time. To overcome the finitude, we describe practical deployment of mobile robots that are capable of carrying and exchanging fuel to other robots. Mechanism implementation including modular hardware and control architecture to demonstrate the capabicities of the approach is presented. Subsequently, the battery exchange algorithm basically based on probabilistic modeling of total energy on each robot located in its local vicinity is described. The paper is concluded with challenging works of chain of mobile robots, rescue, repair, and relation of heterogeneous robots.

Self-repairing control for damaged robotic manipulators

International Nuclear Information System (INIS)

Eisler, G.R.; Robinett, R.D.; Dohrmann, C.R.; Driessen, B.J.

1997-03-01

Algorithms have been developed allowing operation of robotic systems under damaged conditions. Specific areas addressed were optimal sensor location, adaptive nonlinear control, fault-tolerant robot design, and dynamic path-planning. A seven-degree-of-freedom, hydraulic manipulator, with fault-tolerant joint design was also constructed and tested. This report completes this project which was funded under the Laboratory Directed Research and Development program

A Lower Limb Rehabilitation Robot in Sitting Position with a Review of Training Activities.

Science.gov (United States)

Eiammanussakul, Trinnachoke; Sangveraphunsiri, Viboon

2018-01-01

Robots for stroke rehabilitation at the lower limbs in sitting/lying position have been developed extensively. Some of them have been applied in clinics and shown the potential of the recovery of poststroke patients who suffer from hemiparesis. These robots were developed to provide training at different joints of lower limbs with various activities and modalities. This article reviews the training activities that were realized by rehabilitation robots in literature, in order to offer insights for developing a novel robot suitable for stroke rehabilitation. The control system of the lower limb rehabilitation robot in sitting position that was introduced in the previous work is discussed in detail to demonstrate the behavior of the robot while training a subject. The nonlinear impedance control law, based on active assistive control strategy, is able to define the response of the robot with more specifications while the passivity property and the robustness of the system is verified. A preliminary experiment is conducted on a healthy subject to show that the robot is able to perform active assistive exercises with various training activities and assist the subject to complete the training with desired level of assistance.

A Lower Limb Rehabilitation Robot in Sitting Position with a Review of Training Activities

Directory of Open Access Journals (Sweden)

Trinnachoke Eiammanussakul

2018-01-01

Full Text Available Robots for stroke rehabilitation at the lower limbs in sitting/lying position have been developed extensively. Some of them have been applied in clinics and shown the potential of the recovery of poststroke patients who suffer from hemiparesis. These robots were developed to provide training at different joints of lower limbs with various activities and modalities. This article reviews the training activities that were realized by rehabilitation robots in literature, in order to offer insights for developing a novel robot suitable for stroke rehabilitation. The control system of the lower limb rehabilitation robot in sitting position that was introduced in the previous work is discussed in detail to demonstrate the behavior of the robot while training a subject. The nonlinear impedance control law, based on active assistive control strategy, is able to define the response of the robot with more specifications while the passivity property and the robustness of the system is verified. A preliminary experiment is conducted on a healthy subject to show that the robot is able to perform active assistive exercises with various training activities and assist the subject to complete the training with desired level of assistance.

Robots with Internal Models: A Route to Self-Aware and Hence Safer Robots

Science.gov (United States)

Winfield, Alan F. T.

The following sections are included: * Introduction * Internal Models and Self-Awareness * Internal Model-Based Architecture for Robot Safety * The Internal Model * The Consequence Evaluator * The Object Tracker-Localizer * Towards an Ethical Robot * Challenges and Open Questions * Discussion: The Way Forward * Summary and Conclusions

An intention driven hand functions task training robotic system.

Science.gov (United States)

Tong, K Y; Ho, S K; Pang, P K; Hu, X L; Tam, W K; Fung, K L; Wei, X J; Chen, P N; Chen, M

2010-01-01

A novel design of a hand functions task training robotic system was developed for the stroke rehabilitation. It detects the intention of hand opening or hand closing from the stroke person using the electromyography (EMG) signals measured from the hemiplegic side. This training system consists of an embedded controller and a robotic hand module. Each hand robot has 5 individual finger assemblies capable to drive 2 degrees of freedom (DOFs) of each finger at the same time. Powered by the linear actuator, the finger assembly achieves 55 degree range of motion (ROM) at the metacarpophalangeal (MCP) joint and 65 degree range of motion (ROM) at the proximal interphalangeal (PIP) joint. Each finger assembly can also be adjusted to fit for different finger length. With this task training system, stroke subject can open and close their impaired hand using their own intention to carry out some of the daily living tasks.

ADAPTIVE DISTRIBUTION OF A SWARM OF HETEROGENEOUS ROBOTS

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Amanda Prorok

2016-02-01

Full Text Available We present a method that distributes a swarm of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, where each species (robot type is defined by the traits (capabilities that it owns. Our method is based on a continuous abstraction of the swarm at a macroscopic level as we model robots switching between tasks. We formulate an optimization problem that produces an optimal set of transition rates for each species, so that the desired trait distribution is reached as quickly as possible. Since our method is based on the derivation of an analytical gradient, it is very efficient with respect to state-of-the-art methods. Building on this result, we propose a real-time optimization method that enables an online adaptation of transition rates. Our approach is well-suited for real-time applications that rely on online redistribution of large-scale robotic systems.

Comprehensive proficiency-based inanimate training for robotic surgery: reliability, feasibility, and educational benefit.

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Arain, Nabeel A; Dulan, Genevieve; Hogg, Deborah C; Rege, Robert V; Powers, Cathryn E; Tesfay, Seifu T; Hynan, Linda S; Scott, Daniel J

2012-10-01

We previously developed a comprehensive proficiency-based robotic training curriculum demonstrating construct, content, and face validity. This study aimed to assess reliability, feasibility, and educational benefit associated with curricular implementation. Over an 11-month period, 55 residents, fellows, and faculty (robotic novices) from general surgery, urology, and gynecology were enrolled in a 2-month curriculum: online didactics, half-day hands-on tutorial, and self-practice using nine inanimate exercises. Each trainee completed a questionnaire and performed a single proctored repetition of each task before (pretest) and after (post-test) training. Tasks were scored for time and errors using modified FLS metrics. For inter-rater reliability (IRR), three trainees were scored by two raters and analyzed using intraclass correlation coefficients (ICC). Data from eight experts were analyzed using ICC and Cronbach's α to determine test-retest reliability and internal consistency, respectively. Educational benefit was assessed by comparing baseline (pretest) and final (post-test) trainee performance; comparisons used Wilcoxon signed-rank test. Of the 55 trainees that pretested, 53 (96 %) completed all curricular components in 9-17 h and reached proficiency after completing an average of 72 ± 28 repetitions over 5 ± 1 h. Trainees indicated minimal prior robotic experience and "poor comfort" with robotic skills at baseline (1.8 ± 0.9) compared to final testing (3.1 ± 0.8, p reliability was 0.91 (p training for all nine tasks and according to composite scores (548 ± 176 vs. 914 ± 81, p reliability measures, demonstrated feasibility for a large cohort of trainees, and yielded significant educational benefit. Further studies and adoption of this curriculum are encouraged.

A Novel Docking System for Modular Self-Reconfigurable Robots

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Tan Zhang

2017-10-01

Full Text Available Existing self-reconfigurable robots achieve connections and disconnections by a separate drive of the docking system. In this paper, we present a new docking system with which the connections and disconnections are driven by locomotion actuators, without the need for a separate drive, which reduces the weight and the complexity of the modules. This self-reconfigurable robot consists of two types of fundamental modules, i.e., active and passive modules. By the docking system, two types of connections are formed with the fundamental modules, and the docking and undocking actions are achieved through simple control with less sensory feedback. This paper describes the design of the robotic modules, the docking system, the docking process, and the docking force analysis. An experiment is performed to demonstrate the self-reconfigurable robot with the docking system.

Training Revising Based Traversability Analysis of Complex Terrains for Mobile Robot

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Rui Song

2014-05-01

Full Text Available Traversability analysis is one of the core issues in the autonomous navigation for mobile robots to identify the accessible area by the information of sensors on mobile robots. This paper proposed a model to analyze the traversability of complex terrains based on rough sets and training revising. The model described the traversability for mobile robots by traversability cost. Through the experiment, the paper gets the conclusion that traversability analysis model based on rough sets and training revising can be used where terrain features are rich and complex, can effectively handle the unstructured environment, and can provide reliable and effective decision rules in the autonomous navigation for mobile robots.

Semantic Framework for Social Robot Self-Configuration

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Azkune, Gorka; Orduña, Pablo; Laiseca, Xabier; Castillejo, Eduardo; López-de-Ipiña, Diego; Loitxate, Miguel; Azpiazu, Jon

2013-01-01

Healthcare environments, as many other real world environments, present many changing and unpredictable situations. In order to use a social robot in such an environment, the robot has to be prepared to deal with all the changing situations. This paper presents a robot self-configuration approach to overcome suitably the commented problems. The approach is based on the integration of a semantic framework, where a reasoner can take decisions about the configuration of robot services and resources. An ontology has been designed to model the robot and the relevant context information. Besides rules are used to encode human knowledge and serve as policies for the reasoner. The approach has been successfully implemented in a mobile robot, which showed to be more capable of solving situations not pre-designed. PMID:23760085

Robotic surgical education: a collaborative approach to training postgraduate urologists and endourology fellows.

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Mirheydar, Hossein; Jones, Marklyn; Koeneman, Kenneth S; Sweet, Robert M

2009-01-01

Currently, robotic training for inexperienced, practicing surgeons is primarily done vis-à-vis industry and/or society-sponsored day or weekend courses, with limited proctorship opportunities. The objective of this study was to assess the impact of an extended-proctorship program at up to 32 months of follow-up. An extended-proctorship program for robotic-assisted laparoscopic radical prostatectomy was established at our institution. The curriculum consisted of 3 phases: (1) completing an Intuitive Surgical 2-day robotic training course with company representatives; (2) serving as assistant to a trained proctor on 5 to 6 cases; and (3) performing proctored cases up to 1 year until confidence was achieved. Participants were surveyed and asked to evaluate on a 5-point Likert scale their operative experience in robotics and satisfaction regarding their training. Nine of 9 participants are currently performing robotic-assisted laparoscopic radical prostatectomy (RALP) independently. Graduates of our program have performed 477 RALP cases. The mean number of cases performed within phase 3 was 20.1 (range, 5 to 40) prior to independent practice. The program received a rating of 4.2/5 for effectiveness in teaching robotic surgery skills. Our robotic program, with extended proctoring, has led to an outstanding take-rate for disseminating robotic skills in a metropolitan community.

Training and learning robotic surgery, time for a more structured approach: a systematic review

NARCIS (Netherlands)

Schreuder, H. W. R.; Wolswijk, R.; Zweemer, R. P.; Schijven, M. P.; Verheijen, R. H. M.

2012-01-01

Background Robotic assisted laparoscopic surgery is growing rapidly and there is an increasing need for a structured approach to train future robotic surgeons. Objectives To review the literature on training and learning strategies for robotic assisted laparoscopic surgery. Search strategy A

Robotic kidney autotransplantation in a porcine model: a procedure-specific training platform for the simulation of robotic intracorporeal vascular anastomosis.

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Tiong, Ho Yee; Goh, Benjamin Yen Seow; Chiong, Edmund; Tan, Lincoln Guan Lim; Vathsala, Anatharaman

2018-03-31

Robotic-assisted kidney transplantation (RKT) with the Da Vinci (Intuitive, USA) platform has been recently developed to improve outcomes by decreasing surgical site complications and morbidity, especially in obese patients. This potential paradigm shift in the surgical technique of kidney transplantation is performed in only a few centers. For wider adoption of this high stake complex operation, we aimed to develop a procedure-specific simulation platform in a porcine model for the training of robotic intracorporeal vascular anastomosis and evaluating vascular anastomoses patency. This paper describes the requirements and steps developed for the above training purpose. Over a series of four animal ethics' approved experiments, the technique of robotic-assisted laparoscopic autotransplantation of the kidney was developed in Amsterdam live pigs (60-70 kg). The surgery was based around the vascular anastomosis technique described by Menon et al. This non-survival porcine training model is targeted at transplant surgeons with robotic surgery experience. Under general anesthesia, each pig was placed in lateral decubitus position with the placement of one robotic camera port, two robotic 8 mm ports and one assistant port. Robotic docking over the pig posteriorly was performed. The training platform involved the following procedural steps. First, ipsilateral iliac vessel dissection was performed. Second, robotic-assisted laparoscopic donor nephrectomy was performed with in situ perfusion of the kidney with cold Hartmann's solution prior to complete division of the hilar vessels, ureter and kidney mobilization. Thirdly, the kidney was either kept in situ for orthotopic autotransplantation or mobilized to the pelvis and orientated for the vascular anastomosis, which was performed end to end or end to side after vessel loop clamping of the iliac vessels, respectively, using 6/0 Gore-Tex sutures. Following autotransplantation and release of vessel loops, perfusion of the

L-ALLIANCE: a mechanism for adaptive action selection in heterogeneous multi-robot teams

Energy Technology Data Exchange (ETDEWEB)

Parker, L.E.

1995-11-01

In practical applications of robotics, it is usually quite difficult, if not impossible, for the system designer to fully predict the environmental states in which the robots will operate. The complexity of the problem is further increased when dealing with teams of robots which themselves may be incompletely known and characterized in advance. It is thus highly desirable for robot teams to be able to adapt their performance during the mission due to changes in the environment, or to changes in other robot team members. In previous work, we introduced a behavior-based mechanism called the ALLIANCE architecture -- that facilitates the fault tolerant cooperative control of multi-robot teams. However, this previous work did not address the issue of how to dynamically update the control parameters during a mission to adapt to ongoing changes in the environment or in the robot team, and to ensure the efficiency of the collective team actions. In this paper, we address this issue by proposing the L-ALLIANCE mechanism, which defines an automated method whereby robots can use knowledge learned from previous experience to continually improve their collective action selection when working on missions composed of loosely coupled, discrete subtasks. This ability to dynamically update robotic control parameters provides a number of distinct advantages: it alleviates the need for human tuning of control parameters, it facilitates the use of custom-designed multi-robot teams for any given application, it improves the efficiency of the mission performance, and It allows robots to continually adapt their performance over time due to changes in the robot team and/or the environment. We describe the L-ALLIANCE mechanism, present the results of various alternative update strategies we investigated, present the formal model of the L-ALLIANCE mechanism, and present the results of a simple proof of concept implementation on a small team of heterogeneous mobile robots.

Feasibility and safety of early lower limb robot-assisted training in sub-acute stroke patients: a pilot study.

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Gandolfi, Marialuisa; Geroin, Christian; Tomelleri, Christopher; Maddalena, Isacco; Kirilova Dimitrova, Eleonora; Picelli, Alessandro; Smania, Nicola; Waldner, Andreas

2017-12-01

So far, the development of robotic devices for the early lower limb mobilization in the sub-acute phase after stroke has received limited attention. To explore the feasibility of a newly robotic-stationary gait training in sub-acute stroke patients. To report the training effects on lower limb function and muscle activation. A pilot study. Rehabilitation ward. Two sub-acute stroke inpatients and ten age-matched healthy controls were enrolled. Healthy controls served as normative data. Patients underwent 10 robot-assisted training sessions (20 minutes, 5 days/week) in alternating stepping movements (500 repetitions/session) on a hospital bed in addition to conventional rehabilitation. Feasibility outcome measures were compliance, physiotherapist time, and responses to self-report questionnaires. Efficacy outcomes were bilateral lower limb muscle activation pattern as measured by surface electromyography (sEMG), Motricity Index (MI), Medical Research Council (MRC) grade, and Ashworth Scale (AS) scores before and after training. No adverse events occurred. No significant differences in sEMG activity between patients and healthy controls were observed. Post-training improvement in MI and MRC scores, but no significant changes in AS scores, were recorded. Post-treatment sEMG analysis of muscle activation patterns showed a significant delay in rectus femoris offset (P=0.02) and prolonged duration of biceps femoris (P=0.04) compared to pretreatment. The robot-assisted training with our device was feasible and safe. It induced physiological muscle activations pattern in both stroke patients and healthy controls. Full-scale studies are needed to explore its potential role in post-stroke recovery. This robotic device may enrich early rehabilitation in subacute stroke patients by inducing physiological muscle activation patterns. Future studies are warranted to evaluate its effects on promoting restorative mechanisms involved in lower limb recovery after stroke.

Objective assessment in residency-based training for transoral robotic surgery.

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Curry, Martin; Malpani, Anand; Li, Ryan; Tantillo, Thomas; Jog, Amod; Blanco, Ray; Ha, Patrick K; Califano, Joseph; Kumar, Rajesh; Richmon, Jeremy

2012-10-01

To develop a robotic surgery training regimen integrating objective skill assessment for otolaryngology and head and neck surgery trainees consisting of training modules of increasing complexity leading up to procedure-specific training. In particular, we investigated applications of such a training approach for surgical extirpation of oropharyngeal tumors via a transoral approach using the da Vinci robotic system. Prospective blinded data collection and objective evaluation (Objective Structured Assessment of Technical Skills [OSATS]) of three distinct phases using the da Vinci robotic surgical system in an academic university medical engineering/computer science laboratory setting. Between September 2010 and July 2011, eight otolaryngology-head and neck surgery residents and four staff experts from an academic hospital participated in three distinct phases of robotic surgery training involving 1) robotic platform operational skills, 2) set up of the patient side system, and 3) a complete ex vivo surgical extirpation of an oropharyngeal tumor located in the base of tongue. Trainees performed multiple (four) approximately equally spaced training sessions in each stage of the training. In addition to trainees, baseline performance data were obtained for the experts. Each surgical stage was documented with motion and event data captured from the application programming interfaces of the da Vinci system, as well as separate video cameras as appropriate. All data were assessed using automated skill measures of task efficiency and correlated with structured assessment (OSATS and similar Likert scale) from three experts to assess expert and trainee differences and compute automated and expert assessed learning curves. Our data show that such training results in an improved didactic robotic knowledge base and improved clinical efficiency with respect to the set up and console manipulation. Experts (e.g., average OSATS, 25; standard deviation [SD], 3.1; module 1, suturing

Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

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Rong Mei

2017-01-01

Full Text Available This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators.

An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: task training system for stroke rehabilitation.

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Ho, N S K; Tong, K Y; Hu, X L; Fung, K L; Wei, X J; Rong, W; Susanto, E A

2011-01-01

An exoskeleton hand robotic training device is specially designed for persons after stroke to provide training on their impaired hand by using an exoskeleton robotic hand which is actively driven by their own muscle signals. It detects the stroke person's intention using his/her surface electromyography (EMG) signals from the hemiplegic side and assists in hand opening or hand closing functional tasks. The robotic system is made up of an embedded controller and a robotic hand module which can be adjusted to fit for different finger length. Eight chronic stroke subjects had been recruited to evaluate the effects of this device. The preliminary results showed significant improvement in hand functions (ARAT) and upper limb functions (FMA) after 20 sessions of robot-assisted hand functions task training. With the use of this light and portable robotic device, stroke patients can now practice more easily for the opening and closing of their hands at their own will, and handle functional daily living tasks at ease. A video is included together with this paper to give a demonstration of the hand robotic system on chronic stroke subjects and it will be presented in the conference. © 2011 IEEE

Training with a balance exercise assist robot is more effective than conventional training for frail older adults.

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Ozaki, Kenichi; Kondo, Izumi; Hirano, Satoshi; Kagaya, Hitoshi; Saitoh, Eiichi; Osawa, Aiko; Fujinori, Yoichi

2017-11-01

To examine the efficacy of postural strategy training using a balance exercise assist robot (BEAR) as compared with conventional balance training for frail older adults. The present study was designed as a cross-over trial without a washout term. A total of 27 community-dwelling frail or prefrail elderly residents (7 men, 20 women; age range 65-85 years) were selected from a volunteer sample. Two exercises were prepared for interventions: robotic exercise moving the center of gravity by the balance exercise assist robot system; and conventional balance training combining muscle-strengthening exercise, postural strategy training and applied motion exercise. Each exercise was carried out twice a week for 6 weeks. Participants were allocated randomly to either the robotic exercise first group or the conventional balance exercise first group. preferred and maximal gait speeds, tandem gait speeds, timed up-and-go test, functional reach test, functional base of support, center of pressure, and muscle strength of the lower extremities were assessed before and after completion of each exercise program. Robotic exercise achieved significant improvements for tandem gait speed (P = 0.012), functional reach test (P = 0.002), timed up-and-go test (P = 0.023) and muscle strength of the lower extremities (P = 0.001-0.030) compared with conventional exercise. In frail or prefrail older adults, robotic exercise was more effective for improving dynamic balance and lower extremity muscle strength than conventional exercise. These findings suggest that postural strategy training with the balance exercise assist robot is effective to improve the gait instability and muscle weakness often seen in frail older adults. Geriatr Gerontol Int 2017; 17: 1982-1990. © 2017 The Authors. Geriatrics & Gerontology International published by John Wiley & Sons Australia, Ltd on behalf of Japan Geriatrics Society.

Does robotic gait training improve balance in Parkinson's disease? A randomized controlled trial.

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Picelli, Alessandro; Melotti, Camilla; Origano, Francesca; Waldner, Andreas; Gimigliano, Raffaele; Smania, Nicola

2012-09-01

Treadmill training (with or without robotic assistance) has been reported to improve balance skills in patients with Parkinson's disease (PD). However, its effectiveness on postural instability has been evaluated mainly in patients with mild to moderate PD (Hoehn & Yahr stage ≤3). Patients with more severe disease may benefit from robot-assisted gait training performed by the Gait-Trainer GT1, as a harness supports them with their feet placed on motor-driven footplates. The aim of this study was to determine whether robot-assisted gait training could have a positive influence on postural stability in patients with PD at Hoehn & Yahr stage 3-4. Thirty-four patients with PD at Hoehn & Yahr stage 3-4 were randomly assigned into two groups. All patients received twelve, 40-min treatment sessions, three days/week, for four consecutive weeks. The Robotic Training group (n = 17) underwent robot-assisted gait training, while the Physical Therapy group (n = 17) underwent a training program not specifically aimed at improving postural stability. Patients were evaluated before, immediately after and 1-month post-treatment. Primary outcomes were: Berg Balance scale; Nutt's rating. A significant improvement was found after treatment on the Berg Balance Scale and the Nutt's rating in favor of the Robotic Training group (Berg: 43.44 ± 2.73; Nutt: 1.38 ± 0.50) compared to the Physical Therapy group (Berg: 37.27 ± 5.68; Nutt: 2.07 ± 0.59). All improvements were maintained at the 1-month follow-up evaluation. Robot-assisted gait training may improve postural instability in patients with PD at Hoehn & Yahr stage 3-4. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Novel Greeting Selection System for a Culture-Adaptive Humanoid Robot

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Gabriele Trovato

2015-04-01

Full Text Available Robots, especially humanoids, are expected to perform human-like actions and adapt to our ways of communication in order to facilitate their acceptance in human society. Among humans, rules of communication change depending on background culture: greetings are a part of communication in which cultural differences are strong. Robots should adapt to these specific differences in order to communicate effectively, being able to select the appropriate manner of greeting for different cultures depending on the social context. In this paper, we present the modelling of social factors that influence greeting choice, and the resulting novel culture-dependent greeting gesture and words selection system. An experiment with German participants was run using the humanoid robot ARMAR-IIIb. Thanks to this system, the robot, after interacting with Germans, can perform greeting gestures appropriate to German culture in addition to a repertoire of greetings appropriate to Japanese culture.

Hybrid gait training with an overground robot for people with incomplete spinal cord injury: a pilot study

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Antonio J del-Ama

2014-05-01

Full Text Available Locomotor training has proved to provide beneficial effect in terms of mobility in incomplete paraplegic patients. Neuroprosthetic technology can contribute to increase the efficacy of a training paradigm in the promotion of a locomotor pattern. Robotic exoskeletons can be used to manage the unavoidable loss of performance of artificially-driven muscles. Hybrid exoskeletons blend complementary robotic and neuro-prosthetic technologies. The aim of this pilot study was to determine the effects of hybrid gait training in three case studies with persons with incomplete spinal cord injury in terms of locomotion performance during assisted gait, patient-robot adaptations, impact on ambulation and assessment of lower limb muscle strength and spasticity. Participants with incomplete Spinal Cord Injury (SCI received interventions with a hybrid bilateral exoskeleton for 4 days. Assessment of gait function revealed that patients improved the 6 minutes and 10 meters walking tests after the intervention, and further improvements were observed one week after the intervention. Muscle examination revealed improvements in knee and hip sagittal muscle balance scores and decreased score in ankle extensor balance. It is concluded that improvements in biomechanical function of the knee joint after the tested overground hybrid gait trainer are coherent with improvements in gait performance.

Periodic activations of behaviours and emotional adaptation in behaviour-based robotics

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Burattini, Ernesto; Rossi, Silvia

2010-09-01

The possible modulatory influence of motivations and emotions is of great interest in designing robotic adaptive systems. In this paper, an attempt is made to connect the concept of periodic behaviour activations to emotional modulation, in order to link the variability of behaviours to the circumstances in which they are activated. The impact of emotion is studied, described as timed controlled structures, on simple but conflicting reactive behaviours. Through this approach it is shown that the introduction of such asynchronies in the robot control system may lead to an adaptation in the emergent behaviour without having an explicit action selection mechanism. The emergent behaviours of a simple robot designed with both a parallel and a hierarchical architecture are evaluated and compared.

Mathematical model for adaptive control system of ASEA robot at Kennedy Space Center

Science.gov (United States)

Zia, Omar

1989-01-01

The dynamic properties and the mathematical model for the adaptive control of the robotic system presently under investigation at Robotic Application and Development Laboratory at Kennedy Space Center are discussed. NASA is currently investigating the use of robotic manipulators for mating and demating of fuel lines to the Space Shuttle Vehicle prior to launch. The Robotic system used as a testbed for this purpose is an ASEA IRB-90 industrial robot with adaptive control capabilities. The system was tested and it's performance with respect to stability was improved by using an analogue force controller. The objective of this research project is to determine the mathematical model of the system operating under force feedback control with varying dynamic internal perturbation in order to provide continuous stable operation under variable load conditions. A series of lumped parameter models are developed. The models include some effects of robot structural dynamics, sensor compliance, and workpiece dynamics.

Biological Immune System Applications on Mobile Robot for Disabled People

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Songmin Jia

2014-01-01

Full Text Available To improve the service quality of service robots for the disabled, immune system is applied on robot for its advantages such as diversity, dynamic, parallel management, self-organization, and self-adaptation. According to the immune system theory, local environment condition sensed by robot is considered an antigen while robot is regarded as B-cell and possible node as antibody, respectively. Antibody-antigen affinity is employed to choose the optimal possible node to ensure the service robot can pass through the optimal path. The paper details the immune system applications on service robot and gives experimental results.

Actively Perceiving and Responsive Soft Robots Enabled by Self-Powered, Highly Extensible, and Highly Sensitive Triboelectric Proximity- and Pressure-Sensing Skins.

Science.gov (United States)

Lai, Ying-Chih; Deng, Jianan; Liu, Ruiyuan; Hsiao, Yung-Chi; Zhang, Steven L; Peng, Wenbo; Wu, Hsing-Mei; Wang, Xingfu; Wang, Zhong Lin

2018-06-04

Robots that can move, feel, and respond like organisms will bring revolutionary impact to today's technologies. Soft robots with organism-like adaptive bodies have shown great potential in vast robot-human and robot-environment applications. Developing skin-like sensory devices allows them to naturally sense and interact with environment. Also, it would be better if the capabilities to feel can be active, like real skin. However, challenges in the complicated structures, incompatible moduli, poor stretchability and sensitivity, large driving voltage, and power dissipation hinder applicability of conventional technologies. Here, various actively perceivable and responsive soft robots are enabled by self-powered active triboelectric robotic skins (tribo-skins) that simultaneously possess excellent stretchability and excellent sensitivity in the low-pressure regime. The tribo-skins can actively sense proximity, contact, and pressure to external stimuli via self-generating electricity. The driving energy comes from a natural triboelectrification effect involving the cooperation of contact electrification and electrostatic induction. The perfect integration of the tribo-skins and soft actuators enables soft robots to perform various actively sensing and interactive tasks including actively perceiving their muscle motions, working states, textile's dampness, and even subtle human physiological signals. Moreover, the self-generating signals can drive optoelectronic devices for visual communication and be processed for diverse sophisticated uses. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Mergeable nervous systems for robots.

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Mathews, Nithin; Christensen, Anders Lyhne; O'Grady, Rehan; Mondada, Francesco; Dorigo, Marco

2017-09-12

Robots have the potential to display a higher degree of lifetime morphological adaptation than natural organisms. By adopting a modular approach, robots with different capabilities, shapes, and sizes could, in theory, construct and reconfigure themselves as required. However, current modular robots have only been able to display a limited range of hardwired behaviors because they rely solely on distributed control. Here, we present robots whose bodies and control systems can merge to form entirely new robots that retain full sensorimotor control. Our control paradigm enables robots to exhibit properties that go beyond those of any existing machine or of any biological organism: the robots we present can merge to form larger bodies with a single centralized controller, split into separate bodies with independent controllers, and self-heal by removing or replacing malfunctioning body parts. This work takes us closer to robots that can autonomously change their size, form and function.Robots that can self-assemble into different morphologies are desired to perform tasks that require different physical capabilities. Mathews et al. design robots whose bodies and control systems can merge and split to form new robots that retain full sensorimotor control and act as a single entity.

Comparison of two simulation systems to support robotic-assisted surgical training: a pilot study (Swine model).

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Whitehurst, Sabrina V; Lockrow, Ernest G; Lendvay, Thomas S; Propst, Anthony M; Dunlow, Susan G; Rosemeyer, Christopher J; Gobern, Joseph M; White, Lee W; Skinner, Anna; Buller, Jerome L

2015-01-01

To compare the efficacy of simulation-based training between the Mimic dV- Trainer and traditional dry lab da Vinci robot training. A prospective randomized study analyzing the performance of 20 robotics-naive participants. Participants were enrolled in an online da Vinci Intuitive Surgical didactic training module, followed by training in use of the da Vinci standard surgical robot. Spatial ability tests were performed as well. Participants were randomly assigned to 1 of 2 training conditions: performance of 3 Fundamentals of Laparoscopic Surgery dry lab tasks using the da Vinci or performance of 4 dV-Trainer tasks. Participants in both groups performed all tasks to empirically establish proficiency criterion. Participants then performed the transfer task, a cystotomy closure using the daVinci robot on a live animal (swine) model. The performance of robotic tasks was blindly assessed by a panel of experienced surgeons using objective tracking data and using the validated Global Evaluative Assessment of Robotic Surgery (GEARS), a structured assessment tool. No statistically significant difference in surgeon performance was found between the 2 training conditions, dV-Trainer and da Vinci robot. Analysis of a 95% confidence interval for the difference in means (-0.803 to 0.543) indicated that the 2 methods are unlikely to differ to an extent that would be clinically meaningful. Based on the results of this study, a curriculum on the dV- Trainer was shown to be comparable to traditional da Vinci robot training. Therefore, we have identified that training on a virtual reality system may be an alternative to live animal training for future robotic surgeons. Published by Elsevier Inc.

Rapid prototyping framework for robot-assisted training of autistic children

NARCIS (Netherlands)

Kim, Mingyu; Barakova, E.I.; Lourens, T.

2014-01-01

Research in uptake and actual use of robots in socially assistive tasks is rapidly growing. However, practical applications lack behind due to the enormous effort to create meaningful behaviours. This paper describes a rapid prototyping framework for robot-assisted training of children with Autism

Modeling of Two-Wheeled Self-Balancing Robot Driven by DC Gearmotors

Science.gov (United States)

Frankovský, P.; Dominik, L.; Gmiterko, A.; Virgala, I.; Kurylo, P.; Perminova, O.

2017-08-01

This paper is aimed at modelling a two-wheeled self-balancing robot driven by the geared DC motors. A mathematical model consists of two main parts, the model of robot's mechanical structure and the model of the actuator. Linearized equations of motion are derived and the overall model of the two-wheeled self-balancing robot is represented in state-space realization for the purpose of state feedback controller design.

Self-Organized Multi-Camera Network for a Fast and Easy Deployment of Ubiquitous Robots in Unknown Environments

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Canedo-Rodriguez, Adrián; Iglesias, Roberto; Regueiro, Carlos V.; Alvarez-Santos, Victor; Pardo, Xose Manuel

2013-01-01

To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposal. PMID:23271604

Self-organized multi-camera network for a fast and easy deployment of ubiquitous robots in unknown environments.

Science.gov (United States)

Canedo-Rodriguez, Adrián; Iglesias, Roberto; Regueiro, Carlos V; Alvarez-Santos, Victor; Pardo, Xose Manuel

2012-12-27

To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposal.

Robot Guided 'Pen Skill' Training in Children with Motor Difficulties.

Science.gov (United States)

Shire, Katy A; Hill, Liam J B; Snapp-Childs, Winona; Bingham, Geoffrey P; Kountouriotis, Georgios K; Barber, Sally; Mon-Williams, Mark

2016-01-01

Motor deficits are linked to a range of negative physical, social and academic consequences. Haptic robotic interventions, based on the principles of sensorimotor learning, have been shown previously to help children with motor problems learn new movements. We therefore examined whether the training benefits of a robotic system would generalise to a standardised test of 'pen-skills', assessed using objective kinematic measures [via the Clinical Kinematic Assessment Tool, CKAT]. A counterbalanced, cross-over design was used in a group of 51 children (37 male, aged 5-11 years) with manual control difficulties. Improved performance on a novel task using the robotic device could be attributed to the intervention but there was no evidence of generalisation to any of the CKAT tasks. The robotic system appears to have the potential to support motor learning, with the technology affording numerous advantages. However, the training regime may need to target particular manual skills (e.g. letter formation) in order to obtain clinically significant improvements in specific skills such as handwriting.

Induced vibrations increase performance of a winged self-righting robot

Science.gov (United States)

Othayoth, Ratan; Xuan, Qihan; Li, Chen

When upside down, cockroaches can open their wings to dynamically self-right. In this process, an animal often has to perform multiple unsuccessful maneuvers to eventually right, and often flails its legs. Here, we developed a cockroach-inspired winged self-righting robot capable of controlled body vibrations to test the hypothesis that vibrations assist self-righting transitions. Robot body vibrations were induced by an oscillating mass (10% of body mass) and varied by changing oscillation frequency. We discovered that, as the robot's body vibrations increased, righting probability increased, and righting time decreased (P locomotor transitions, but highlights the need for further stochastic modeling to capture the uncertain nature of when righting maneuvers result in successful righting.

Combining environment-driven adaptation and task-driven optimisation in evolutionary robotics.

Science.gov (United States)

Haasdijk, Evert; Bredeche, Nicolas; Eiben, A E

2014-01-01

Embodied evolutionary robotics is a sub-field of evolutionary robotics that employs evolutionary algorithms on the robotic hardware itself, during the operational period, i.e., in an on-line fashion. This enables robotic systems that continuously adapt, and are therefore capable of (re-)adjusting themselves to previously unknown or dynamically changing conditions autonomously, without human oversight. This paper addresses one of the major challenges that such systems face, viz. that the robots must satisfy two sets of requirements. Firstly, they must continue to operate reliably in their environment (viability), and secondly they must competently perform user-specified tasks (usefulness). The solution we propose exploits the fact that evolutionary methods have two basic selection mechanisms-survivor selection and parent selection. This allows evolution to tackle the two sets of requirements separately: survivor selection is driven by the environment and parent selection is based on task-performance. This idea is elaborated in the Multi-Objective aNd open-Ended Evolution (monee) framework, which we experimentally validate. Experiments with robotic swarms of 100 simulated e-pucks show that monee does indeed promote task-driven behaviour without compromising environmental adaptation. We also investigate an extension of the parent selection process with a 'market mechanism' that can ensure equitable distribution of effort over multiple tasks, a particularly pressing issue if the environment promotes specialisation in single tasks.

Combining environment-driven adaptation and task-driven optimisation in evolutionary robotics.

Directory of Open Access Journals (Sweden)

Evert Haasdijk

Full Text Available Embodied evolutionary robotics is a sub-field of evolutionary robotics that employs evolutionary algorithms on the robotic hardware itself, during the operational period, i.e., in an on-line fashion. This enables robotic systems that continuously adapt, and are therefore capable of (re-adjusting themselves to previously unknown or dynamically changing conditions autonomously, without human oversight. This paper addresses one of the major challenges that such systems face, viz. that the robots must satisfy two sets of requirements. Firstly, they must continue to operate reliably in their environment (viability, and secondly they must competently perform user-specified tasks (usefulness. The solution we propose exploits the fact that evolutionary methods have two basic selection mechanisms-survivor selection and parent selection. This allows evolution to tackle the two sets of requirements separately: survivor selection is driven by the environment and parent selection is based on task-performance. This idea is elaborated in the Multi-Objective aNd open-Ended Evolution (monee framework, which we experimentally validate. Experiments with robotic swarms of 100 simulated e-pucks show that monee does indeed promote task-driven behaviour without compromising environmental adaptation. We also investigate an extension of the parent selection process with a 'market mechanism' that can ensure equitable distribution of effort over multiple tasks, a particularly pressing issue if the environment promotes specialisation in single tasks.

Robot trajectory tracking with self-tuning predicted control

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Cui, Xianzhong; Shin, Kang G.

1988-01-01

A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.

Effect of Robot-Assisted Game Training on Upper Extremity Function in Stroke Patients

Science.gov (United States)

2017-01-01

Objective To determine the effects of combining robot-assisted game training with conventional upper extremity rehabilitation training (RCT) on motor and daily functions in comparison with conventional upper extremity rehabilitation training (OCT) in stroke patients. Methods Subjects were eligible if they were able to perform the robot-assisted game training and were divided randomly into a RCT and an OCT group. The RCT group performed one daily session of 30 minutes of robot-assisted game training with a rehabilitation robot, plus one daily session of 30 minutes of conventional rehabilitation training, 5 days a week for 2 weeks. The OCT group performed two daily sessions of 30 minutes of conventional rehabilitation training. The effects of training were measured by a Manual Function Test (MFT), Manual Muscle Test (MMT), Korean version of the Modified Barthel Index (K-MBI) and a questionnaire about satisfaction with training. These measurements were taken before and after the 2-week training. Results Both groups contained 25 subjects. After training, both groups showed significant improvements in motor and daily functions measured by MFT, MMT, and K-MBI compared to the baseline. Both groups demonstrated similar training effects, except motor power of wrist flexion. Patients in the RCT group were more satisfied than those in the OCT group. Conclusion There were no significant differences in changes in most of the motor and daily functions between the two types of training. However, patients in the RCT group were more satisfied than those in the OCT group. Therefore, RCT could be a useful upper extremity rehabilitation training method. PMID:28971037

Effect of Robot-Assisted Game Training on Upper Extremity Function in Stroke Patients.

Science.gov (United States)

Lee, Kyeong Woo; Kim, Sang Beom; Lee, Jong Hwa; Lee, Sook Joung; Kim, Jin Wan

2017-08-01

To determine the effects of combining robot-assisted game training with conventional upper extremity rehabilitation training (RCT) on motor and daily functions in comparison with conventional upper extremity rehabilitation training (OCT) in stroke patients. Subjects were eligible if they were able to perform the robot-assisted game training and were divided randomly into a RCT and an OCT group. The RCT group performed one daily session of 30 minutes of robot-assisted game training with a rehabilitation robot, plus one daily session of 30 minutes of conventional rehabilitation training, 5 days a week for 2 weeks. The OCT group performed two daily sessions of 30 minutes of conventional rehabilitation training. The effects of training were measured by a Manual Function Test (MFT), Manual Muscle Test (MMT), Korean version of the Modified Barthel Index (K-MBI) and a questionnaire about satisfaction with training. These measurements were taken before and after the 2-week training. Both groups contained 25 subjects. After training, both groups showed significant improvements in motor and daily functions measured by MFT, MMT, and K-MBI compared to the baseline. Both groups demonstrated similar training effects, except motor power of wrist flexion. Patients in the RCT group were more satisfied than those in the OCT group. There were no significant differences in changes in most of the motor and daily functions between the two types of training. However, patients in the RCT group were more satisfied than those in the OCT group. Therefore, RCT could be a useful upper extremity rehabilitation training method.

Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems

Science.gov (United States)

Abu-Alqumsan, Mohammad; Ebert, Felix; Peer, Angelika

2017-06-01

Objective. This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main focus is laid upon inferring the hidden target goals of users while navigating in a remote environment as a basis for possible adaptations. Approach. To reason about possible user goals, a general user-agnostic Bayesian update rule is devised to be recursively applied upon the arrival of evidences, i.e. user input and user gaze. Experiments were conducted with healthy subjects within robotic embodiment settings to evaluate the proposed method. These experiments varied along three factors: the type of the robot/environment (simulated and physical), the type of the interface (keyboard or BCI), and the way goal recognition (GR) is used to guide a simple shared control (SC) driving scheme. Main results. Our results show that the proposed GR algorithm is able to track and infer the hidden user goals with relatively high precision and recall. Further, the realized SC driving scheme benefits from the output of the GR system and is able to reduce the user effort needed to accomplish the assigned tasks. Despite the fact that the BCI requires higher effort compared to the keyboard conditions, most subjects were able to complete the assigned tasks, and the proposed GR system is additionally shown able to handle the uncertainty in user input during SSVEP-based interaction. The SC application of the belief vector indicates that the benefits of the GR module are more pronounced for BCIs, compared to the keyboard interface. Significance. Being based on intuitive heuristics that model the behavior of the general population during the execution of navigation tasks, the proposed GR method can be used without prior tuning for the

Robotic training and kinematic analysis of arm and hand after incomplete spinal cord injury: a case study.

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Kadivar, Z; Sullivan, J L; Eng, D P; Pehlivan, A U; O'Malley, M K; Yozbatiran, N; Francisco, G E

2011-01-01

Regaining upper extremity function is the primary concern of persons with tetraplegia caused by spinal cord injury (SCI). Robotic rehabilitation has been inadequately tested and underutilized in rehabilitation of the upper extremity in the SCI population. Given the acceptance of robotic training in stroke rehabilitation and SCI gait training, coupled with recent evidence that the spinal cord, like the brain, demonstrates plasticity that can be catalyzed by repetitive movement training such as that available with robotic devices, it is probable that robotic upper-extremity training of persons with SCI could be clinically beneficial. The primary goal of this pilot study was to test the feasibility of using a novel robotic device for the upper extremity (RiceWrist) and to evaluate robotic rehabilitation using the RiceWrist in a tetraplegic person with incomplete SCI. A 24-year-old male with incomplete SCI participated in 10 sessions of robot-assisted therapy involving intensive upper limb training. The subject successfully completed all training sessions and showed improvements in movement smoothness, as well as in the hand function. Results from this study provide valuable information for further developments of robotic devices for upper limb rehabilitation in persons with SCI. © 2011 IEEE

Adaptive PID formation control of nonholonomic robots without leader's velocity information.

Science.gov (United States)

Shen, Dongbin; Sun, Weijie; Sun, Zhendong

2014-03-01

This paper proposes an adaptive proportional integral derivative (PID) algorithm to solve a formation control problem in the leader-follower framework where the leader robot's velocities are unknown for the follower robots. The main idea is first to design some proper ideal control law for the formation system to obtain a required performance, and then to propose the adaptive PID methodology to approach the ideal controller. As a result, the formation is achieved with much more enhanced robust formation performance. The stability of the closed-loop system is theoretically proved by Lyapunov method. Both numerical simulations and physical vehicle experiments are presented to verify the effectiveness of the proposed adaptive PID algorithm. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

External Environment Sensing by a Module on Self-reconfiguration Robot

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Goto, Tomotsugu; Uchida, Masafumi; Onogaki, Hitoshi

In the situation in which a robot and a human work together by collaborating with each other, a robot and a human share one working environment, and each interferes in each other. The boundary of each complex dynamic occupation area changes in the connection movement which is the component of collaborative works at this time. The main restraint condition which relates to the robustness of that connection movement is each physical charactristics, that is, the embodiment. A robot body is variability though the embodiment of a human is almost fixed. Therefore, the safe and the robust connection movement is brought when a robot has the robot body which is well suitable for the embodiment of a human. A purpose for this research is that the colaboration works between the self-reconfiguration robot and a human is realized. To achieve this purpose, sensing function of external environment on a module was examined. A module is a component of the self-reconfiguration robot. A robot body vibrates when a module actuates an arm actively. This vibration is observed by using some acceleration sensors. Measured datas reflects a difference of objects that it touches a robot body. In this paper, the sensing technique of external environment which identifies this difference by using the neural network is proposed.

Feedback in Videogame-Based Adaptive Training

Science.gov (United States)

Rivera, Iris Daliz

2010-01-01

The field of training has been changing rapidly due to advances in technology such as videogame-based adaptive training. Videogame-based adaptive training has provided flexibility and adaptability for training in cost-effective ways. Although this method of training may have many benefits for the trainee, current research has not kept up to pace…

Robotic surgery training with commercially available simulation systems in 2011: a current review and practice pattern survey from the society of urologic robotic surgeons.

Science.gov (United States)

Lallas, Costas D; Davis, John W

2012-03-01

Virtual reality (VR) simulation has the potential to standardize surgical training for robotic surgery. We sought to evaluate all commercially available VR robotic simulators. A MEDLINE(®) literature search was performed of all applicable keywords. Available VR simulators were evaluated with regard to face, content, and construct validation. Additionally, a survey was e-mailed to all members of the Endourological Society, querying the pervasiveness of VR simulators in robotic surgical training. Finally, each company was e-mailed to ask for a price quote for their respective system. There are four VR robotic surgical simulators currently available: RoSS™, dV-Trainer™, SEP Robot™, and da Vinci(®) Skills Simulator™. Each system is represented in the literature and all possess varying degrees of face, content, and construct validity. Although all systems have basic skill sets with performance analysis and metrics software, most do not contain procedural components. When evaluating the results of our survey, most respondents did not possess a VR simulator although almost all believed there to be great potential for these devices in robotic surgical training. With the exception of the SEP Robot, all VR simulators are similar in price. VR simulators have a definite role in the future of robotic surgical training. Although the simulators target technical components of training, their largest impact will be appreciated when incorporated into a comprehensive educational curriculum.

Adaptive Robotic Systems Design in University of Applied Sciences

Directory of Open Access Journals (Sweden)

Gunsing Jos

2016-01-01

Full Text Available In the industry for highly specialized machine building (small series with high variety and high complexity and in healthcare a demand for adaptive robotics is rapidly coming up. Technically skilled people are not always available in sufficient numbers. A lot of know how with respect to the required technologies is available but successful adaptive robotic system designs are still rare. In our research at the university of applied sciences we incorporate new available technologies in our education courses by way of research projects; in these projects students will investigate the application possibilities of new technologies together with companies and teachers. Thus we are able to transfer knowledge to the students including an innovation oriented attitude and skills. Last years we developed several industrial binpicking applications for logistics and machining-factories with different types of 3D vision. Also force feedback gripping has been developed including slip sensing. Especially for healthcare robotics we developed a so-called twisted wire actuator, which is very compact in combination with an underactuated gripper, manufactured in one piece in polyurethane. We work both on modeling and testing the functions of these designs but we work also on complete demonstrator systems. Since the amount of disciplines involved in complex product and machine design increases rapidly we pay a lot of attention with respect to systems engineering methods. Apart from the classical engineering disciplines like mechanical, electrical, software and mechatronics engineering, especially for adaptive robotics more and more disciplines like industrial product design, communication … multimedia design and of course physics and even art are to be involved depending on the specific application to be designed. Design tools like V-model, agile/scrum and design-approaches to obtain the best set of requirements are being implemented in the engineering studies from

Beyond adaptive-critic creative learning for intelligent mobile robots

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Liao, Xiaoqun; Cao, Ming; Hall, Ernest L.

2001-10-01

Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. The adaptive critic is a form of reinforcement learning. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The success of the system is highly dependent on the accuracy of the forecasted grades and adaptability of the action module. Examples from the guidance of a mobile robot are provided to illustrate the method for simple line following and for the more complex navigation and control in an unstructured environment. The theory presented that is beyond the adaptive critic may be called creative theory. Creative theory is a form of learning that models the highest level of human learning - imagination. The application of the creative theory appears to not only be to mobile robots but also to many other forms of human endeavor such as educational learning and business forecasting. Reinforcement learning such as the adaptive critic may be applied to known problems to aid in the discovery of their solutions. The significance of creative theory is that it

The Combined Effects of Adaptive Control and Virtual Reality on Robot-Assisted Fine Hand Motion Rehabilitation in Chronic Stroke Patients: A Case Study.

Science.gov (United States)

Huang, Xianwei; Naghdy, Fazel; Naghdy, Golshah; Du, Haiping; Todd, Catherine

2018-01-01

Robot-assisted therapy is regarded as an effective and reliable method for the delivery of highly repetitive training that is needed to trigger neuroplasticity following a stroke. However, the lack of fully adaptive assist-as-needed control of the robotic devices and an inadequate immersive virtual environment that can promote active participation during training are obstacles hindering the achievement of better training results with fewer training sessions required. This study thus focuses on these research gaps by combining these 2 key components into a rehabilitation system, with special attention on the rehabilitation of fine hand motion skills. The effectiveness of the proposed system is tested by conducting clinical trials on a chronic stroke patient and verified through clinical evaluation methods by measuring the key kinematic features such as active range of motion (ROM), finger strength, and velocity. By comparing the pretraining and post-training results, the study demonstrates that the proposed method can further enhance the effectiveness of fine hand motion rehabilitation training by improving finger ROM, strength, and coordination. Copyright © 2018 National Stroke Association. Published by Elsevier Inc. All rights reserved.

A Domain-Specific Language for Programming Self-Reconfigurable Robots

DEFF Research Database (Denmark)

Schultz, Ulrik Pagh; Christensen, David Johan; Støy, Kasper

2007-01-01

, self-reconfigurable robots, we have developed a declarative, role-based language that allows the programmer to define roles and behavior independently of the concrete physical structure of the robot. Roles are compiled to mobile code fragments that distribute themselves over the physical structure...

Intelligent manipulation technique for multi-branch robotic systems

Science.gov (United States)

Chen, Alexander Y. K.; Chen, Eugene Y. S.

1990-01-01

New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system.

Effect of visual distraction and auditory feedback on patient effort during robot-assisted movement training after stroke.

Science.gov (United States)

Secoli, Riccardo; Milot, Marie-Helene; Rosati, Giulio; Reinkensmeyer, David J

2011-04-23

Practicing arm and gait movements with robotic assistance after neurologic injury can help patients improve their movement ability, but patients sometimes reduce their effort during training in response to the assistance. Reduced effort has been hypothesized to diminish clinical outcomes of robotic training. To better understand patient slacking, we studied the role of visual distraction and auditory feedback in modulating patient effort during a common robot-assisted tracking task. Fourteen participants with chronic left hemiparesis from stroke, five control participants with chronic right hemiparesis and fourteen non-impaired healthy control participants, tracked a visual target with their arms while receiving adaptive assistance from a robotic arm exoskeleton. We compared four practice conditions: the baseline tracking task alone; tracking while also performing a visual distracter task; tracking with the visual distracter and sound feedback; and tracking with sound feedback. For the distracter task, symbols were randomly displayed in the corners of the computer screen, and the participants were instructed to click a mouse button when a target symbol appeared. The sound feedback consisted of a repeating beep, with the frequency of repetition made to increase with increasing tracking error. Participants with stroke halved their effort and doubled their tracking error when performing the visual distracter task with their left hemiparetic arm. With sound feedback, however, these participants increased their effort and decreased their tracking error close to their baseline levels, while also performing the distracter task successfully. These effects were significantly smaller for the participants who used their non-paretic arm and for the participants without stroke. Visual distraction decreased participants effort during a standard robot-assisted movement training task. This effect was greater for the hemiparetic arm, suggesting that the increased demands associated

Robotic finger perturbation training improves finger postural steadiness and hand dexterity.

Science.gov (United States)

Yoshitake, Yasuhide; Ikeda, Atsutoshi; Shinohara, Minoru

2018-02-01

The purpose of the study was to understand the effect of robotic finger perturbation training on steadiness in finger posture and hand dexterity in healthy young adults. A mobile robotic finger training system was designed to have the functions of high-speed mechanical response, two degrees of freedom, and adjustable loading amplitude and direction. Healthy young adults were assigned to one of the three groups: random perturbation training (RPT), constant force training (CFT), and control. Subjects in RPT and CFT performed steady posture training with their index finger using the robot in different modes: random force in RPT and constant force in CFT. After the 2-week intervention period, fluctuations of the index finger posture decreased only in RPT during steady position-matching tasks with an inertial load. Purdue pegboard test score improved also in RPT only. The relative change in finger postural fluctuations was negatively correlated with the relative change in the number of completed pegs in the pegboard test in RPT. The results indicate that finger posture training with random mechanical perturbations of varying amplitudes and directions of force is effective in improving finger postural steadiness and hand dexterity in healthy young adults. Copyright © 2017 Elsevier Ltd. All rights reserved.

Self-Reconfiguration Planning of Robot Embodiment for Inherent Safe Performance

Science.gov (United States)

Uchida, Masafumi; Nozawa, Akio; Asano, Hirotoshi; Onogaki, Hitoshi; Mizuno, Tota; Park, Young-Il; Ide, Hideto; Yokoyama, Shuichi

In the situation in which a robot and a human work together by collaborating with each other, a robot and a human share one working environment, and each interferes in each other. In other ward, it is impossible to avoid the physical contact and the interaction of force between a robot and a human. The boundary of each complex dynamic occupation area changes in the connection movement which is the component of collaborative works at this time. The main restraint condition which relates to the robustness of that connection movement is each physical charactristics, that is, the embodiment. A robot body is variability though the embodiment of a human is almost fixed. Therefore, the safe and the robust connection movement is brought when a robot has the robot body which is well suitable for the embodiment of a human. A purpose for this research is that the colaboration works between the self-reconfiguration robot and a human is realized. To achieve this purpose, a self-reconfiguration algorithm based on some indexes to evaluate a robot body in the macroscopic point of view was examined on a modular robot system of the 2-D lattice structure. In this paper, it investigated effect specially that the object of learning of each individual was limited to the cooperative behavior between the adjoining modules toward the macroscopic evaluation index.

Outcomes of a virtual-reality simulator-training programme on basic surgical skills in robot-assisted laparoscopic surgery.

Science.gov (United States)

Phé, Véronique; Cattarino, Susanna; Parra, Jérôme; Bitker, Marc-Olivier; Ambrogi, Vanina; Vaessen, Christophe; Rouprêt, Morgan

2017-06-01

The utility of the virtual-reality robotic simulator in training programmes has not been clearly evaluated. Our aim was to evaluate the impact of a virtual-reality robotic simulator-training programme on basic surgical skills. A simulator-training programme in robotic surgery, using the da Vinci Skills Simulator, was evaluated in a population including junior and seasoned surgeons, and non-physicians. Their performances on robotic dots and suturing-skin pod platforms before and after virtual-simulation training were rated anonymously by surgeons experienced in robotics. 39 participants were enrolled: 14 medical students and residents in surgery, 14 seasoned surgeons, 11 non-physicians. Junior and seasoned surgeons' performances on platforms were not significantly improved after virtual-reality robotic simulation in any of the skill domains, in contrast to non-physicians. The benefits of virtual-reality simulator training on several tasks to basic skills in robotic surgery were not obvious among surgeons in our initial and early experience with the simulator. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

The quadruped robot adaptive control in trotting gait walking on slopes

Science.gov (United States)

Zhang, Shulong; Ma, Hongxu; Yang, Yu; Wang, Jian

2017-10-01

The quadruped robot can be decomposed into a planar seven-link closed kinematic chain in the direction of supporting line and a linear inverted pendulum in normal direction of supporting line. The ground slope can be estimated by using the body attitude information and supporting legs length. The slope degree is used in feedback, to achieve the point of quadruped robot adaptive control walking on slopes. The simulation results verify that the quadruped robot can achieves steady locomotion on the slope with the control strategy proposed in this passage.

Robot Guided 'Pen Skill' Training in Children with Motor Difficulties.

Directory of Open Access Journals (Sweden)

Katy A Shire

Full Text Available Motor deficits are linked to a range of negative physical, social and academic consequences. Haptic robotic interventions, based on the principles of sensorimotor learning, have been shown previously to help children with motor problems learn new movements. We therefore examined whether the training benefits of a robotic system would generalise to a standardised test of 'pen-skills', assessed using objective kinematic measures [via the Clinical Kinematic Assessment Tool, CKAT]. A counterbalanced, cross-over design was used in a group of 51 children (37 male, aged 5-11 years with manual control difficulties. Improved performance on a novel task using the robotic device could be attributed to the intervention but there was no evidence of generalisation to any of the CKAT tasks. The robotic system appears to have the potential to support motor learning, with the technology affording numerous advantages. However, the training regime may need to target particular manual skills (e.g. letter formation in order to obtain clinically significant improvements in specific skills such as handwriting.

Alignment Condition-Based Robust Adaptive Iterative Learning Control of Uncertain Robot System

Directory of Open Access Journals (Sweden)

Guofeng Tong

2014-04-01

Full Text Available This paper proposes an adaptive iterative learning control strategy integrated with saturation-based robust control for uncertain robot system in presence of modelling uncertainties, unknown parameter, and external disturbance under alignment condition. An important merit is that it achieves adaptive switching of gain matrix both in conventional PD-type feedforward control and robust adaptive control in the iteration domain simultaneously. The analysis of convergence of proposed control law is based on Lyapunov's direct method under alignment initial condition. Simulation results demonstrate the faster learning rate and better robust performance with proposed algorithm by comparing with other existing robust controllers. The actual experiment on three-DOF robot manipulator shows its better practical effectiveness.

Adaptive control of an exoskeleton robot with uncertainties on kinematics and dynamics.

Science.gov (United States)

Brahmi, Brahim; Saad, Maarouf; Ochoa-Luna, Cristobal; Rahman, Mohammad H

2017-07-01

In this paper, we propose a new adaptive control technique based on nonlinear sliding mode control (JSTDE) taking into account kinematics and dynamics uncertainties. This approach is applied to an exoskeleton robot with uncertain kinematics and dynamics. The adaptation design is based on Time Delay Estimation (TDE). The proposed strategy does not necessitate the well-defined dynamic and kinematic models of the system robot. The updated laws are designed using Lyapunov-function to solve the adaptation problem systematically, proving the close loop stability and ensuring the convergence asymptotically of the outputs tracking errors. Experiments results show the effectiveness and feasibility of JSTDE technique to deal with the variation of the unknown nonlinear dynamics and kinematics of the exoskeleton model.

Virtual spring damper method for nonholonomic robotic swarm self-organization and leader following

Science.gov (United States)

Wiech, Jakub; Eremeyev, Victor A.; Giorgio, Ivan

2018-04-01

In this paper, we demonstrate a method for self-organization and leader following of nonholonomic robotic swarm based on spring damper mesh. By self-organization of swarm robots we mean the emergence of order in a swarm as the result of interactions among the single robots. In other words the self-organization of swarm robots mimics some natural behavior of social animals like ants among others. The dynamics of two-wheel robot is derived, and a relation between virtual forces and robot control inputs is defined in order to establish stable swarm formation. Two cases of swarm control are analyzed. In the first case the swarm cohesion is achieved by virtual spring damper mesh connecting nearest neighboring robots without designated leader. In the second case we introduce a swarm leader interacting with nearest and second neighbors allowing the swarm to follow the leader. The paper ends with numeric simulation for performance evaluation of the proposed control method.

Self-Inhibiting Modules Can Self-Organize as a Brain of a Robot: A Conjecture

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J. Negrete-Martínez

2006-01-01

Full Text Available In this article we describe a new robot control architecture on the basis of self-organization of self-inhibiting modules. The architecture can generate a complex behaviour repertoire. The repertoire can be performance-enhanced or increased by modular poly-functionality and/or by addition of new modules. This architecture is illustrated in a robot consisting of a car carrying an arm with a grasping tool. In the robot, each module drives either a joint motor or a pair of wheel motors. Every module estimates the distance from a sensor placed in the tool to a beacon. If the distance is smaller than a previously measured distance, the module drives its motor in the same direction of its prior movement. If the distance is larger, the next movement will be in the opposite direction; but, if the movement produces no significant change in distance, the module self-inhibits. A self-organization emerges: any module can be the next to take control of the motor activity of the robot once one module self-inhibits. A single module is active at a given time. The modules are implemented as computer procedures and their turn for participation scheduled by an endless program. The overall behaviour of the robot corresponds to a reaching attention behaviour. It is easily switched to a running-away attention behaviour by changing the sign of the same parameter in each module. The addition of a “sensor-gain attenuation reflex” module and of a “light-orientation reflex” module provides an increase of the behavioural attention repertoire and performance enhancement. Since scheduling a module does not necessarily produce its sustained intervention, the architecture of the “brain” is actually providing action induction rather than action selection.

Controlling maximum evaluation duration in on-line and on-board evolutionary robotics

NARCIS (Netherlands)

Atta-ul-Qayyum, A.; Nedev, D.G.; Haasdijk, E.W.

2014-01-01

On-line evolution of robot controllers allows robots to adapt while they perform their proper tasks. In our investigations, robots contain their own self-sufficient evolutionary algorithm (known as the encapsulated approach) where individual solutions are evaluated by means of a time sharing scheme:

Development of a Self-Stabilizing Robotic Chassis for Industry

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Ryadchikov Igor

2017-01-01

Full Text Available Presented the description of the bipedal robotic chassis with the unique kinematic scheme which has the possibility to locomote in complicated multi-level environment. AnyWalker is equipped with the system of compensation of external impacts with motor-wheels which can self-stabilize the robotic system in 3 dimensions. Presented chassis suggests to have open software and hardware architecture in order to become the universal walking platform for service and industry robots.

Positive effects of robotic exoskeleton training of upper limb reaching movements after stroke

Science.gov (United States)

2012-01-01

This study, conducted in a group of nine chronic patients with right-side hemiparesis after stroke, investigated the effects of a robotic-assisted rehabilitation training with an upper limb robotic exoskeleton for the restoration of motor function in spatial reaching movements. The robotic assisted rehabilitation training was administered for a period of 6 weeks including reaching and spatial antigravity movements. To assess the carry-over of the observed improvements in movement during training into improved function, a kinesiologic assessment of the effects of the training was performed by means of motion and dynamic electromyographic analysis of reaching movements performed before and after training. The same kinesiologic measurements were performed in a healthy control group of seven volunteers, to determine a benchmark for the experimental observations in the patients’ group. Moreover degree of functional impairment at the enrolment and discharge was measured by clinical evaluation with upper limb Fugl-Meyer Assessment scale (FMA, 0–66 points), Modified Ashworth scale (MA, 0–60 pts) and active ranges of motion. The robot aided training induced, independently by time of stroke, statistical significant improvements of kinesiologic (movement time, smoothness of motion) and clinical (4.6 ± 4.2 increase in FMA, 3.2 ± 2.1 decrease in MA) parameters, as a result of the increased active ranges of motion and improved co-contraction index for shoulder extension/flexion. Kinesiologic parameters correlated significantly with clinical assessment values, and their changes after the training were affected by the direction of motion (inward vs. outward movement) and position of target to be reached (ipsilateral, central and contralateral peripersonal space). These changes can be explained as a result of the motor recovery induced by the robotic training, in terms of regained ability to execute single joint movements and of improved interjoint coordination of

Positive effects of robotic exoskeleton training of upper limb reaching movements after stroke

Directory of Open Access Journals (Sweden)

Frisoli Antonio

2012-06-01

Full Text Available Abstract This study, conducted in a group of nine chronic patients with right-side hemiparesis after stroke, investigated the effects of a robotic-assisted rehabilitation training with an upper limb robotic exoskeleton for the restoration of motor function in spatial reaching movements. The robotic assisted rehabilitation training was administered for a period of 6 weeks including reaching and spatial antigravity movements. To assess the carry-over of the observed improvements in movement during training into improved function, a kinesiologic assessment of the effects of the training was performed by means of motion and dynamic electromyographic analysis of reaching movements performed before and after training. The same kinesiologic measurements were performed in a healthy control group of seven volunteers, to determine a benchmark for the experimental observations in the patients’ group. Moreover degree of functional impairment at the enrolment and discharge was measured by clinical evaluation with upper limb Fugl-Meyer Assessment scale (FMA, 0–66 points, Modified Ashworth scale (MA, 0–60 pts and active ranges of motion. The robot aided training induced, independently by time of stroke, statistical significant improvements of kinesiologic (movement time, smoothness of motion and clinical (4.6 ± 4.2 increase in FMA, 3.2 ± 2.1 decrease in MA parameters, as a result of the increased active ranges of motion and improved co-contraction index for shoulder extension/flexion. Kinesiologic parameters correlated significantly with clinical assessment values, and their changes after the training were affected by the direction of motion (inward vs. outward movement and position of target to be reached (ipsilateral, central and contralateral peripersonal space. These changes can be explained as a result of the motor recovery induced by the robotic training, in terms of regained ability to execute single joint movements and of improved

Towards more effective robotic gait training for stroke rehabilitation: a review

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Pennycott Andrew

2012-09-01

Full Text Available Abstract Background Stroke is the most common cause of disability in the developed world and can severely degrade walking function. Robot-driven gait therapy can provide assistance to patients during training and offers a number of advantages over other forms of therapy. These potential benefits do not, however, seem to have been fully realised as of yet in clinical practice. Objectives This review determines ways in which robot-driven gait technology could be improved in order to achieve better outcomes in gait rehabilitation. Methods The literature on gait impairments caused by stroke is reviewed, followed by research detailing the different pathways to recovery. The outcomes of clinical trials investigating robot-driven gait therapy are then examined. Finally, an analysis of the literature focused on the technical features of the robot-based devices is presented. This review thus combines both clinical and technical aspects in order to determine the routes by which robot-driven gait therapy could be further developed. Conclusions Active subject participation in robot-driven gait therapy is vital to many of the potential recovery pathways and is therefore an important feature of gait training. Higher levels of subject participation and challenge could be promoted through designs with a high emphasis on robotic transparency and sufficient degrees of freedom to allow other aspects of gait such as balance to be incorporated.

Robot Games for Elderly

DEFF Research Database (Denmark)

Hansen, Søren Tranberg

2011-01-01

improve a person’s overall health, and this thesis investigates how games based on an autonomous, mobile robot platform, can be used to motivate elderly to move physically while playing. The focus of the investigation is on the development of games for an autonomous, mobile robot based on algorithms using...... spatio-temporal information about player behaviour - more specifically, I investigate three types of games each using a different control strategy. The first game is based on basic robot control which allows the robot to detect and follow a person. A field study in a rehabilitation centre and a nursing....... The robot facilitates interaction, and the study suggests that robot based games potentially can be used for training balance and orientation. The second game consists in an adaptive game algorithm which gradually adjusts the game challenge to the mobility skills of the player based on spatio...

The effectiveness of robotic training depends on motor task characteristics.

Science.gov (United States)

Marchal-Crespo, Laura; Rappo, Nicole; Riener, Robert

2017-12-01

Previous research suggests that the effectiveness of robotic training depends on the motor task to be learned. However, it is still an open question which specific task's characteristics influence the efficacy of error-modulating training strategies. Motor tasks can be classified based on the time characteristics of the task, in particular the task's duration (discrete vs. continuous). Continuous tasks require movements without distinct beginning or end. Discrete tasks require fast movements that include well-defined postures at the beginning and the end. We developed two games, one that requires a continuous movement-a tracking task-and one that requires discrete movements-a fast reaching task. We conducted an experiment with thirty healthy subjects to evaluate the effectiveness of three error-modulating training strategies-no guidance, error amplification (i.e., repulsive forces proportional to errors) and haptic guidance-on self-reported motivation and learning of the continuous and discrete games. Training with error amplification resulted in better motor learning than haptic guidance, besides the fact that error amplification reduced subjects' interest/enjoyment and perceived competence during training. Only subjects trained with error amplification improved their performance after training the discrete game. In fact, subjects trained without guidance improved the performance in the continuous game significantly more than in the discrete game, probably because the continuous task required greater attentional levels. Error-amplifying training strategies have a great potential to provoke better motor learning in continuous and discrete tasks. However, their long-lasting negative effects on motivation might limit their applicability in intense neurorehabilitation programs.

Adaptive Controller for 6-DOF Parallel Robot Using T-S Fuzzy Inference

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Xue Jian

2013-02-01

Full Text Available 6-DOF parallel robot always appears in the form of Stewart platform. It has been widely used in industry for the benefits such as strong structural stiffness, high movement accuracy and so on. Space docking technology makes higher requirements of motion accuracy and dynamic performance to the control method on 6-DOF parallel robot. In this paper, a hydraulic 6-DOF parallel robot was used to simulate the docking process. Based on this point, this paper gave a thorough study on the design of an adaptive controller to eliminate the asymmetric of controlled plant and uncertain load force interference. Takagi-Sugeno (T-S fuzzy inference model was used to build the fuzzy adaptive controller. With T-S model, the controller directly imposes adaptive control signal on the plant to make sure that the output of plant could track the reference model output. The controller has simple structure and is easy to implement. Experiment results show that the controller can eliminate asymmetric and achieve good dynamic performance, and has good robustness to load interference.

Fuzzy Adaptation Algorithms’ Control for Robot Manipulators with Uncertainty Modelling Errors

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Yongqing Fan

2018-01-01

Full Text Available A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators’ system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed-loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example.

Human-robot cooperative movement training: Learning a novel sensory motor transformation during walking with robotic assistance-as-needed

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Benitez Raul

2007-03-01

Full Text Available Abstract Background A prevailing paradigm of physical rehabilitation following neurologic injury is to "assist-as-needed" in completing desired movements. Several research groups are attempting to automate this principle with robotic movement training devices and patient cooperative algorithms that encourage voluntary participation. These attempts are currently not based on computational models of motor learning. Methods Here we assume that motor recovery from a neurologic injury can be modelled as a process of learning a novel sensory motor transformation, which allows us to study a simplified experimental protocol amenable to mathematical description. Specifically, we use a robotic force field paradigm to impose a virtual impairment on the left leg of unimpaired subjects walking on a treadmill. We then derive an "assist-as-needed" robotic training algorithm to help subjects overcome the virtual impairment and walk normally. The problem is posed as an optimization of performance error and robotic assistance. The optimal robotic movement trainer becomes an error-based controller with a forgetting factor that bounds kinematic errors while systematically reducing its assistance when those errors are small. As humans have a natural range of movement variability, we introduce an error weighting function that causes the robotic trainer to disregard this variability. Results We experimentally validated the controller with ten unimpaired subjects by demonstrating how it helped the subjects learn the novel sensory motor transformation necessary to counteract the virtual impairment, while also preventing them from experiencing large kinematic errors. The addition of the error weighting function allowed the robot assistance to fade to zero even though the subjects' movements were variable. We also show that in order to assist-as-needed, the robot must relax its assistance at a rate faster than that of the learning human. Conclusion The assist

Human-robot cooperative movement training: learning a novel sensory motor transformation during walking with robotic assistance-as-needed.

Science.gov (United States)

Emken, Jeremy L; Benitez, Raul; Reinkensmeyer, David J

2007-03-28

A prevailing paradigm of physical rehabilitation following neurologic injury is to "assist-as-needed" in completing desired movements. Several research groups are attempting to automate this principle with robotic movement training devices and patient cooperative algorithms that encourage voluntary participation. These attempts are currently not based on computational models of motor learning. Here we assume that motor recovery from a neurologic injury can be modelled as a process of learning a novel sensory motor transformation, which allows us to study a simplified experimental protocol amenable to mathematical description. Specifically, we use a robotic force field paradigm to impose a virtual impairment on the left leg of unimpaired subjects walking on a treadmill. We then derive an "assist-as-needed" robotic training algorithm to help subjects overcome the virtual impairment and walk normally. The problem is posed as an optimization of performance error and robotic assistance. The optimal robotic movement trainer becomes an error-based controller with a forgetting factor that bounds kinematic errors while systematically reducing its assistance when those errors are small. As humans have a natural range of movement variability, we introduce an error weighting function that causes the robotic trainer to disregard this variability. We experimentally validated the controller with ten unimpaired subjects by demonstrating how it helped the subjects learn the novel sensory motor transformation necessary to counteract the virtual impairment, while also preventing them from experiencing large kinematic errors. The addition of the error weighting function allowed the robot assistance to fade to zero even though the subjects' movements were variable. We also show that in order to assist-as-needed, the robot must relax its assistance at a rate faster than that of the learning human. The assist-as-needed algorithm proposed here can limit error during the learning of a

Effects of robot-assisted training on upper limb functional recovery during the rehabilitation of poststroke patients.

Science.gov (United States)

Daunoraviciene, Kristina; Adomaviciene, Ausra; Grigonyte, Agne; Griškevičius, Julius; Juocevicius, Alvydas

2018-05-18

The study aims to determine the effectiveness of robot-assisted training in the recovery of stroke-affected arms using an exoskeleton robot Armeo Spring. To identify the effect of robot training on functional recovery of the arm. A total of 34 stroke patients were divided into either an experimental group (EG; n= 17) or a control group (n= 17). EG was also trained to use the Armeo Spring during occupational therapy. Both groups were clinically assessed before and after treatment. Statistical comparison methods (i.e. one-tailed t-tests for differences between two independent means and the simplest test) were conducted to compare motor recovery using robot-assisted training or conventional therapy. Patients assigned to the EG showed a statistically significant improvement in upper extremity motor function when compared to the CG by FIM (Peffect in the EG and CG was meaningful for shoulder and elbow kinematic parameters. The findings show the benefits of robot therapy in two areas of functional recovery. Task-oriented robotic training in rehabilitation setting facilitates recovery not only of the motor function of the paretic arm but also of the cognitive abilities in stroke patients.

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment.

Science.gov (United States)

Yao, Yao; Storme, Veronique; Marchal, Kathleen; Van de Peer, Yves

2016-01-01

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population.

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment

Science.gov (United States)

Yao, Yao; Storme, Veronique; Marchal, Kathleen

2016-01-01

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population. PMID:28028477

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment

Directory of Open Access Journals (Sweden)

Yao Yao

2016-12-01

Full Text Available We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population.

Towards Python-based Domain-specific Languages for Self-reconfigurable Modular Robotics Research

DEFF Research Database (Denmark)

Moghadam, Mikael; Johan Christensen, David; Brandt, David

2013-01-01

This paper explores the role of operating system and high-level languages in the development of software and domain-specific languages (DSLs) for self-reconfigurable robotics. We review some of the current trends in self-reconfigurable robotics and describe the development of a software system...... for ATRON II which utilizes Linux and Python to significantly improve software abstraction and portability while providing some basic features which could prove useful when using Python, either stand-alone or via a DSL, on a self-reconfigurable robot system. These features include transparent socket...... communication, module identification, easy software transfer and reliable module-to-module communication. The end result is a software platform for modular robots that where appropriate builds on existing work in operating systems, virtual machines, middleware and high-level languages....

An Adaptive Approach for Precise Underwater Vehicle Control in Combined Robot-Diver Operations

Science.gov (United States)

2015-03-01

and Nicosia and Tomei [13] focused on industrial applications involving robotic manipulator arms carrying various loads. The application of...1987. 94 [13] S. Nicosia and P. Tomei, “Model reference adaptive control algorithms for industrial robots ,” Automatica, vol. 20, pp. 635–644, 9... kinematic and dynamic properties,” The International Journal of Robotics Research, vol. 25, pp. 283–296, March 01, 2006. [17] A. Sanei and M. French

Development and Validity of a Silicone Renal Tumor Model for Robotic Partial Nephrectomy Training.

Science.gov (United States)

Monda, Steven M; Weese, Jonathan R; Anderson, Barrett G; Vetter, Joel M; Venkatesh, Ramakrishna; Du, Kefu; Andriole, Gerald L; Figenshau, Robert S

2018-04-01

To provide a training tool to address the technical challenges of robot-assisted laparoscopic partial nephrectomy, we created silicone renal tumor models using 3-dimensional printed molds of a patient's kidney with a mass. In this study, we assessed the face, content, and construct validity of these models. Surgeons of different training levels completed 4 simulations on silicone renal tumor models. Participants were surveyed on the usefulness and realism of the model as a training tool. Performance was measured using operation-specific metrics, self-reported operative demands (NASA Task Load Index [NASA TLX]), and blinded expert assessment (Global Evaluative Assessment of Robotic Surgeons [GEARS]). Twenty-four participants included attending urologists, endourology fellows, urology residents, and medical students. Post-training surveys of expert participants yielded mean results of 79.2 on the realism of the model's overall feel and 90.2 on the model's overall usefulness for training. Renal artery clamp times and GEARS scores were significantly better in surgeons further in training (P ≤.005 and P ≤.025). Renal artery clamp times, preserved renal parenchyma, positive margins, NASA TLX, and GEARS scores were all found to improve across trials (P <.001, P = .025, P = .024, P ≤.020, and P ≤.006, respectively). Face, content, and construct validity were demonstrated in the use of a silicone renal tumor model in a cohort of surgeons of different training levels. Expert participants deemed the model useful and realistic. Surgeons of higher training levels performed better than less experienced surgeons in various study metrics, and improvements within individuals were observed over sequential trials. Future studies should aim to assess model predictive validity, namely, the association between model performance improvements and improvements in live surgery. Copyright © 2018 Elsevier Inc. All rights reserved.

Auto-adaptative Robot-aided Therapy based in 3D Virtual Tasks controlled by a Supervised and Dynamic Neuro-Fuzzy System

Directory of Open Access Journals (Sweden)

Luis Daniel Lledó

2015-03-01

Full Text Available This paper presents an application formed by a classification method based on the architecture of ART neural network (Adaptive Resonance Theory and the Fuzzy Set Theory to classify physiological reactions in order to automatically and dynamically adapt a robot-assisted rehabilitation therapy to the patient needs, using a three-dimensional task in a virtual reality system. Firstly, the mathematical and structural model of the neuro-fuzzy classification method is described together with the signal and training data acquisition. Then, the virtual designed task with physics behavior and its development procedure are explained. Finally, the general architecture of the experimentation for the auto-adaptive therapy is presented using the classification method with the virtual reality exercise.

Android Robot-Mediated Mock Job Interview Sessions for Young Adults with Autism Spectrum Disorder: A Pilot Study.

Science.gov (United States)

Kumazaki, Hirokazu; Warren, Zachary; Corbett, Blythe A; Yoshikawa, Yuichiro; Matsumoto, Yoshio; Higashida, Haruhiro; Yuhi, Teruko; Ikeda, Takashi; Ishiguro, Hiroshi; Kikuchi, Mitsuru

2017-01-01

The feasibility and preliminary efficacy of an android robot-mediated mock job interview training in terms of both bolstering self-confidence and reducing biological levels of stress in comparison to a psycho-educational approach human interview was assessed in a randomized study. Young adults (ages 18-25 years) with autism spectrum disorder (ASD) were randomized to participate either in a mock job interview training with our android robot system ( n  = 7) or a self-paced review of materials about job-interviewing skills ( n  = 8). Baseline and outcome measurements of self-reported performance/efficacy and salivary cortisol were obtained after a mock job interview with a human interviewer. After training sessions, individuals with ASD participating in the android robot-mediated sessions reported marginally improved self-confidence and demonstrated significantly lower levels of salivary cortisol as compared to the control condition. These results provide preliminary support for the feasibility and efficacy of android robot-mediated learning.

Android Robot-Mediated Mock Job Interview Sessions for Young Adults with Autism Spectrum Disorder: A Pilot Study

Directory of Open Access Journals (Sweden)

Hirokazu Kumazaki

2017-09-01

Full Text Available The feasibility and preliminary efficacy of an android robot-mediated mock job interview training in terms of both bolstering self-confidence and reducing biological levels of stress in comparison to a psycho-educational approach human interview was assessed in a randomized study. Young adults (ages 18–25 years with autism spectrum disorder (ASD were randomized to participate either in a mock job interview training with our android robot system (n = 7 or a self-paced review of materials about job-interviewing skills (n = 8. Baseline and outcome measurements of self-reported performance/efficacy and salivary cortisol were obtained after a mock job interview with a human interviewer. After training sessions, individuals with ASD participating in the android robot-mediated sessions reported marginally improved self-confidence and demonstrated significantly lower levels of salivary cortisol as compared to the control condition. These results provide preliminary support for the feasibility and efficacy of android robot-mediated learning.

Biologically inspired control of humanoid robot arms robust and adaptive approaches

CERN Document Server

Spiers, Adam; Herrmann, Guido

2016-01-01

This book investigates a biologically inspired method of robot arm control, developed with the objective of synthesising human-like motion dynamically, using nonlinear, robust and adaptive control techniques in practical robot systems. The control method caters to a rising interest in humanoid robots and the need for appropriate control schemes to match these systems. Unlike the classic kinematic schemes used in industrial manipulators, the dynamic approaches proposed here promote human-like motion with better exploitation of the robot’s physical structure. This also benefits human-robot interaction. The control schemes proposed in this book are inspired by a wealth of human-motion literature that indicates the drivers of motion to be dynamic, model-based and optimal. Such considerations lend themselves nicely to achievement via nonlinear control techniques without the necessity for extensive and complex biological models. The operational-space method of robot control forms the basis of many of the techniqu...

Adapting a robotics program to enhance participation and interest in STEM among children with disabilities: a pilot study.

Science.gov (United States)

Lindsay, Sally; Hounsell, Kara Grace

2017-10-01

Youth with disabilities are under-represented in science, technology, engineering, and math (STEM) in school and in the workforce. One encouraging approach to engage youth's interest in STEM is through robotics; however, such programs are mostly for typically developing youth. The purpose of this study was to understand the development and implementation of an adapted robotics program for children and youth with disabilities and their experiences within it. Our mixed methods pilot study (pre- and post-workshop surveys, observations, and interviews) involved 41 participants including: 18 youth (aged 6-13), 12 parents and 11 key informants. The robotics program involved 6, two-hour workshops held at a paediatric hospital. Our findings showed that several adaptations made to the robotics program helped to enhance the participation of children with disabilities. Adaptations addressed the educational/curriculum, cognitive and learning, physical and social needs of the children. In regards to experiences within the adapted hospital program, our findings highlight that children enjoyed the program and learned about computer programming and building robots. Clinicians and educators should consider engaging youth with disabilities in robotics to enhance learning and interest in STEM. Implications for Rehabilitation Clinicians and educators should consider adapting curriculum content and mode of delivery of LEGO ® robotics programs to include youth with disabilities. Appropriate staffing including clinicians and educators who are knowledgeable about youth with disabilities and LEGO ® robotics are needed. Clinicians should consider engaging youth with disabilities in LEGO ® to enhance learning and interest in STEM.

Multiple Decoupled CPGs with Local Sensory Feedback for Adaptive Locomotion Behaviors of Bio-inspired Walking Robots

DEFF Research Database (Denmark)

Shaker Barikhan, Subhi; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

, and their interactions during body and leg movements through the environment. Based on this concept, we present here an artificial bio-inspired walking system. Its intralimb coordination is formed by multiple decoupled CPGs while its interlimb coordination is attained by the interactions between body dynamics...... and the environment through local sensory feedback of each leg. Simulation results show that this bio-inspired approach generates self-organizing emergent locomotion allowing the robot to adaptively form regular patterns, to stably walk while pushing an object with its front legs or performing multiple stepping...

Adaptive Human-Aware Robot Navigation in Close Proximity to Humans

DEFF Research Database (Denmark)

Svenstrup, Mikael; Hansen, Søren Tranberg; Andersen, Hans Jørgen

2011-01-01

For robots to be able coexist with people in future everyday human environments, they must be able to act in a safe, natural and comfortable way. This work addresses the motion of a mobile robot in an environment, where humans potentially want to interact with it. The designed system consists...... system that uses a potential field to derive motion that respects the personʹs social zones and perceived interest in interaction. The operation of the system is evaluated in a controlled scenario in an open hall environment. It is demonstrated that the robot is able to learn to estimate if a person...... wishes to interact, and that the system is capable of adapting to changing behaviours of the humans in the environment....

Arousal regulation and affective adaptation to human responsiveness by a robot that explores and learns a novel environment.

Science.gov (United States)

Hiolle, Antoine; Lewis, Matthew; Cañamero, Lola

2014-01-01

In the context of our work in developmental robotics regarding robot-human caregiver interactions, in this paper we investigate how a "baby" robot that explores and learns novel environments can adapt its affective regulatory behavior of soliciting help from a "caregiver" to the preferences shown by the caregiver in terms of varying responsiveness. We build on two strands of previous work that assessed independently (a) the differences between two "idealized" robot profiles-a "needy" and an "independent" robot-in terms of their use of a caregiver as a means to regulate the "stress" (arousal) produced by the exploration and learning of a novel environment, and (b) the effects on the robot behaviors of two caregiving profiles varying in their responsiveness-"responsive" and "non-responsive"-to the regulatory requests of the robot. Going beyond previous work, in this paper we (a) assess the effects that the varying regulatory behavior of the two robot profiles has on the exploratory and learning patterns of the robots; (b) bring together the two strands previously investigated in isolation and take a step further by endowing the robot with the capability to adapt its regulatory behavior along the "needy" and "independent" axis as a function of the varying responsiveness of the caregiver; and (c) analyze the effects that the varying regulatory behavior has on the exploratory and learning patterns of the adaptive robot.

Implementing a robotics curriculum at an academic general surgery training program: our initial experience.

Science.gov (United States)

Winder, Joshua S; Juza, Ryan M; Sasaki, Jennifer; Rogers, Ann M; Pauli, Eric M; Haluck, Randy S; Estes, Stephanie J; Lyn-Sue, Jerome R

2016-09-01

The robotic surgical platform is being utilized by a growing number of hospitals across the country, including academic medical centers. Training programs are tasked with teaching their residents how to utilize this technology. To this end, we have developed and implemented a robotic surgical curriculum, and share our initial experience here. Our curriculum was implemented for all General Surgical residents for the academic year 2014-2015. The curriculum consisted of online training, readings, bedside training, console simulation, participating in ten cases as bedside first assistant, and operating at the console. 20 surgical residents were included. Residents were provided the curriculum and notified the department upon completion. Bedside assistance and operative console training were completed in the operating room through a mix of biliary, foregut, and colorectal cases. During the fiscal years of 2014 and 2015, there were 164 and 263 robot-assisted surgeries performed within the General Surgery Department, respectively. All 20 residents completed the online and bedside instruction portions of the curriculum. Of the 20 residents trained, 13/20 (65 %) sat at the Surgeon console during at least one case. Utilizing this curriculum, we have trained and incorporated residents into robot-assisted cases in an efficient manner. A successful curriculum must be based on didactic learning, reading, bedside training, simulation, and training in the operating room. Each program must examine their caseload and resident class to ensure proper exposure to this platform.

Effect of visual distraction and auditory feedback on patient effort during robot-assisted movement training after stroke

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Reinkensmeyer David J

2011-04-01

Full Text Available Abstract Background Practicing arm and gait movements with robotic assistance after neurologic injury can help patients improve their movement ability, but patients sometimes reduce their effort during training in response to the assistance. Reduced effort has been hypothesized to diminish clinical outcomes of robotic training. To better understand patient slacking, we studied the role of visual distraction and auditory feedback in modulating patient effort during a common robot-assisted tracking task. Methods Fourteen participants with chronic left hemiparesis from stroke, five control participants with chronic right hemiparesis and fourteen non-impaired healthy control participants, tracked a visual target with their arms while receiving adaptive assistance from a robotic arm exoskeleton. We compared four practice conditions: the baseline tracking task alone; tracking while also performing a visual distracter task; tracking with the visual distracter and sound feedback; and tracking with sound feedback. For the distracter task, symbols were randomly displayed in the corners of the computer screen, and the participants were instructed to click a mouse button when a target symbol appeared. The sound feedback consisted of a repeating beep, with the frequency of repetition made to increase with increasing tracking error. Results Participants with stroke halved their effort and doubled their tracking error when performing the visual distracter task with their left hemiparetic arm. With sound feedback, however, these participants increased their effort and decreased their tracking error close to their baseline levels, while also performing the distracter task successfully. These effects were significantly smaller for the participants who used their non-paretic arm and for the participants without stroke. Conclusions Visual distraction decreased participants effort during a standard robot-assisted movement training task. This effect was greater for

Modbus RTU protocol and arduino IO package: A real time implementation of a 3 finger adaptive robot gripper

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Sadun Amirul Syafiq

2017-01-01

Full Text Available Recently, the Modbus RTU protocol has been widely accepted in the application of robotics, communications and industrial control systems due to its simplicity and reliability. With the help of the MATLAB Instrument Control Toolbox, a serial communication between Simulink and a 3 Finger Adaptive Robot Gripper can be realized to demonstrate a grasping functionality. The toolbox includes a “to instrument” and “query instrument” programming blocks that enable the users to create a serial communication with the targeted hardware/robot. Similarly, the Simulink Arduino IO package also offers a real-time feature that enabled it to act as a DAQ device. This paper establishes a real-time robot control by using Modbus RTU and Arduino IO Package for a 3 Finger Adaptive Robot Gripper. The robot communication and grasping performance were successfully implemented and demonstrated. In particular, three (3 different grasping mode via normal, wide and pinch were tested. Moreover, the robot gripper’s feedback data, such as encoder position, motor current and the grasping force were easily measured and acquired in real-time. This certainly essential for future grasping analysis of a 3 Finger Adaptive Robot Gripper.

Effects of Robot Assisted Gait Training in Progressive Supranuclear Palsy (PSP: a preliminary report.

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Patrizio eSale

2014-04-01

Full Text Available Background and Purpose: Progressive supranuclear palsy (PSP is a rare neurodegenerative disease clinically characterized by prominent axial extrapyramidal motor symptoms with frequent falls. Over the last years the introduction of robotic technologies to recover lower limb function has been greatly employed in the rehabilitative practice. This observational trial is aimed at investigating the feasibility, the effectiveness and the efficacy of end-effector robot training in people with PSP.Method: Pilot observational trial.Participants: Five cognitively intact participants with PSP and gait disorders.Interventions: Patients were submitted to a rehabilitative program of robot-assisted walking sessions for 45 minutes, 5 times a week for 4 weeks.Main outcome measures: The spatiotemporal parameters at the beginning (T0 and at the end of treatment (T1 were recorded by a gait analysis laboratory.Results: Robot training was feasible, acceptable and safe and all participants completed the prescribed training sessions. All patients showed an improvement in the gait index (Mean velocity, Cadence, Step length and Step width (T0 versus T1.Conclusions: Robot training is a feasible and safe form of rehabilitation for cognitively intact people with PSP. This innovative approach can contribute to improve lower limb motor recovery. The focus on gait recovery is another quality that makes this research important for clinical practice. On the whole, the simplicity of treatment, the lack of side effects and the positive results in the patients support the recommendation to extend the trials of this treatment. Further investigation regarding the effectiveness of robot training in time is necessary.Trial registration: ClinicalTrials.gov NCT01668407.

Adaptive control of two-wheeled mobile balance robot capable to adapt different surfaces using a novel artificial neural network–based real-time switching dynamic controller

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Ali Unluturk

2017-03-01

Full Text Available In this article, a novel real-time artificial neural network–based adaptable switching dynamic controller is developed and practically implemented. It will be used for real-time control of two-wheeled balance robot which can balance itself upright position on different surfaces. In order to examine the efficiency of the proposed controller, a two-wheeled mobile balance robot is designed and a test platform for experimental setup is made for balance problem on different surfaces. In a developed adaptive controller algorithm which is capable to adapt different surfaces, mean absolute target angle deviation error, mean absolute target displacement deviation error and mean absolute controller output data are employed for surface estimation by using artificial neural network. In a designed two-wheeled mobile balance robot system, robot tilt angle is estimated via Kalman filter from accelerometer and gyroscope sensor signals. Furthermore, a visual robot control interface is developed in C++ software development environment so that robot controller parameters can be changed as desired. In addition, robot balance angle, linear displacement and controller output can be observed online on personal computer. According to the real-time experimental results, the proposed novel type controller gives more effective results than the classic ones.

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot.

Science.gov (United States)

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles

Training Modalities to Increase Sensorimotor Adaptability

Science.gov (United States)

Bloomberg, J. J.; Mulavara, A. P.; Peters, B. T.; Brady, R.; Audas, C.; Cohen, H. S.

2009-01-01

During the acute phase of adaptation to novel gravitational environments, sensorimotor disturbances have the potential to disrupt the ability of astronauts to perform required mission tasks. The goal of our current series of studies is develop a sensorimotor adaptability (SA) training program designed to facilitate recovery of functional capabilities when astronauts transition to different gravitational environments. The project has conducted a series of studies investigating the efficacy of treadmill training combined with a variety of sensory challenges (incongruent visual input, support surface instability) designed to increase adaptability. SA training using a treadmill combined with exposure to altered visual input was effective in producing increased adaptability in a more complex over-ground ambulatory task on an obstacle course. This confirms that for a complex task like walking, treadmill training contains enough of the critical features of overground walking to be an effective training modality. SA training can be optimized by using a periodized training schedule. Test sessions that each contain short-duration exposures to multiple perturbation stimuli allows subjects to acquire a greater ability to rapidly reorganize appropriate response strategies when encountering a novel sensory environment. Using a treadmill mounted on top of a six degree-of-freedom motion base platform we investigated locomotor training responses produced by subjects introduced to a dynamic walking surface combined with alterations in visual flow. Subjects who received this training had improved locomotor performance and faster reaction times when exposed to the novel sensory stimuli compared to control subjects. Results also demonstrate that individual sensory biases (i.e. increased visual dependency) can predict adaptive responses to novel sensory environments suggesting that individual training prescription can be developed to enhance adaptability. These data indicate that SA

Intelligent, self-contained robotic hand

Science.gov (United States)

Krutik, Vitaliy; Doo, Burt; Townsend, William T.; Hauptman, Traveler; Crowell, Adam; Zenowich, Brian; Lawson, John

2007-01-30

A robotic device has a base and at least one finger having at least two links that are connected in series on rotary joints with at least two degrees of freedom. A brushless motor and an associated controller are located at each joint to produce a rotational movement of a link. Wires for electrical power and communication serially connect the controllers in a distributed control network. A network operating controller coordinates the operation of the network, including power distribution. At least one, but more typically two to five, wires interconnect all the controllers through one or more joints. Motor sensors and external world sensors monitor operating parameters of the robotic hand. The electrical signal output of the sensors can be input anywhere on the distributed control network. V-grooves on the robotic hand locate objects precisely and assist in gripping. The hand is sealed, immersible and has electrical connections through the rotary joints for anodizing in a single dunk without masking. In various forms, this intelligent, self-contained, dexterous hand, or combinations of such hands, can perform a wide variety of object gripping and manipulating tasks, as well as locomotion and combinations of locomotion and gripping.

Market-Based Coordination and Auditing Mechanisms for Self-Interested Multi-Robot Systems

Science.gov (United States)

Ham, MyungJoo

2009-01-01

We propose market-based coordinated task allocation mechanisms, which allocate complex tasks that require synchronized and collaborated services of multiple robot agents to robot agents, and an auditing mechanism, which ensures proper behaviors of robot agents by verifying inter-agent activities, for self-interested, fully-distributed, and…

A distance weighted-based approach for self-organized aggregation in robot swarms

KAUST Repository

Khaldi, Belkacem

2017-12-14

In this paper, a Distance-Weighted K Nearest Neighboring (DW-KNN) topology is proposed to study self-organized aggregation as an emergent swarming behavior within robot swarms. A virtual physics approach is applied among the proposed neighborhood topology to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach is used as a key factor to identify the K-Nearest neighbors taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbors is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach showing various self-organized aggregations performed by a swarm of N foot-bot robots.

Fuzzy adaptive robust control for space robot considering the effect of the gravity

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Qin Li

2014-12-01

Full Text Available Space robot is assembled and tested in gravity environment, and completes on-orbit service (OOS in microgravity environment. The kinematic and dynamic characteristic of the robot will change with the variations of gravity in different working condition. Fully considering the change of kinematic and dynamic models caused by the change of gravity environment, a fuzzy adaptive robust control (FARC strategy which is adaptive to these model variations is put forward for trajectory tracking control of space robot. A fuzzy algorithm is employed to approximate the nonlinear uncertainties in the model, adaptive laws of the parameters are constructed, and the approximation error is compensated by using a robust control algorithm. The stability of the control system is guaranteed based on the Lyapunov theory and the trajectory tracking control simulation is performed. The simulation results are compared with the proportional plus derivative (PD controller, and the effectiveness to achieve better trajectory tracking performance under different gravity environment without changing the control parameters and the advantage of the proposed controller are verified.

Human-like Compliance for Dexterous Robot Hands

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Jau, Bruno M.

1995-01-01

This paper describes the Active Electromechanical Compliance (AEC) system that was developed for the Jau-JPL anthropomorphic robot. The AEC system imitates the functionality of the human muscle's secondary function, which is to control the joint's stiffness: AEC is implemented through servo controlling the joint drive train's stiffness. The control strategy, controlling compliant joints in teleoperation, is described. It enables automatic hybrid position and force control through utilizing sensory feedback from joint and compliance sensors. This compliant control strategy is adaptable for autonomous robot control as well. Active compliance enables dual arm manipulations, human-like soft grasping by the robot hand, and opens the way to many new robotics applications.

Nutrition and training adaptations in aquatic sports.

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Mujika, Iñigo; Stellingwerff, Trent; Tipton, Kevin

2014-08-01

The adaptive response to training is determined by the combination of the intensity, volume, and frequency of the training. Various periodized approaches to training are used by aquatic sports athletes to achieve performance peaks. Nutritional support to optimize training adaptations should take periodization into consideration; that is, nutrition should also be periodized to optimally support training and facilitate adaptations. Moreover, other aspects of training (e.g., overload training, tapering and detraining) should be considered when making nutrition recommendations for aquatic athletes. There is evidence, albeit not in aquatic sports, that restricting carbohydrate availability may enhance some training adaptations. More research needs to be performed, particularly in aquatic sports, to determine the optimal strategy for periodizing carbohydrate intake to optimize adaptations. Protein nutrition is an important consideration for optimal training adaptations. Factors other than the total amount of daily protein intake should be considered. For instance, the type of protein, timing and pattern of protein intake and the amount of protein ingested at any one time influence the metabolic response to protein ingestion. Body mass and composition are important for aquatic sport athletes in relation to power-to-mass and for aesthetic reasons. Protein may be particularly important for athletes desiring to maintain muscle while losing body mass. Nutritional supplements, such as b-alanine and sodium bicarbonate, may have particular usefulness for aquatic athletes' training adaptation.

Design optimization on the drive train of a light-weight robotic arm

DEFF Research Database (Denmark)

Zhou, Lelai; Bai, Shaoping; Hansen, Michael Rygaard

2011-01-01

A drive train optimization method for design of light-weight robots is proposed. Optimal selections of motors and gearboxes from a limited catalog of commercially available components are done simultaneously for all joints of a robotic arm. Characteristics of the motor and gearbox, including gear...... ratio, gear inertia, motor inertia, and gear efficiency, are considered in the drive train modeling. A co-simulation method is developed for dynamic simulation of the arm. A design example is included to demonstrate the proposed design optimization method....

Structural brain changes after traditional and robot-assisted multi-domain cognitive training in community-dwelling healthy elderly.

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Geon Ha Kim

Full Text Available The purpose of this study was to investigate if multi-domain cognitive training, especially robot-assisted training, alters cortical thickness in the brains of elderly participants. A controlled trial was conducted with 85 volunteers without cognitive impairment who were 60 years old or older. Participants were first randomized into two groups. One group consisted of 48 participants who would receive cognitive training and 37 who would not receive training. The cognitive training group was randomly divided into two groups, 24 who received traditional cognitive training and 24 who received robot-assisted cognitive training. The training for both groups consisted of daily 90-min-session, five days a week for a total of 12 weeks. The primary outcome was the changes in cortical thickness. When compared to the control group, both groups who underwent cognitive training demonstrated attenuation of age related cortical thinning in the frontotemporal association cortices. When the robot and the traditional interventions were directly compared, the robot group showed less cortical thinning in the anterior cingulate cortices. Our results suggest that cognitive training can mitigate age-associated structural brain changes in the elderly.ClnicalTrials.gov NCT01596205.

Modeling and Simulation to Muscle Strength Training of Lower Limbs Rehabilitation Robots

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Ke-Yi Wang

2015-01-01

Full Text Available Considering the issues of lower limb rehabilitation robots with single control strategies and poor training types, a training method for improving muscle strength was put forward in this paper. Patients’ muscle strength could be achieved by targeted exercises at the end of rehabilitation. This approach could be realized through programming wires’ force. On the one hand, each wires force was measured by tension sensor and force closed loop control was established to control the value of wires’ force which was acted on trainees. On the other hand, the direction of output force was changed by detecting the trainees’ state of motion and the way of putting load to patient was achieved. Finally, the target of enhancing patients’ muscle strength was realized. Dynamic model was built by means of mechanism and training types of robots. Force closed loop control strategy was established based on training pattern. In view of the characteristics of the redundance and economy of wire control, the process for simple wire's load changes was discussed. In order to confirm the characteristics of robot control system, the controller was simulated in Matlab/Simulink. It was verified that command signal could be traced by control system availably and the load during muscle training would be provided effectively.

Is body-weight-supported treadmill training or robotic-assisted gait training superior to overground gait training and other forms of physiotherapy in people with spinal cord injury? A systematic review.

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Mehrholz, J; Harvey, L A; Thomas, S; Elsner, B

2017-08-01

Systematic review about randomised trials comparing different training strategies to improve gait in people with spinal cord injuries (SCI). The aim of this systematic review was to compare the effectiveness of body-weight-supported treadmill training (BWSTT) and robotic-assisted gait training with overground gait training and other forms of physiotherapy in people with traumatic SCI. Systematic review conducted by researchers from Germany and Australia. An extensive search was conducted for randomised controlled trials involving people with traumatic SCI that compared either BWSTT or robotic-assisted gait training with overground gait training and other forms of physiotherapy. The two outcomes of interest were walking speed (m s -1 ) and walking distance (m). BWSTT and robotic-assisted gait training were analysed separately, and data were pooled across trials to derive mean between-group differences using a random-effects model. Thirteen randomised controlled trials involving 586 people were identified. Ten trials involving 462 participants compared BWSTT to overground gait training and other forms of physiotherapy, but only nine trials provided useable data. The pooled mean (95% confidence interval (CI)) between-group differences for walking speed and walking distance were -0.03 m s -1 (-0.10 to 0.04) and -7 m (-45 to 31), respectively, favouring overground gait training. Five trials involving 344 participants compared robotic-assisted gait training to overground gait training and other forms of physiotherapy but only three provided useable data. The pooled mean (95% CI) between-group differences for walking speed and walking distance were -0.04 m s -1 (95% CI -0.21 to 0.13) and -6 m (95% CI -86 to 74), respectively, favouring overground gait training. BWSTT and robotic-assisted gait training do not increase walking speed more than overground gait training and other forms of physiotherapy do, but their effects on walking distance are not clear.

Modeling of Two-Wheeled Self-Balancing Robot Driven by DC Gearmotors

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Frankovský P.

2017-08-01

Full Text Available This paper is aimed at modelling a two-wheeled self-balancing robot driven by the geared DC motors. A mathematical model consists of two main parts, the model of robot’s mechanical structure and the model of the actuator. Linearized equations of motion are derived and the overall model of the two-wheeled self-balancing robot is represented in state-space realization for the purpose of state feedback controller design.

Control of autonomous robot using neural networks

Science.gov (United States)

Barton, Adam; Volna, Eva

2017-07-01

The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.

Kinematics effectively delineate accomplished users of endovascular robotics with a physical training model.

Science.gov (United States)

Duran, Cassidy; Estrada, Sean; O'Malley, Marcia; Lumsden, Alan B; Bismuth, Jean

2015-02-01

Endovascular robotics systems, now approved for clinical use in the United States and Europe, are seeing rapid growth in interest. Determining who has sufficient expertise for safe and effective clinical use remains elusive. Our aim was to analyze performance on a robotic platform to determine what defines an expert user. During three sessions, 21 subjects with a range of endovascular expertise and endovascular robotic experience (novices 20 hours) performed four tasks on a training model. All participants completed a 2-hour training session on the robot by a certified instructor. Completion times, global rating scores, and motion metrics were collected to assess performance. Electromagnetic tracking was used to capture and to analyze catheter tip motion. Motion analysis was based on derivations of speed and position including spectral arc length and total number of submovements (inversely proportional to proficiency of motion) and duration of submovements (directly proportional to proficiency). Ninety-eight percent of competent subjects successfully completed the tasks within the given time, whereas 91% of noncompetent subjects were successful. There was no significant difference in completion times between competent and noncompetent users except for the posterior branch (151 s:105 s; P = .01). The competent users had more efficient motion as evidenced by statistically significant differences in the metrics of motion analysis. Users with >20 hours of experience performed significantly better than those newer to the system, independent of prior endovascular experience. This study demonstrates that motion-based metrics can differentiate novice from trained users of flexible robotics systems for basic endovascular tasks. Efficiency of catheter movement, consistency of performance, and learning curves may help identify users who are sufficiently trained for safe clinical use of the system. This work will help identify the learning curve and specific movements that

Obstacle avoidance for kinematically redundant robots using an adaptive fuzzy logic algorithm

International Nuclear Information System (INIS)

Beheshti, M.T.H.; Tehrani, A.K.

1999-05-01

In this paper the Adaptive Fuzzy Logic approach for solving the inverse kinematics of redundant robots in an environment with obstacles is presented. The obstacles are modeled as convex bodies. A fuzzy rule base that is updated via an adaptive law is used to solve the inverse kinematic problem. Additional rules have been introduced to take care of the obstacles avoidance problem. The proposed method has advantages such as high accuracy, simplicity of computations and generality for all redundant robots. Simulation results illustrate much better tracking performance than the dynamic base solution for a given trajectory in cartesian space, while guaranteeing a collision-free trajectory and observation of a mechanical joint limit

Self-attitude awareness training: An aid to effective performance in microgravity and virtual environments

Science.gov (United States)

Parker, Donald E.; Harm, D. L.; Florer, Faith L.

1993-01-01

This paper describes ongoing development of training procedures to enhance self-attitude awareness in astronaut trainees. The procedures are based on observations regarding self-attitude (perceived self-orientation and self-motion) reported by astronauts. Self-attitude awareness training is implemented on a personal computer system and consists of lesson stacks programmed using Hypertalk with Macromind Director movie imports. Training evaluation will be accomplished by an active search task using the virtual Spacelab environment produced by the Device for Orientation and Motion Environments Preflight Adaptation Trainer (DOME-PAT) as well as by assessment of astronauts' performance and sense of well-being during orbital flight. The general purpose of self-attitude awareness training is to use as efficiently as possible the limited DOME-PAT training time available to astronauts prior to a space mission. We suggest that similar training procedures may enhance the performance of virtual environment operators.

Retention of fundamental surgical skills learned in robot-assisted surgery.

Science.gov (United States)

Suh, Irene H; Mukherjee, Mukul; Shah, Bhavin C; Oleynikov, Dmitry; Siu, Ka-Chun

2012-12-01

Evaluation of the learning curve for robotic surgery has shown reduced errors and decreased task completion and training times compared with regular laparoscopic surgery. However, most training evaluations of robotic surgery have only addressed short-term retention after the completion of training. Our goal was to investigate the amount of surgical skills retained after 3 months of training with the da Vinci™ Surgical System. Seven medical students without any surgical experience were recruited. Participants were trained with a 4-day training program of robotic surgical skills and underwent a series of retention tests at 1 day, 1 week, 1 month, and 3 months post-training. Data analysis included time to task completion, speed, distance traveled, and movement curvature by the instrument tip. Performance of the participants was graded using the modified Objective Structured Assessment of Technical Skills (OSATS) for robotic surgery. Participants filled out a survey after each training session by answering a set of questions. Time to task completion and the movement curvature was decreased from pre- to post-training and the performance was retained at all the corresponding retention periods: 1 day, 1 week, 1 month, and 3 months. The modified OSATS showed improvement from pre-test to post-test and this improvement was maintained during all the retention periods. Participants increased in self-confidence and mastery in performing robotic surgical tasks after training. Our novel comprehensive training program improved robot-assisted surgical performance and learning. All trainees retained their fundamental surgical skills for 3 months after receiving the training program.

Self-Organization in Aggregating Robot Swarms: A DW-KNN Topological Approach

KAUST Repository

Khaldi, Belkacem

2018-02-02

In certain swarm applications, where the inter-agent distance is not the only factor in the collective behaviours of the swarm, additional properties such as density could have a crucial effect. In this paper, we propose applying a Distance-Weighted K-Nearest Neighbouring (DW-KNN) topology to the behaviour of robot swarms performing self-organized aggregation, in combination with a virtual physics approach to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach, which is used to evaluate the robot density in the swarm, is applied as the key factor for identifying the K-nearest neighbours taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbours is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach, showing various self-organized aggregations performed by a swarm of N foot-bot robots. Also, we compared the aggregation quality of DW-KNN aggregation approach to that of the conventional KNN approach and found better performance.

Robot-assisted gait training versus treadmill training in patients with Parkinson's disease: a kinematic evaluation with gait profile score.

Science.gov (United States)

Galli, M; Cimolin, V; De Pandis, M F; Le Pera, D; Sova, I; Albertini, G; Stocchi, F; Franceschini, M

2016-01-01

The purpose of this study was to quantitatively compare the effects, on walking performance, of end-effector robotic rehabilitation locomotor training versus intensive training with a treadmill in Parkinson's disease (PD). Fifty patients with PD were randomly divided into two groups: 25 were assigned to the robot-assisted therapy group (RG) and 25 to the intensive treadmill therapy group (IG). They were evaluated with clinical examination and 3D quantitative gait analysis [gait profile score (GPS) and its constituent gait variable scores (GVSs) were calculated from gait analysis data] at the beginning (T0) and at the end (T1) of the treatment. In the RG no differences were found in the GPS, but there were significant improvements in some GVSs (Pelvic Obl and Hip Ab-Add). The IG showed no statistically significant changes in either GPS or GVSs. The end-effector robotic rehabilitation locomotor training improved gait kinematics and seems to be effective for rehabilitation in patients with mild PD.

Social Intelligence for a Robot Engaging People in Cognitive Training Activities

Directory of Open Access Journals (Sweden)

Jeanie Chan

2012-10-01

Full Text Available Current research supports the use of cognitive training interventions to improve the brain functioning of both adults and children. Our work focuses on exploring the potential use of robot assistants to allow for these interventions to become more accessible. Namely, we aim to develop an intelligent, socially assistive robot that can engage individuals in person-centred cognitively stimulating activities. In this paper, we present the design of a novel control architecture for the robot Brian 2.0, which enables the robot to be a social motivator by providing assistance, encouragement and celebration during an activity. A hierarchical reinforcement learning approach is used in the architecture to allow the robot to: 1 learn appropriate assistive behaviours based on the structure of the activity, and 2 personalize an interaction based on user states. Experiments show that the control architecture is effective in determining the robot's optimal assistive behaviours during a memory game interaction.

Transformers: Shape-Changing Space Systems Built with Robotic Textiles

Science.gov (United States)

Stoica, Adrian

2013-01-01

Prior approaches to transformer-like robots had only very limited success. They suffer from lack of reliability, ability to integrate large surfaces, and very modest change in overall shape. Robots can now be built from two-dimensional (2D) layers of robotic fabric. These transformers, a new kind of robotic space system, are dramatically different from current systems in at least two ways. First, the entire transformer is built from a single, thin sheet; a flexible layer of a robotic fabric (ro-fabric); or robotic textile (ro-textile). Second, the ro-textile layer is foldable to small volume and self-unfolding to adapt shape and function to mission phases.

Design and implementation of self-balancing coaxial two wheel robot based on HSIC

Science.gov (United States)

Hu, Tianlian; Zhang, Hua; Dai, Xin; Xia, Xianfeng; Liu, Ran; Qiu, Bo

2007-12-01

This thesis has studied the control problem concerning position and orientation control of self-balancing coaxial two wheel robot based on the human simulated intelligent control (HSIC) theory. Adopting Lagrange equation, the dynamic model of self-balancing coaxial two-wheel Robot is built up, and the Sensory-motor Intelligent Schemas (SMIS) of HSIC controller for the robot is designed by analyzing its movement and simulating the human controller. In robot's motion process, by perceiving position and orientation of the robot and using multi-mode control strategy based on characteristic identification, the HSIC controller enables the robot to control posture. Utilizing Matlab/Simulink, a simulation platform is established and a motion controller is designed and realized based on RT-Linux real-time operating system, employing high speed ARM9 processor S3C2440 as kernel of the motion controller. The effectiveness of the new design is testified by the experiment.

Cognitive training for technical and non-technical skills in robotic surgery: a randomised controlled trial.

Science.gov (United States)

Raison, Nicholas; Ahmed, Kamran; Abe, Takashige; Brunckhorst, Oliver; Novara, Giacomo; Buffi, Nicolò; McIlhenny, Craig; van der Poel, Henk; van Hemelrijck, Mieke; Gavazzi, Andrea; Dasgupta, Prokar

2018-05-07

To investigate the effectiveness of motor imagery (MI) for technical skill and non-technical skill (NTS) training in minimally invasive surgery (MIS). A single-blind, parallel-group randomised controlled trial was conducted at the Vattikuti Institute of Robotic Surgery, King's College London. Novice surgeons were recruited by open invitation in 2015. After basic robotic skills training, participants underwent simple randomisation to either MI training or standard training. All participants completed a robotic urethrovesical anastomosis task within a simulated operating room. In addition to the technical task, participants were required to manage three scripted NTS scenarios. Assessment was performed by five blinded expert surgeons and a NTS expert using validated tools for evaluating technical skills [Global Evaluative Assessment of Robotic Skills (GEARS)] and NTS [Non-Technical Skills for Surgeons (NOTSS)]. Quality of MI was assessed using a revised Movement Imagery Questionnaire (MIQ). In all, 33 participants underwent MI training and 29 underwent standard training. Interrater reliability was high, Krippendorff's α = 0.85. After MI training, the mean (sd) GEARS score was significantly higher than after standard training, at 13.1 (3.25) vs 11.4 (2.97) (P = 0.03). There was no difference in mean NOTSS scores, at 25.8 vs 26.4 (P = 0.77). MI training was successful with significantly higher imagery scores than standard training (mean MIQ score 5.1 vs 4.5, P = 0.04). Motor imagery is an effective training tool for improving technical skill in MIS even in novice participants. No beneficial effect for NTS was found. © 2018 The Authors BJU International © 2018 BJU International Published by John Wiley & Sons Ltd.

Pointing with a One-Eyed Cursor for Supervised Training in Minimally Invasive Robotic Surgery

DEFF Research Database (Denmark)

Kibsgaard, Martin; Kraus, Martin

2016-01-01

Pointing in the endoscopic view of a surgical robot is a natural and effcient way for instructors to communicate with trainees in robot-assisted minimally invasive surgery. However, pointing in a stereo-endoscopic view can be limited by problems such as video delay, double vision, arm fatigue......-day training units in robot- assisted minimally invasive surgery on anaesthetised pigs....

Self-organized control in cooperative robots using a pattern formation principle

DEFF Research Database (Denmark)

Starke, Jens; Ellsaesser, Carmen; Fukuda, Toshio

2011-01-01

problems where robot teams have to serve manufacturing targets such that an objective function is optimized. Feasibility of the self-organized solutions can be guaranteed even for unpredictable situations like sudden changes in the demands or breakdowns of robots. As example an uncrewed space mission...

Using robot-applied resistance to augment body-weight-supported treadmill training in an individual with incomplete spinal cord injury.

Science.gov (United States)

Lam, Tania; Pauhl, Katherine; Krassioukov, Andrei; Eng, Janice J

2011-01-01

The efficacy of task-specific gait training for people with spinal cord injury (SCI) is premised on evidence that the provision of gait-related afferent feedback is key for the recovery of stepping movements. Recent findings have shown that sensory feedback from flexor muscle afferents can facilitate flexor muscle activity during the swing phase of walking. This case report was undertaken to determine the feasibility of using robot-applied forces to resist leg movements during body-weight-supported treadmill training (BWSTT) and to measure its effect on gait and other health-related outcomes. The patient described in this case report was a 43-year-old man with a T11 incomplete chronic SCI. He underwent 36 sessions of BWSTT using a robotic gait orthosis to provide forces that resist hip and knee flexion. Tolerance to the training program was monitored using the Borg CR10 scale and heart rate and blood pressure changes during each training session. Outcome measures (ie, 10-Meter Walk Test, Six-Minute Walk Test, modified Emory Functional Ambulation Profile [mEFAP], Activities-specific Balance Confidence Scale, and Canadian Occupational Performance Measure) were completed and kinematic parameters of gait, lower-extremity muscle strength (force-generating capacity), lower-limb girth, and tolerance to orthostatic stress were measured before and after the training program. The patient could tolerate the training. Overground walking speed, endurance, and performance on all subtasks of the mEFAP improved and were accompanied by increased lower-limb joint flexion and toe clearance during gait. The patient's ambulatory self-confidence and self-perceived performance in walking also improved. These findings suggest that this new approach to BWSTT is a feasible and potentially effective therapy for improving skilled overground walking performance.

Versatile robotic interface to evaluate, enable and train locomotion and balance after neuromotor disorders.

Science.gov (United States)

Dominici, Nadia; Keller, Urs; Vallery, Heike; Friedli, Lucia; van den Brand, Rubia; Starkey, Michelle L; Musienko, Pavel; Riener, Robert; Courtine, Grégoire

2012-07-01

Central nervous system (CNS) disorders distinctly impair locomotor pattern generation and balance, but technical limitations prevent independent assessment and rehabilitation of these subfunctions. Here we introduce a versatile robotic interface to evaluate, enable and train pattern generation and balance independently during natural walking behaviors in rats. In evaluation mode, the robotic interface affords detailed assessments of pattern generation and dynamic equilibrium after spinal cord injury (SCI) and stroke. In enabling mode,the robot acts as a propulsive or postural neuroprosthesis that instantly promotes unexpected locomotor capacities including overground walking after complete SCI, stair climbing following partial SCI and precise paw placement shortly after stroke. In training mode, robot-enabled rehabilitation, epidural electrical stimulation and monoamine agonists reestablish weight-supported locomotion, coordinated steering and balance in rats with a paralyzing SCI. This new robotic technology and associated concepts have broad implications for both assessing and restoring motor functions after CNS disorders, both in animals and in humans.

Current status of robotic simulators in acquisition of robotic surgical skills.

Science.gov (United States)

Kumar, Anup; Smith, Roger; Patel, Vipul R

2015-03-01

This article provides an overview of the current status of simulator systems in robotic surgery training curriculum, focusing on available simulators for training, their comparison, new technologies introduced in simulation focusing on concepts of training along with existing challenges and future perspectives of simulator training in robotic surgery. The different virtual reality simulators available in the market like dVSS, dVT, RoSS, ProMIS and SEP have shown face, content and construct validity in robotic skills training for novices outside the operating room. Recently, augmented reality simulators like HoST, Maestro AR and RobotiX Mentor have been introduced in robotic training providing a more realistic operating environment, emphasizing more on procedure-specific robotic training . Further, the Xperience Team Trainer, which provides training to console surgeon and bed-side assistant simultaneously, has been recently introduced to emphasize the importance of teamwork and proper coordination. Simulator training holds an important place in current robotic training curriculum of future robotic surgeons. There is a need for more procedure-specific augmented reality simulator training, utilizing advancements in computing and graphical capabilities for new innovations in simulator technology. Further studies are required to establish its cost-benefit ratio along with concurrent and predictive validity.

Stereoscopic Augmented Reality System for Supervised Training on Minimal Invasive Surgery Robots

DEFF Research Database (Denmark)

Matu, Florin-Octavian; Thøgersen, Mikkel; Galsgaard, Bo

2014-01-01

the need for efficient training. When training with the robot, the communication between the trainer and the trainee is limited, since the trainee often cannot see the trainer. To overcome this issue, this paper proposes an Augmented Reality (AR) system where the trainer is controlling two virtual robotic...... arms. These arms are virtually superimposed on the video feed to the trainee, and can therefore be used to demonstrate and perform various tasks for the trainee. Furthermore, the trainer is presented with a 3D image through a stereoscopic display. Because of the added depth perception, this enables...... the procedure, and thereby enhances the training experience. The virtual overlay was also found to work as a good and illustrative approach for enhanced communication. However, the delay of the prototype made it difficult to use for actual training....

Supervisory Adaptive Network-Based Fuzzy Inference System (SANFIS Design for Empirical Test of Mobile Robot

Directory of Open Access Journals (Sweden)

Yi-Jen Mon

2012-10-01

Full Text Available A supervisory Adaptive Network-based Fuzzy Inference System (SANFIS is proposed for the empirical control of a mobile robot. This controller includes an ANFIS controller and a supervisory controller. The ANFIS controller is off-line tuned by an adaptive fuzzy inference system, the supervisory controller is designed to compensate for the approximation error between the ANFIS controller and the ideal controller, and drive the trajectory of the system onto a specified surface (called the sliding surface or switching surface while maintaining the trajectory onto this switching surface continuously to guarantee the system stability. This SANFIS controller can achieve favourable empirical control performance of the mobile robot in the empirical tests of driving the mobile robot with a square path. Practical experimental results demonstrate that the proposed SANFIS can achieve better control performance than that achieved using an ANFIS controller for empirical control of the mobile robot.

Friendship with a robot: Children's perception of similarity between a robot's physical and virtual embodiment that supports diabetes self-management.

Science.gov (United States)

Sinoo, Claudia; van der Pal, Sylvia; Blanson Henkemans, Olivier A; Keizer, Anouk; Bierman, Bert P B; Looije, Rosemarijn; Neerincx, Mark A

2018-02-21

The PAL project develops a conversational agent with a physical (robot) and virtual (avatar) embodiment to support diabetes self-management of children ubiquitously. This paper assesses 1) the effect of perceived similarity between robot and avatar on children's' friendship towards the avatar, and 2) the effect of this friendship on usability of a self-management application containing the avatar (a) and children's motivation to play with it (b). During a four-day diabetes camp in the Netherlands, 21 children participated in interactions with both agent embodiments. Questionnaires measured perceived similarity, friendship, motivation to play with the app and its usability. Children felt stronger friendship towards the physical robot than towards the avatar. The more children perceived the robot and its avatar as the same agency, the stronger their friendship with the avatar was. The stronger their friendship with the avatar, the more they were motivated to play with the app and the higher the app scored on usability. The combination of physical and virtual embodiments seems to provide a unique opportunity for building ubiquitous long-term child-agent friendships. an avatar complementing a physical robot in health care could increase children's motivation and adherence to use self-management support systems. Copyright © 2018 Elsevier B.V. All rights reserved.

Satisfaction and perceptions of long-term manual wheelchair users with a spinal cord injury upon completion of a locomotor training program with an overground robotic exoskeleton.

Science.gov (United States)

Gagnon, Dany H; Vermette, Martin; Duclos, Cyril; Aubertin-Leheudre, Mylène; Ahmed, Sara; Kairy, Dahlia

2017-12-19

study are unanimously satisfied upon completion of a 6-8-week locomotor training program with the robotic exoskeleton and would recommend the program to their peers. All long-term manual wheelchair users with a spinal cord injury who participated in the study offered positive feedback about the robotic exoskeleton itself and feel it is easy to learn to perform sit-stand transfers and walk with the robotic exoskeleton. All long-term manual wheelchair users with a spinal cord injury who participated in the study predominantly perceived improvements in their overall health status, upper limb strength and endurance as well as in their sleep and psychological well-being upon completion of a 6-8-week locomotor training program with the robotic exoskeleton. All long-term manual wheelchair users with a spinal cord injury who participated in the study unanimously felt motivated to engage in a regular physical activity program adapted to their condition and most of them do plan to continue to participate in moderate-to-strenuous physical exercise. Additional research on clients' perspectives, especially satisfaction with the overground exoskeleton and locomotor training program attributes, is needed.

Toward cognitive robotics

Science.gov (United States)

Laird, John E.

2009-05-01

Our long-term goal is to develop autonomous robotic systems that have the cognitive abilities of humans, including communication, coordination, adapting to novel situations, and learning through experience. Our approach rests on the recent integration of the Soar cognitive architecture with both virtual and physical robotic systems. Soar has been used to develop a wide variety of knowledge-rich agents for complex virtual environments, including distributed training environments and interactive computer games. For development and testing in robotic virtual environments, Soar interfaces to a variety of robotic simulators and a simple mobile robot. We have recently made significant extensions to Soar that add new memories and new non-symbolic reasoning to Soar's original symbolic processing, which should significantly improve Soar abilities for control of robots. These extensions include episodic memory, semantic memory, reinforcement learning, and mental imagery. Episodic memory and semantic memory support the learning and recalling of prior events and situations as well as facts about the world. Reinforcement learning provides the ability of the system to tune its procedural knowledge - knowledge about how to do things. Mental imagery supports the use of diagrammatic and visual representations that are critical to support spatial reasoning. We speculate on the future of unmanned systems and the need for cognitive robotics to support dynamic instruction and taskability.

Trainer variability during step training after spinal cord injury: Implications for robotic gait-training device design.

Science.gov (United States)

Galvez, Jose A; Budovitch, Amy; Harkema, Susan J; Reinkensmeyer, David J

2011-01-01

Robotic devices are being developed to automate repetitive aspects of walking retraining after neurological injuries, in part because they might improve the consistency and quality of training. However, it is unclear how inconsistent manual training actually is or whether stepping quality depends strongly on the trainers' manual skill. The objective of this study was to quantify trainer variability of manual skill during step training using body-weight support on a treadmill and assess factors of trainer skill. We attached a sensorized orthosis to one leg of each patient with spinal cord injury and measured the shank kinematics and forces exerted by different trainers during six training sessions. An expert trainer rated the trainers' skill level based on videotape recordings. Between-trainer force variability was substantial, about two times greater than within-trainer variability. Trainer skill rating correlated strongly with two gait features: better knee extension during stance and fewer episodes of toe dragging. Better knee extension correlated directly with larger knee horizontal assistance force, but better toe clearance did not correlate with larger ankle push-up force; rather, it correlated with better knee and hip extension. These results are useful to inform robotic gait-training design.

Feasibility of the adaptive and automatic presentation of tasks (ADAPT system for rehabilitation of upper extremity function post-stroke

Directory of Open Access Journals (Sweden)

Choi Younggeun

2011-08-01

Full Text Available Abstract Background Current guidelines for rehabilitation of arm and hand function after stroke recommend that motor training focus on realistic tasks that require reaching and manipulation and engage the patient intensively, actively, and adaptively. Here, we investigated the feasibility of a novel robotic task-practice system, ADAPT, designed in accordance with such guidelines. At each trial, ADAPT selects a functional task according to a training schedule and with difficulty based on previous performance. Once the task is selected, the robot picks up and presents the corresponding tool, simulates the dynamics of the tasks, and the patient interacts with the tool to perform the task. Methods Five participants with chronic stroke with mild to moderate impairments (> 9 months post-stroke; Fugl-Meyer arm score 49.2 ± 5.6 practiced four functional tasks (selected out of six in a pre-test with ADAPT for about one and half hour and 144 trials in a pseudo-random schedule of 3-trial blocks per task. Results No adverse events occurred and ADAPT successfully presented the six functional tasks without human intervention for a total of 900 trials. Qualitative analysis of trajectories showed that ADAPT simulated the desired task dynamics adequately, and participants reported good, although not excellent, task fidelity. During training, the adaptive difficulty algorithm progressively increased task difficulty leading towards an optimal challenge point based on performance; difficulty was then continuously adjusted to keep performance around the challenge point. Furthermore, the time to complete all trained tasks decreased significantly from pretest to one-hour post-test. Finally, post-training questionnaires demonstrated positive patient acceptance of ADAPT. Conclusions ADAPT successfully provided adaptive progressive training for multiple functional tasks based on participant's performance. Our encouraging results establish the feasibility of ADAPT; its

Robotically facilitated virtual rehabilitation of arm transport integrated with finger movement in persons with hemiparesis.

Science.gov (United States)

Merians, Alma S; Fluet, Gerard G; Qiu, Qinyin; Saleh, Soha; Lafond, Ian; Davidow, Amy; Adamovich, Sergei V

2011-05-16

Recovery of upper extremity function is particularly recalcitrant to successful rehabilitation. Robotic-assisted arm training devices integrated with virtual targets or complex virtual reality gaming simulations are being developed to deal with this problem. Neural control mechanisms indicate that reaching and hand-object manipulation are interdependent, suggesting that training on tasks requiring coordinated effort of both the upper arm and hand may be a more effective method for improving recovery of real world function. However, most robotic therapies have focused on training the proximal, rather than distal effectors of the upper extremity. This paper describes the effects of robotically-assisted, integrated upper extremity training. Twelve subjects post-stroke were trained for eight days on four upper extremity gaming simulations using adaptive robots during 2-3 hour sessions. The subjects demonstrated improved proximal stability, smoothness and efficiency of the movement path. This was in concert with improvement in the distal kinematic measures of finger individuation and improved speed. Importantly, these changes were accompanied by a robust 16-second decrease in overall time in the Wolf Motor Function Test and a 24-second decrease in the Jebsen Test of Hand Function. Complex gaming simulations interfaced with adaptive robots requiring integrated control of shoulder, elbow, forearm, wrist and finger movements appear to have a substantial effect on improving hemiparetic hand function. We believe that the magnitude of the changes and the stability of the patient's function prior to training, along with maintenance of several aspects of the gains demonstrated at retention make a compelling argument for this approach to training.

Self-sufficiency of an autonomous reconfigurable modular robotic organism

CERN Document Server

Qadir, Raja Humza

2015-01-01

This book describes how the principle of self-sufficiency can be applied to a reconfigurable modular robotic organism. It shows the design considerations for a novel REPLICATOR robotic platform, both hardware and software, featuring the behavioral characteristics of social insect colonies. Following a comprehensive overview of some of the bio-inspired techniques already available, and of the state-of-the-art in re-configurable modular robotic systems, the book presents a novel power management system with fault-tolerant energy sharing, as well as its implementation in the REPLICATOR robotic modules. In addition, the book discusses, for the first time, the concept of “artificial energy homeostasis” in the context of a modular robotic organism, and shows its verification on a custom-designed simulation framework in different dynamic power distribution and fault tolerance scenarios. This book offers an ideal reference guide for both hardware engineers and software developers involved in the design and implem...

Computational surgery and dual training computing, robotics and imaging

CERN Document Server

Bass, Barbara; Berceli, Scott; Collet, Christophe; Cerveri, Pietro

2014-01-01

This critical volume focuses on the use of medical imaging, medical robotics, simulation, and information technology in surgery. It offers a road map for computational surgery success,  discusses the computer-assisted management of disease and surgery, and provides a rational for image processing and diagnostic. This book also presents some advances on image-driven intervention and robotics, as well as evaluates models and simulations for a broad spectrum of cancers as well as cardiovascular, neurological, and bone diseases. Training and performance analysis in surgery assisted by robotic systems is also covered. This book also: ·         Provides a comprehensive overview of the use of computational surgery and disease management ·         Discusses the design and use of medical robotic tools for orthopedic surgery, endoscopic surgery, and prostate surgery ·         Provides practical examples and case studies in the areas of image processing, virtual surgery, and simulation traini...

Effort, performance, and motivation: insights from robot-assisted training of human golf putting and rat grip strength.

Science.gov (United States)

Duarte, Jaime E; Gebrekristos, Berkenesh; Perez, Sergi; Rowe, Justin B; Sharp, Kelli; Reinkensmeyer, David J

2013-06-01

Robotic devices can modulate success rates and required effort levels during motor training, but it is unclear how this affects performance gains and motivation. Here we present results from training unimpaired humans in a virtual golf-putting task, and training spinal cord injured (SCI) rats in a grip strength task using robotically modulated success rates and effort levels. Robotic assistance in golf practice increased trainees feelings of competence, and, paradoxically, increased their sense effort, even though it had mixed effects on learning. Reducing effort during a grip strength training task led rats with SCI to practice the task more frequently. However, the more frequent practice of these rats did not cause them to exceed the strength gains achieved by rats that exercised less often at higher required effort levels. These results show that increasing success and decreasing effort with robots increases motivation, but has mixed effects on performance gains.

Trainer variability during step training after spinal cord injury: Implications for robotic gait-training device design

OpenAIRE

Jose A. Galvez, PhD; Amy Budovitch, PT; Susan J. Harkema, PhD; David J. Reinkensmeyer, PhD

2011-01-01

Robotic devices are being developed to automate repetitive aspects of walking retraining after neurological injuries, in part because they might improve the consistency and quality of training. However, it is unclear how inconsistent manual training actually is or whether stepping quality depends strongly on the trainers' manual skill. The objective of this study was to quantify trainer variability of manual skill during step training using body-weight support on a treadmill and assess factor...

Comparison of three-dimensional, assist-as-needed robotic arm/hand movement training provided with Pneu-WREX to conventional tabletop therapy after chronic stroke.

Science.gov (United States)

Reinkensmeyer, David J; Wolbrecht, Eric T; Chan, Vicky; Chou, Cathy; Cramer, Steven C; Bobrow, James E

2012-11-01

Robot-assisted movement training can help individuals with stroke reduce arm and hand impairment, but robot therapy is typically only about as effective as conventional therapy. Refining the way that robots assist during training may make them more effective than conventional therapy. Here, the authors measured the therapeutic effect of a robot that required individuals with a stroke to achieve virtual tasks in three dimensions against gravity. The robot continuously estimated how much assistance patients needed to perform the tasks and provided slightly less assistance than needed to reduce patient slacking. Individuals with a chronic stroke (n = 26; baseline upper limb Fugl-Meyer score, 23 ± 8) were randomized into two groups and underwent 24 one-hour training sessions over 2 mos. One group received the assist-as-needed robot training and the other received conventional tabletop therapy with the supervision of a physical therapist. Training helped both groups significantly reduce their motor impairment, as measured by the primary outcome measure, the Fugl-Meyer score, but the improvement was small (3.0 ± 4.9 points for robot therapy vs. 0.9 ± 1.7 for conventional therapy). There was a trend for greater reduction for the robot-trained group (P = 0.07). The robot group largely sustained this gain at the 3-mo follow-up. The robot-trained group also experienced significant improvements in Box and Blocks score and hand grip strength, whereas the control group did not, but these improvements were not sustained at follow-up. In addition, the robot-trained group showed a trend toward greater improvement in sensory function, as measured by the Nottingham Sensory Test (P = 0.06). These results suggest that in patients with chronic stroke and moderate-severe deficits, assisting in three-dimensional virtual tasks with an assist-as-needed controller may make robotic training more effective than conventional tabletop training.

Robotics

Science.gov (United States)

Popov, E. P.; Iurevich, E. I.

The history and the current status of robotics are reviewed, as are the design, operation, and principal applications of industrial robots. Attention is given to programmable robots, robots with adaptive control and elements of artificial intelligence, and remotely controlled robots. The applications of robots discussed include mechanical engineering, cargo handling during transportation and storage, mining, and metallurgy. The future prospects of robotics are briefly outlined.

Automatic Modeling and Simulation of Modular Robots

Science.gov (United States)

Jiang, C.; Wei, H.; Zhang, Y.

2018-03-01

The ability of reconfiguration makes modular robots have the ability of adaptable, low-cost, self-healing and fault-tolerant. It can also be applied to a variety of mission situations. In this manuscript, a robot platform which relied on the module library was designed, based on the screw theory and module theory. Then, the configuration design method of the modular robot was proposed. And the different configurations of modular robot system have been built, including industrial mechanical arms, the mobile platform, six-legged robot and 3D exoskeleton manipulator. Finally, the simulation and verification of one system among them have been made, using the analyses of screw kinematics and polynomial planning. The results of experiments demonstrate the feasibility and superiority of this modular system.

Adapting a perinatal empathic training method from South Africa to Germany.

Science.gov (United States)

Knapp, Caprice; Honikman, Simone; Wirsching, Michael; Husni-Pascha, Gidah; Hänselmann, Eva

2018-01-01

Maternal mental health conditions are prevalent across the world. For women, the perinatal period is associated with increased rates of depression and anxiety. At the same time, there is widespread documentation of disrespectful care for women by maternity health staff. Improving the empathic engagement skills of maternity healthcare workers may enable them to respond to the mental health needs of their clients more effectively. In South Africa, a participatory empathic training method, the "Secret History" has been used as part of a national Department of Health training program with maternity staff and has showed promising results. For this paper, we aimed to describe an adaptation of the Secret History empathic training method from the South African to the German setting and to evaluate the adapted training. The pilot study occurred in an academic medical center in Germany. A focus group ( n  = 8) was used to adapt the training by describing the local context and changing the materials to be relevant to Germany. After adapting the materials, the pilot training was conducted with a mixed group of professionals ( n  = 15), many of whom were trainers themselves. A pre-post survey assessed the participants' empathy levels and attitudes towards the training method. In adapting the materials, the focus group discussion generated several experiences that were considered to be typical interpersonal and structural challenges facing healthcare workers in maternal care in Germany. These experiences were crafted into case scenarios that then formed the basis of the activities used in the Secret History empathic training pilot. Evaluation of the pilot training showed that although the participants had high levels of empathy in the pre-phase (100% estimated their empathic ability as high or very high), 69% became more aware of their own emotional experiences with patients and the need for self-care after the training. A majority, or 85%, indicated that the training

Robot-assisted gait training versus treadmill training in patients with Parkinson’s disease: a kinematic evaluation with gait profile score

Science.gov (United States)

Galli, Manuela; Cimolin, Veronica; De Pandis, Maria Francesca; Le Pera, Domenica; Sova, Ivan; Albertini, Giorgio; Stocchi, Fabrizio; Franceschini, Marco

2016-01-01

Summary The purpose of this study was to quantitatively compare the effects, on walking performance, of end-effector robotic rehabilitation locomotor training versus intensive training with a treadmill in Parkinson’s disease (PD). Fifty patients with PD were randomly divided into two groups: 25 were assigned to the robot-assisted therapy group (RG) and 25 to the intensive treadmill therapy group (IG). They were evaluated with clinical examination and 3D quantitative gait analysis [gait profile score (GPS) and its constituent gait variable scores (GVSs) were calculated from gait analysis data] at the beginning (T0) and at the end (T1) of the treatment. In the RG no differences were found in the GPS, but there were significant improvements in some GVSs (Pelvic Obl and Hip Ab-Add). The IG showed no statistically significant changes in either GPS or GVSs. The end-effector robotic rehabilitation locomotor training improved gait kinematics and seems to be effective for rehabilitation in patients with mild PD. PMID:27678210

Robo-AO KP: A new era in robotic adaptive optics

Science.gov (United States)

Riddle, Reed L.; Baranec, Christoph; Law, Nicholas M.; Kulkarni, Shrinivas R.; Duev, Dmitry; Ziegler, Carl; Jensen-Clem, Rebecca M.; Atkinson, Dani Eleanor; Tanner, Angelle M.; Zhang, Celia; Ray, Amy

2016-01-01

Robo-AO is the first and only fully automated adaptive optics laser guide star AO instrument. It was developed as an instrument for 1-3m robotic telescopes, in order to take advantage of their availability to pursue large survey programs and target of opportunity observations that aren't possible with other AO systems. Robo-AO is currently the most efficient AO system in existence, and it can achieve an observation rate of 20+ science targets per hour. In more than three years of operations at Palomar Observatory, it has been quite successful, producing technology that is being adapted by other AO systems and robotic telescope projects, as well as several high impact scientific publications. Now, Robo-AO has been selected to take over operation of the Kitt Peak National Observatory 2.1m telescope. This will give Robo-AO KP the opportunity to pursue multiple science programs consisting of several thousand targets each during the three years it will be on the telescope. One-sixth of the observing time will be allocated to the US community through the NOAO TAC process. This presentation will discuss the process adapting Robo-AO to the KPNO 2.1m telescope, the plans for integration and initial operations, and the science operations and programs to be pursued.

Awareness and Self-Awareness for Multi-Robot Organisms

OpenAIRE

Kernbach, Serge

2011-01-01

Awareness and self-awareness are two different notions related to knowing the environment and itself. In a general context, the mechanism of self-awareness belongs to a class of co-called "self-issues" (self-* or self-star): self-adaptation, self-repairing, self-replication, self-development or self-recovery. The self-* issues are connected in many ways to adaptability and evolvability, to the emergence of behavior and to the controllability of long-term developmental processes. Self-* are ei...

Slow Versus Fast Robot-Assisted Locomotor Training After Severe Stroke: A Randomized Controlled Trial.

Science.gov (United States)

Rodrigues, Thais Amanda; Goroso, Daniel Gustavo; Westgate, Philip M; Carrico, Cheryl; Batistella, Linamara R; Sawaki, Lumy

2017-10-01

Robot-assisted locomotor training on a bodyweight-supported treadmill is a rehabilitation intervention that compels repetitive practice of gait movements. Standard treadmill speed may elicit rhythmic movements generated primarily by spinal circuits. Slower-than-standard treadmill speed may elicit discrete movements, which are more complex than rhythmic movements and involve cortical areas. Compare effects of fast (i.e., rhythmic) versus slow (i.e., discrete) robot-assisted locomotor training on a bodyweight-supported treadmill in subjects with chronic, severe gait deficit after stroke. Subjects (N = 18) were randomized to receive 30 sessions (5 d/wk) of either fast or slow robot-assisted locomotor training on a bodyweight-supported treadmill in an inpatient setting. Functional ambulation category, time up and go, 6-min walk test, 10-m walk test, Berg Balance Scale, and Fugl-Meyer Assessment were administered at baseline and postintervention. The slow group had statistically significant improvement on functional ambulation category (first quartile-third quartile, P = 0.004), 6-min walk test (95% confidence interval [CI] = 1.8 to 49.0, P = 0.040), Berg Balance Scale (95% CI = 7.4 to 14.8, P locomotor training on a bodyweight-supported treadmill after severe stroke, slow training targeting discrete movement may yield greater benefit than fast training.

The influence of taekwondo training on school-life adaptation and exercise value in the United States.

Science.gov (United States)

Cho, Ik Rae; Park, Hyo Joo; Lee, Taek Kyun

2018-04-01

Previous experience has shown that school-based taekwondo training in the United States (US) results in many beneficial effect sregarding school education and the physical health of the adolescent participants; of especial significance, the training plays an important role in terms of exercise value and school-life adaptation. To explore this overall effect, a self-administered questionnaire was distributed to 401 adolescents over the age of 10 years. The survey comprisesa total of 29 questions that consist of 17 exercise-value-related questions (general, moral, and status) and 12 questions that are related to school-life adaptation (adaptation to teachers, adaptation to academic activities, adaptation to rule compliance, and adaptation to school activities). The survey results show that taekwondo training affects school-life adaptation by helping to improve student morality and by bolstering the students compliance with school rules during their schooling. The exercise value of taekwondo training is considered a necessity for US adolescents due to the corresponding educational aspects; in particular, the training plays a very important role in the maintenance of amenable student-teacher and student-peer relationships. From the previously mentioned findings, and if taekwondo teachers train their students carefully with educational missions in mind, it is expected that taekwondo training will play a very important role in the cultivation of anappropriate education value among US adolescents.

Self-generation of controller of an underwater robot with neural network

International Nuclear Information System (INIS)

Suto, T.; Ura, T.

1994-01-01

A self-organizing controller system is constructed based on artificial neural networks and applied to constant altitude swimming of the autonomous underwater robot PTEROA 150. The system consists of a controller and a forward model which calculates the values for evaluation as a result of control. Some methods are introduced for quick and appropriate adjustment of the controller network. Modification of the controller network is executed based on error-back-propagation method utilizing the forward model network. The forward model is divided into three sub-networks which represent dynamics of the vehicle, estimation of relative position to the seabed and calculation of the altitude. The proposed adaptive system is demonstrated in computer simulations where objective of a vehicle is keeping a constant altitude from seabed which is constituted of triangular ridges

Self discovery enables robot social cognition: are you my teacher?

Science.gov (United States)

Kaipa, Krishnanand N; Bongard, Josh C; Meltzoff, Andrew N

2010-01-01

Infants exploit the perception that others are 'like me' to bootstrap social cognition (Meltzoff, 2007a). This paper demonstrates how the above theory can be instantiated in a social robot that uses itself as a model to recognize structural similarities with other robots; this thereby enables the student to distinguish between appropriate and inappropriate teachers. This is accomplished by the student robot first performing self-discovery, a phase in which it uses actuation-perception relationships to infer its own structure. Second, the student models a candidate teacher using a vision-based active learning approach to create an approximate physical simulation of the teacher. Third, the student determines that the teacher is structurally similar (but not necessarily visually similar) to itself if it can find a neural controller that allows its self model (created in the first phase) to reproduce the perceived motion of the teacher model (created in the second phase). Fourth, the student uses the neural controller (created in the third phase) to move, resulting in imitation of the teacher. Results with a physical student robot and two physical robot teachers demonstrate the effectiveness of this approach. The generalizability of the proposed model allows it to be used over variations in the demonstrator: The student robot would still be able to imitate teachers of different sizes and at different distances from itself, as well as different positions in its field of view, because change in the interrelations of the teacher's body parts are used for imitation, rather than absolute geometric properties. Copyright © 2010 Elsevier Ltd. All rights reserved.

Structured training on the da Vinci Skills Simulator leads to improvement in technical performance of robotic novices.

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Walliczek-Dworschak, U; Mandapathil, M; Förtsch, A; Teymoortash, A; Dworschak, P; Werner, J A; Güldner, C

2017-02-01

The increasing use of minimally invasive techniques such as robotic-assisted devices raises the question of how to acquire robotic surgery skills. The da Vinci Skills Simulator has been demonstrated to be an effective training tool in previous reports. To date, little data are available on how to acquire proficiency through simulator training. We investigated the outcome of a structured training programme for robotic surgical skills by robotic novices. This prospective study was conducted from January to December 2013 using the da Vinci Skills Simulator. Twenty participants, all robotic novices, were enrolled in a 4-week training curriculum. After a brief introduction to the simulator system, three consecutive repetitions of five selected exercises (Match Board 1, 2, 3 and Ring and Rail 1, 2) were performed in a defined order on days 1, 8, 15 and 22. On day 22, one repetition of a previously unpractised more advanced module (Needle Targeting) was also performed. After completion of each study day, the overall performance, time to completion, economy in motion, instrument collisions, excessive instrument force, instruments out of view, master workspace range and number of drops were analysed. Comparing the first and final repetition, overall score and time needed to complete all exercises, economy of motion and instrument collisions were significantly improved in nearly all exercises. Regarding the new exercise, a positive training effect could be demonstrated. While its overall entry score was significantly higher, the time to completion and economy of motion were significantly lower than the scores on the first repetition of the previous 5 exercises. It could be shown that training on the da Vinci Skills Simulator led to an improvement in technical performance of robotic novices. With regard to a new exercise, the training had a positive effect on the technical performance. © 2016 John Wiley & Sons Ltd.

Evidence of Self-Directed Learning on a High School Robotics Team

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Nathan R. Dolenc

2014-12-01

Full Text Available Self-directed learning is described as an individual taking the initiative to engage in a learning experience while assuming responsibility to follow through to its conclusion. Robotics competitions are examples of informal environments that can facilitate self-directed learning. This study examined how mentor involvement, student behavior, and physical workspace contributed to self-directed learning on one robotics competition team. How did mentors transfer responsibility to students? How did students respond to managing a team? Are the physical attributes of a workspace important? The mentor, student, and workplace factors captured in the research showed mentors wanting students to do the work, students assuming leadership roles, and the limited workspace having a positive effect on student productivity.

Self-Rescue Mask Training

CERN Multimedia

2013-01-01

Nine new self-rescue mask instructors have been trained since early 2013, which provides CERN with a total of 26 self-rescue mask instructors to date. This will allow us to meet the increasing training needs caused by the Long Shut Down LS1.   The self-rescue mask instructors have trained 1650 persons in 2012 and about 500 persons since the beginning of the year on how to wear the masks properly. We thank all the instructors and all the persons that made this training possible. Please remember that the self-rescue masks training sessions are scheduled as follows: Basic course: Tuesday and Thursday mornings (2 sessions – 8.30 AM and 10.30 AM), duration:  1.30 hour, in French and English – registration via CERN online training catalogue – Course code 077Y00. Refresher training : Monday mornings (2 sessions – 8.30 AM and 10.30 AM), duration: 1.30 hour , in French and English – registration via CERN online training catalogue &...

Promoting training adaptations through nutritional interventions.

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Hawley, John A; Tipton, Kevin D; Millard-Stafford, Mindy L

2006-07-01

Training and nutrition are highly interrelated in that optimal adaptation to the demands of repeated training sessions typically requires a diet that can sustain muscle energy reserves. As nutrient stores (i.e. muscle and liver glycogen) play a predominant role in the performance of prolonged, intense, intermittent exercise typical of the patterns of soccer match-play, and in the replenishment of energy reserves for subsequent training sessions, the extent to which acutely altering substrate availability might modify the training impulse has been a key research area among exercise physiologists and sport nutritionists for several decades. Although the major perturbations to cellular homeostasis and muscle substrate stores occur during exercise, the activation of several major signalling pathways important for chronic training adaptations take place during the first few hours of recovery, returning to baseline values within 24 h after exercise. This has led to the paradigm that many chronic training adaptations are generated by the cumulative effects of the transient events that occur during recovery from each (acute) exercise bout. Evidence is accumulating that nutrient supplementation can serve as a potent modulator of many of the acute responses to both endurance and resistance training. In this article, we review the molecular and cellular events that occur in skeletal muscle during exercise and subsequent recovery, and the potential for nutrient supplementation (e.g. carbohydrate, fat, protein) to affect many of the adaptive responses to training.

Robotically facilitated virtual rehabilitation of arm transport integrated with finger movement in persons with hemiparesis

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Davidow Amy

2011-05-01

Full Text Available Abstract Background Recovery of upper extremity function is particularly recalcitrant to successful rehabilitation. Robotic-assisted arm training devices integrated with virtual targets or complex virtual reality gaming simulations are being developed to deal with this problem. Neural control mechanisms indicate that reaching and hand-object manipulation are interdependent, suggesting that training on tasks requiring coordinated effort of both the upper arm and hand may be a more effective method for improving recovery of real world function. However, most robotic therapies have focused on training the proximal, rather than distal effectors of the upper extremity. This paper describes the effects of robotically-assisted, integrated upper extremity training. Methods Twelve subjects post-stroke were trained for eight days on four upper extremity gaming simulations using adaptive robots during 2-3 hour sessions. Results The subjects demonstrated improved proximal stability, smoothness and efficiency of the movement path. This was in concert with improvement in the distal kinematic measures of finger individuation and improved speed. Importantly, these changes were accompanied by a robust 16-second decrease in overall time in the Wolf Motor Function Test and a 24-second decrease in the Jebsen Test of Hand Function. Conclusions Complex gaming simulations interfaced with adaptive robots requiring integrated control of shoulder, elbow, forearm, wrist and finger movements appear to have a substantial effect on improving hemiparetic hand function. We believe that the magnitude of the changes and the stability of the patient's function prior to training, along with maintenance of several aspects of the gains demonstrated at retention make a compelling argument for this approach to training.

Advanced Myoelectric Control for Robotic Hand-Assisted Training: Outcome from a Stroke Patient.

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Lu, Zhiyuan; Tong, Kai-Yu; Shin, Henry; Li, Sheng; Zhou, Ping

2017-01-01

A hand exoskeleton driven by myoelectric pattern recognition was designed for stroke rehabilitation. It detects and recognizes the user's motion intent based on electromyography (EMG) signals, and then helps the user to accomplish hand motions in real time. The hand exoskeleton can perform six kinds of motions, including the whole hand closing/opening, tripod pinch/opening, and the "gun" sign/opening. A 52-year-old woman, 8 months after stroke, made 20× 2-h visits over 10 weeks to participate in robot-assisted hand training. Though she was unable to move her fingers on her right hand before the training, EMG activities could be detected on her right forearm. In each visit, she took 4× 10-min robot-assisted training sessions, in which she repeated the aforementioned six motion patterns assisted by our intent-driven hand exoskeleton. After the training, her grip force increased from 1.5 to 2.7 kg, her pinch force increased from 1.5 to 2.5 kg, her score of Box and Block test increased from 3 to 7, her score of Fugl-Meyer (Part C) increased from 0 to 7, and her hand function increased from Stage 1 to Stage 2 in Chedoke-McMaster assessment. The results demonstrate the feasibility of robot-assisted training driven by myoelectric pattern recognition after stroke.

A Voice Operated Tour Planning System for Autonomous Mobile Robots

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Charles V. Smith Iii

2010-06-01

Full Text Available Control systems driven by voice recognition software have been implemented before but lacked the context driven approach to generate relevant responses and actions. A partially voice activated control system for mobile robotics is presented that allows an autonomous robot to interact with people and the environment in a meaningful way, while dynamically creating customized tours. Many existing control systems also require substantial training for voice application. The system proposed requires little to no training and is adaptable to chaotic environments. The traversable area is mapped once and from that map a fully customized route is generated to the user

Patient body image, self-esteem, and cosmetic results of minimally invasive robotic cardiac surgery.

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İyigün, Taner; Kaya, Mehmet; Gülbeyaz, Sevil Özgül; Fıstıkçı, Nurhan; Uyanık, Gözde; Yılmaz, Bilge; Onan, Burak; Erkanlı, Korhan

2017-03-01

Patient-reported outcome measures reveal the quality of surgical care from the patient's perspective. We aimed to compare body image, self-esteem, hospital anxiety and depression, and cosmetic outcomes by using validated tools between patients undergoing robot-assisted surgery and those undergoing conventional open surgery. This single-center, multidisciplinary, randomized, prospective study of 62 patients who underwent cardiac surgery was conducted at Hospital from May 2013 to January 2015. The patients were divided into two groups: the robotic group (n = 33) and the open group (n = 29). The study employed five different tools to assess body image, self-esteem, and overall patient-rated scar satisfaction. There were statistically significant differences between the groups in terms of self-esteem scores (p = 0.038), body image scores (p = 0.026), overall Observer Scar Assessment Scale (p = 0.013), and overall Patient Scar Assessment Scale (p = 0.036) scores in favor of the robotic group during the postoperative period. Robot-assisted surgery protected the patient's body image and self-esteem, while conventional open surgery decreased these levels but without causing pathologies. Preoperative depression and anxiety level was reduced by both robot-assisted surgery and conventional open surgery. The groups did not significantly differ on Patient Satisfaction Scores and depression/anxiety scores. The results of this study clearly demonstrated that a minimally invasive approach using robotic-assisted surgery has advantages in terms of body image, self-esteem, and cosmetic outcomes over the conventional approach in patients undergoing cardiac surgery. Copyright © 2017 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.

Adaptive Hybrid Visual Servo Regulation of Mobile Robots Based on Fast Homography Decomposition

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Chunfu Wu

2015-01-01

Full Text Available For the monocular camera-based mobile robot system, an adaptive hybrid visual servo regulation algorithm which is based on a fast homography decomposition method is proposed to drive the mobile robot to its desired position and orientation, even when object’s imaging depth and camera’s position extrinsic parameters are unknown. Firstly, the homography’s particular properties caused by mobile robot’s 2-DOF motion are taken into account to induce a fast homography decomposition method. Secondly, the homography matrix and the extracted orientation error, incorporated with the desired view’s single feature point, are utilized to form an error vector and its open-loop error function. Finally, Lyapunov-based techniques are exploited to construct an adaptive regulation control law, followed by the experimental verification. The experimental results show that the proposed fast homography decomposition method is not only simple and efficient, but also highly precise. Meanwhile, the designed control law can well enable mobile robot position and orientation regulation despite the lack of depth information and camera’s position extrinsic parameters.

Robotic Hand-Assisted Training for Spinal Cord Injury Driven by Myoelectric Pattern Recognition: A Case Report.

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Lu, Zhiyuan; Tong, Kai-Yu; Shin, Henry; Stampas, Argyrios; Zhou, Ping

2017-10-01

A 51-year-old man with an incomplete C6 spinal cord injury sustained 26 yrs ago attended twenty 2-hr visits over 10 wks for robot-assisted hand training driven by myoelectric pattern recognition. In each visit, his right hand was assisted to perform motions by an exoskeleton robot, while the robot was triggered by his own motion intentions. The hand robot was designed for this study, which can perform six kinds of motions, including hand closing/opening; thumb, index finger, and middle finger closing/opening; and middle, ring, and little fingers closing/opening. After the training, his grip force increased from 13.5 to 19.6 kg, his pinch force remained the same (5.0 kg), his score of Box and Block test increased from 32 to 39, and his score from the Graded Redefined Assessment of Strength, Sensibility, and Prehension test Part 4.B increased from 22 to 24. He accomplished the tasks in the Graded Redefined Assessment of Strength, Sensibility, and Prehension test Part 4.B 28.8% faster on average. The results demonstrate the feasibility and effectiveness of robot-assisted training driven by myoelectric pattern recognition after spinal cord injury.

Robot training for hand motor recovery in subacute stroke patients: A randomized controlled trial.

Science.gov (United States)

Orihuela-Espina, Felipe; Roldán, Giovana Femat; Sánchez-Villavicencio, Israel; Palafox, Lorena; Leder, Ronald; Sucar, Luis Enrique; Hernández-Franco, Jorge

2016-01-01

Evidence of superiority of robot training for the hand over classical therapies in stroke patients remains controversial. During the subacute stage, hand training is likely to be the most useful. To establish whether robot active assisted therapies provides any additional motor recovery for the hand when administered during the subacute stage (robot based therapies for hand recovery will show significant differences at subacute stages. A randomized clinical trial. A between subjects randomized controlled trial was carried out on subacute stroke patients (n = 17) comparing robot active assisted therapy (RT) with a classical occupational therapy (OT). Both groups received 40 sessions ensuring at least 300 repetitions per session. Treatment duration was (mean ± std) 2.18 ± 1.25 months for the control group and 2.44 ± 0.88 months for the study group. The primary outcome was motor dexterity changes assessed with the Fugl-Meyer (FMA) and the Motricity Index (MI). Both groups (OT: n = 8; RT: n = 9) exhibited significant improvements over time (Non-parametric Cliff's delta-within effect sizes: dwOT-FMA = 0.5, dwOT-MI = 0.5, dwRT-FMA = 1, dwRT-MI = 1). Regarding differences between the therapies; the Fugl-Meyer score indicated a significant advantage for the hand training with the robot (FMA hand: WRS: W = 8, p hand prehension for RT with respect to OT but failed to reach significance (MI prehension: W = 17.5, p = 0.080). No harm occurred. Robotic therapies may be useful during the subacute stages of stroke - both endpoints (FM hand and MI prehension) showed the expected trend with bigger effect size for the robotic intervention. Additional benefit of the robotic therapy over the control therapy was only significant when the difference was measured with FM, demanding further investigation with larger samples. Implications of this study are important for decision making during therapy administration and resource allocation. Copyright © 2016 Hanley

Towards Sociable Robots

DEFF Research Database (Denmark)

Ngo, Trung Dung

This thesis studies aspects of self-sufficient energy (energy autonomy) for truly autonomous robots and towards sociable robots. Over sixty years of history of robotics through three developmental ages containing single robot, multi-robot systems, and social (sociable) robots, the main objective...... of roboticists mostly focuses on how to make a robotic system function autonomously and further, socially. However, such approaches mostly emphasize behavioural autonomy, rather than energy autonomy which is the key factor for not only any living machine, but for life on the earth. Consequently, self......-sufficient energy is one of the challenges for not only single robot or multi-robot systems, but also social and sociable robots. This thesis is to deal with energy autonomy for multi-robot systems through energy sharing (trophallaxis) in which each robot is equipped with two capabilities: self-refueling energy...

Content and face validity of a comprehensive robotic skills training program for general surgery, urology, and gynecology.

Science.gov (United States)

Dulan, Genevieve; Rege, Robert V; Hogg, Deborah C; Gilberg-Fisher, Kristine K; Tesfay, Seifu T; Scott, Daniel J

2012-04-01

The authors previously developed a comprehensive, proficiency-based robotic training curriculum that aimed to address 23 unique skills identified via task deconstruction of robotic operations. The purpose of this study was to determine the content and face validity of this curriculum. Expert robotic surgeons (n = 12) rated each deconstructed skill regarding relevance to robotic operations, were oriented to the curricular components, performed 3 to 5 repetitions on the 9 exercises, and rated each exercise. In terms of content validity, experts rated all 23 deconstructed skills as highly relevant (4.5 on a 5-point scale). Ratings for the 9 inanimate exercises indicated moderate to thorough measurement of designated skills. For face validity, experts indicated that each exercise effectively measured relevant skills (100% agreement) and was highly effective for training and assessment (4.5 on a 5-point scale). These data indicate that the 23 deconstructed skills accurately represent the appropriate content for robotic skills training and strongly support content and face validity for this curriculum. Copyright © 2012. Published by Elsevier Inc.

Improving the Adaptability of Simulated Evolutionary Swarm Robots in Dynamically Changing Environments

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Yao, Yao; Marchal, Kathleen; Van de Peer, Yves

2014-01-01

One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store ‘good behaviour’ and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment. PMID:24599485

Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments.

Directory of Open Access Journals (Sweden)

Yao Yao

Full Text Available One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN. An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment.

An adaptive unscented Kalman filter-based adaptive tracking control for wheeled mobile robots with control constrains in the presence of wheel slipping

Directory of Open Access Journals (Sweden)

Mingyue Cui

2016-09-01

Full Text Available A novel control approach is proposed for trajectory tracking of a wheeled mobile robot with unknown wheels’ slipping. The longitudinal and lateral slipping are considered and processed as three time-varying parameters. The adaptive unscented Kalman filter is then designed to estimate the slipping parameters online, an adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the adaptive unscented Kalman filter context. Considering the practical physical constrains, a stable tracking control law for this robot system is proposed by the backstepping method. Asymptotic stability is guaranteed by Lyapunov stability theory. Control gains are determined online by applying pole placement method. Simulation and real experiment results show the effectiveness and robustness of the proposed control method.

An immune-inspired swarm aggregation algorithm for self-healing swarm robotic systems.

Science.gov (United States)

Timmis, J; Ismail, A R; Bjerknes, J D; Winfield, A F T

2016-08-01

Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the use of swarm robotic systems, recent studies have shown that swarm robotic systems are susceptible to certain types of failure. In this paper we propose an approach to self-healing swarm robotic systems and take inspiration from the process of granuloma formation, a process of containment and repair found in the immune system. We use a case study of a swarm performing team work where previous works have demonstrated that partially failed robots have the most detrimental effect on overall swarm behaviour. We have developed an immune inspired approach that permits the recovery from certain failure modes during operation of the swarm, overcoming issues that effect swarm behaviour associated with partially failed robots. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Nutritional strategies to influence adaptations to training.

Science.gov (United States)

Spriet, Lawrence L; Gibala, Martin J

2004-01-01

This article highlights new nutritional concerns or practices that may influence the adaptation to training. The discussion is based on the assumption that the adaptation to repeated bouts of training occurs during recovery periods and that if one can train harder, the adaptation will be greater. The goal is to maximize with nutrition the recovery/adaptation that occurs in all rest periods, such that recovery before the next training session is complete. Four issues have been identified where recent scientific information will force sports nutritionists to embrace new issues and reassess old issues and, ultimately, alter the nutritional recommendations they give to athletes. These are: (1) caffeine ingestion; (2) creatine ingestion; (3) the use of intramuscular triacylglycerol (IMTG) as a fuel during exercise and the nutritional effects on IMTG repletion following exercise; and (4) the role nutrition may play in regulating the expression of genes during and after exercise training sessions. Recent findings suggest that low doses of caffeine exert significant ergogenic effects by directly affecting the central nervous system during exercise. Caffeine can cross the blood-brain barrier and antagonize the effects of adenosine, resulting in higher concentrations of stimulatory neurotransmitters. These new data strengthen the case for using low doses of caffeine during training. On the other hand, the data on the role that supplemental creatine ingestion plays in augmenting the increase in skeletal muscle mass and strength during resistance training remain equivocal. Some studies are able to demonstrate increases in muscle fibre size with creatine ingestion and some are not. The final two nutritional topics are new and have not progressed to the point that we can specifically identify strategies to enhance the adaptation to training. However, it is likely that nutritional strategies will be needed to replenish the IMTG that is used during endurance exercise. It is not

A Self-Synthesis Approach to Perceptual Learning for Multisensory Fusion in Robotics

Science.gov (United States)

Axenie, Cristian; Richter, Christoph; Conradt, Jörg

2016-01-01

Biological and technical systems operate in a rich multimodal environment. Due to the diversity of incoming sensory streams a system perceives and the variety of motor capabilities a system exhibits there is no single representation and no singular unambiguous interpretation of such a complex scene. In this work we propose a novel sensory processing architecture, inspired by the distributed macro-architecture of the mammalian cortex. The underlying computation is performed by a network of computational maps, each representing a different sensory quantity. All the different sensory streams enter the system through multiple parallel channels. The system autonomously associates and combines them into a coherent representation, given incoming observations. These processes are adaptive and involve learning. The proposed framework introduces mechanisms for self-creation and learning of the functional relations between the computational maps, encoding sensorimotor streams, directly from the data. Its intrinsic scalability, parallelisation, and automatic adaptation to unforeseen sensory perturbations make our approach a promising candidate for robust multisensory fusion in robotic systems. We demonstrate this by applying our model to a 3D motion estimation on a quadrotor. PMID:27775621

A Self-Synthesis Approach to Perceptual Learning for Multisensory Fusion in Robotics

Directory of Open Access Journals (Sweden)

Cristian Axenie

2016-10-01

Full Text Available Biological and technical systems operate in a rich multimodal environment. Due to the diversity of incoming sensory streams a system perceives and the variety of motor capabilities a system exhibits there is no single representation and no singular unambiguous interpretation of such a complex scene. In this work we propose a novel sensory processing architecture, inspired by the distributed macro-architecture of the mammalian cortex. The underlying computation is performed by a network of computational maps, each representing a different sensory quantity. All the different sensory streams enter the system through multiple parallel channels. The system autonomously associates and combines them into a coherent representation, given incoming observations. These processes are adaptive and involve learning. The proposed framework introduces mechanisms for self-creation and learning of the functional relations between the computational maps, encoding sensorimotor streams, directly from the data. Its intrinsic scalability, parallelisation, and automatic adaptation to unforeseen sensory perturbations make our approach a promising candidate for robust multisensory fusion in robotic systems. We demonstrate this by applying our model to a 3D motion estimation on a quadrotor.

A Novel Low-Cost Adaptive Scanner Concept for Mobile Robots

Directory of Open Access Journals (Sweden)

Ivo Stančić

2014-09-01

Full Text Available A fundamental problem in mobile robot applications is the need for accurate knowledge of the position of a vehicle for localizing itself and for avoiding obstacles in its path. In the search for a solution to this problem, researchers and engineers have developed different sensors, systems and techniques. Modern mobile robots relay information obtained from a variety of sensors and sophisticated data fusion algorithms. In this paper, a novel concept for a low-cost adaptive scanner based on a projected light pattern is proposed. The main advantage of the proposed system is its adaptivity, which enables the rapid scanning of the robot’s surroundings in search of obstacles and a more detailed scan of a single object to retrieve its surface configuration and perform some limited analyses. This paper addresses the concept behind such a scanner, where a proof-of-concept is achieved using an office DLP projector. During the measurements, the accuracy of the proposed system was tested on obstacles and objects with known configurations. The obtained results are presented and analyzed, and conclusions about the system’s performance and possible improvements are discussed.

Adaptive Robotic Fabrication for Conditions of Material Inconsistency

DEFF Research Database (Denmark)

Nicholas, Paul; Zwierzycki, Mateusz; Clausen Nørgaard, Esben

2017-01-01

This paper describes research that addresses the variable behaviour of industrial quality metals and the extension of computational techniques into the fabrication process. It describes the context of robotic incremental sheet metal forming, a freeform method for imparting 3D form onto a 2D thin...... and the fabrication process? Here, two adaptive methods are presented that aim to increase forming accuracy with only a minimum increase in fabrication time, and that maintain ongoing input from the results of the fabrication process. The first method is an online sensor-based strategy and the second method...

Spontaneous Self-Distancing and Adaptive Self-Reflection Across Adolescence.

Science.gov (United States)

White, Rachel E; Kross, Ethan; Duckworth, Angela L

2015-07-01

Experiments performed primarily with adults show that self-distancing facilitates adaptive self-reflection. However, no research has investigated whether adolescents spontaneously engage in this process or whether doing so is linked to adaptive outcomes. In this study, 226 African American adolescents, aged 11-20, reflected on an anger-related interpersonal experience. As expected, spontaneous self-distancing during reflection predicted lower levels of emotional reactivity by leading adolescents to reconstrue (rather than recount) their experience and blame their partner less. Moreover, the inverse relation between self-distancing and emotional reactivity strengthened with age. These findings highlight the role that self-distancing plays in fostering adaptive self-reflection in adolescence, and begin to elucidate the role that development plays in enhancing the benefits of engaging in this process. © 2015 The Authors. Child Development © 2015 Society for Research in Child Development, Inc.

SIMULATION OF ADAPTIVE BEHAVIOR IN THE CONTEXT OF SOLVING AN AUTONOMOUS ROBOTIC VEHICLE MOTION TASK ON TWO-DIMENSIONAL PLANE WITH OBSTACLES

Directory of Open Access Journals (Sweden)

R. A. Prakapovich

2014-01-01

Full Text Available An adaptive neurocontroller for autonomous robotic vehicle control, which is designed to generate control signals (according to preprogrammed motion algorithm and to develop individual reactions to some external impacts during functioning process, that allows the robot to adapt to external environment changes, is suggested. To debug and test the proposed neurocontroller a specially designed program, able to simulate the sensory and executive systems operation of the robotic vehicle, is used.

Hydrodynamic investigation of a self-propelled robotic fish based on a force-feedback control method

International Nuclear Information System (INIS)

Wen, L; Wang, T M; Liang, J H; Wu, G H

2012-01-01

We implement a mackerel (Scomber scombrus) body-shaped robot, programmed to display the three most typical body/caudal fin undulatory kinematics (i.e. anguilliform, carangiform and thunniform), in order to biomimetically investigate hydrodynamic issues not easily tackled experimentally with live fish. The robotic mackerel, mounted on a servo towing system and initially at rest, can determine its self-propelled speed by measuring the external force acting upon it and allowing for the simultaneous measurement of power, flow field and self-propelled speed. Experimental results showed that the robotic swimmer with thunniform kinematics achieved a faster final swimming speed (St = 0.424) relative to those with carangiform (St = 0.43) and anguilliform kinematics (St = 0.55). The thrust efficiency, estimated from a digital particle image velocimetry (DPIV) flow field, showed that the robotic swimmer with thunniform kinematics is more efficient (47.3%) than those with carangiform (31.4%) and anguilliform kinematics (26.6%). Furthermore, the DPIV measurements illustrate that the large-scale characteristics of the flow pattern generated by the robotic swimmer with both anguilliform and carangiform kinematics were wedge-like, double-row wake structures. Additionally, a typical single-row reverse Karman vortex was produced by the robotic swimmer using thunniform kinematics. Finally, we discuss this novel force-feedback-controlled experimental method, and review the relative self-propelled hydrodynamic results of the robot when utilizing the three types of undulatory kinematics. (paper)

[Self-acceptance as adaptively resigning the self to low self-evaluation].

Science.gov (United States)

Ueda, T

1996-10-01

In past studies, the concept of self-acceptance has often been confused with self-evaluation or self-esteem. The purpose of this study was to distinguish these concepts, and operationally define self-acceptance as Carl Rogers proposed: feeling all right toward the self when self-evaluation was low. Self-acceptance as adaptive resignation, a moderating variable, therefore should raise self-esteem of only those people with low self-evaluation. Self-acceptance was measured in the study as affirmative evaluation of own self-evaluation. Two hundred and forty college students, 120 each for men and women, completed a questionnaire of self-evaluative consciousness and self-esteem scales. Results of statistical analyses showed that among subjects with low self-evaluation, the higher self-acceptance, the higher the person's self-esteem. The same relation was not observed among those with high self-evaluation. Thus, it may be concluded that self-acceptance was adaptive resignation, and therefore meaningful to only those with low self-evaluation.

Fine finger motor skill training with exoskeleton robotic hand in chronic stroke: stroke rehabilitation.

Science.gov (United States)

Ockenfeld, Corinna; Tong, Raymond K Y; Susanto, Evan A; Ho, Sze-Kit; Hu, Xiao-ling

2013-06-01

Background and Purpose. Stroke survivors often show a limited recovery in the hand function to perform delicate motions, such as full hand grasping, finger pinching and individual finger movement. The purpose of this study is to describe the implementation of an exoskeleton robotic hand together with fine finger motor skill training on 2 chronic stroke patients. Case Descriptions. Two post-stroke patients participated in a 20-session training program by integrating 10 minutes physical therapy, 20 minutes robotic hand training and 15 minutes functional training tasks with delicate objects(card, pen and coin). These two patients (A and B) had cerebrovascular accident at 6 months and 11 months respectively when enrolled in this study. Outcomes. The results showed that both patients had improvements in Fugl-Meyer assessment (FM), Action Research Arm Test (ARAT). Patients had better isolation of the individual finger flexion and extension based on the reduced muscle co-contraction from the electromyographic(EMG) signals and finger extension force after 20 sessions of training. Discussion. This preliminary study showed that by focusing on the fine finger motor skills together with the exoskeleton robotic hand, it could improve the motor recovery of the upper extremity in the fingers and hand function, which were showed in the ARAT. Future randomized controlled trials are needed to evaluate the clinical effectiveness.

Comparison of haptic guidance and error amplification robotic trainings for the learning of a timing-based motor task by healthy seniors.

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Bouchard, Amy E; Corriveau, Hélène; Milot, Marie-Hélène

2015-01-01

With age, a decline in the temporal aspect of movement is observed such as a longer movement execution time and a decreased timing accuracy. Robotic training can represent an interesting approach to help improve movement timing among the elderly. Two types of robotic training-haptic guidance (HG; demonstrating the correct movement for a better movement planning and improved execution of movement) and error amplification (EA; exaggerating movement errors to have a more rapid and complete learning) have been positively used in young healthy subjects to boost timing accuracy. For healthy seniors, only HG training has been used so far where significant and positive timing gains have been obtained. The goal of the study was to evaluate and compare the impact of both HG and EA robotic trainings on the improvement of seniors' movement timing. Thirty-two healthy seniors (mean age 68 ± 4 years) learned to play a pinball-like game by triggering a one-degree-of-freedom hand robot at the proper time to make a flipper move and direct a falling ball toward a randomly positioned target. During HG and EA robotic trainings, the subjects' timing errors were decreased and increased, respectively, based on the subjects' timing errors in initiating a movement. Results showed that only HG training benefited learning, but the improvement did not generalize to untrained targets. Also, age had no influence on the efficacy of HG robotic training, meaning that the oldest subjects did not benefit more from HG training than the younger senior subjects. Using HG to teach the correct timing of movement seems to be a good strategy to improve motor learning for the elderly as for younger people. However, more studies are needed to assess the long-term impact of HG robotic training on improvement in movement timing.

Stroke Rehabilitation in Frail Elderly with the Robotic Training Device ACRE: A Randomized Controlled Trial and Cost-Effectiveness Study

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M. Schoone

2011-01-01

Full Text Available The ACRE (ACtive REhabilitation robotic device is developed to enhance therapeutic treatment of upper limbs after stroke. The aim of this study is to assess effects and costs of ACRE training for frail elderly patients and to establish if ACRE can be a valuable addition to standard therapy in nursing home rehabilitation. The study was designed as randomized controlled trial, one group receiving therapy as usual and the other receiving additional ACRE training. Changes in motor abilities, stroke impact, quality of life and emotional well-being were assessed. In total, 24 patients were included. In this small number no significant effects of the ACRE training were found. A large number of 136 patients were excluded. Main reasons for exclusion were lack of physiological or cognitive abilities. Further improvement of the ACRE can best be focused on making the system suitable for self-training and development of training software for activities of daily living.

Design and Experimental Development of a Pneumatic Stiffness Adjustable Foot System for Biped Robots Adaptable to Bumps on the Ground

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Xizhe Zang

2017-09-01

Full Text Available Walking on rough terrains still remains a challenge that needs to be addressed for biped robots because the unevenness on the ground can easily disrupt the walking stability. This paper proposes a novel foot system with passively adjustable stiffness for biped robots which is adaptable to small-sized bumps on the ground. The robotic foot is developed by attaching eight pneumatic variable stiffness units to the sole separately and symmetrically. Each variable stiffness unit mainly consists of a pneumatic bladder and a mechanical reversing valve. When walking on rough ground, the pneumatic bladders in contact with bumps are compressed, and the corresponding reversing valves are triggered to expel out the air, enabling the pneumatic bladders to adapt to the bumps with low stiffness; while the other pneumatic bladders remain rigid and maintain stable contact with the ground, providing support to the biped robot. The performances of the proposed foot system, including the variable stiffness mechanism, the adaptability on the bumps of different heights, and the application on a biped robot prototype are demonstrated by various experiments.

Robot-Assisted Body-Weight-Supported Treadmill Training in Gait Impairment in Multiple Sclerosis Patients: A Pilot Study.

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Łyp, Marek; Stanisławska, Iwona; Witek, Bożena; Olszewska-Żaczek, Ewelina; Czarny-Działak, Małgorzata; Kaczor, Ryszard

2018-02-13

This study deals with the use of a robot-assisted body-weight-supported treadmill training in multiple sclerosis (MS) patients with gait dysfunction. Twenty MS patients (10 men and 10 women) of the mean of 46.3 ± 8.5 years were assigned to a six-week-long training period with the use of robot-assisted treadmill training of increasing intensity of the Lokomat type. The outcome measure consisted of the difference in motion-dependent torque of lower extremity joint muscles after training compared with baseline before training. We found that the training uniformly and significantly augmented the torque of both extensors and flexors of the hip and knee joints. The muscle power in the lower limbs of SM patients was improved, leading to corrective changes of disordered walking movements, which enabled the patients to walk with less effort and less assistance of care givers. The torque augmentation could have its role in affecting the function of the lower extremity muscle groups during walking. The results of this pilot study suggest that the robot-assisted body-weight-supported treadmill training may be a potential adjunct measure in the rehabilitation paradigm of 'gait reeducation' in peripheral neuropathies.

The value of haptic feedback in conventional and robot-assisted minimal invasive surgery and virtual reality training: a current review.

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van der Meijden, O A J; Schijven, M P

2009-06-01

Virtual reality (VR) as surgical training tool has become a state-of-the-art technique in training and teaching skills for minimally invasive surgery (MIS). Although intuitively appealing, the true benefits of haptic (VR training) platforms are unknown. Many questions about haptic feedback in the different areas of surgical skills (training) need to be answered before adding costly haptic feedback in VR simulation for MIS training. This study was designed to review the current status and value of haptic feedback in conventional and robot-assisted MIS and training by using virtual reality simulation. A systematic review of the literature was undertaken using PubMed and MEDLINE. The following search terms were used: Haptic feedback OR Haptics OR Force feedback AND/OR Minimal Invasive Surgery AND/OR Minimal Access Surgery AND/OR Robotics AND/OR Robotic Surgery AND/OR Endoscopic Surgery AND/OR Virtual Reality AND/OR Simulation OR Surgical Training/Education. The results were assessed according to level of evidence as reflected by the Oxford Centre of Evidence-based Medicine Levels of Evidence. In the current literature, no firm consensus exists on the importance of haptic feedback in performing minimally invasive surgery. Although the majority of the results show positive assessment of the benefits of force feedback, results are ambivalent and not unanimous on the subject. Benefits are least disputed when related to surgery using robotics, because there is no haptic feedback in currently used robotics. The addition of haptics is believed to reduce surgical errors resulting from a lack of it, especially in knot tying. Little research has been performed in the area of robot-assisted endoscopic surgical training, but results seem promising. Concerning VR training, results indicate that haptic feedback is important during the early phase of psychomotor skill acquisition.

Adaptive training of feedforward neural networks by Kalman filtering

International Nuclear Information System (INIS)

Ciftcioglu, Oe.

1995-02-01

Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.)

Graduate employability capacities, self-esteem and career adaptability among South African young adults

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Sadika Ismail

2017-08-01

Full Text Available Orientation: Employers expect young graduates to have a well-rounded sense of self, to display a range of graduate employability capacities and to adapt to constant changes they are faced with in order to obtain and maintain employment. Research purpose: The goals of this study are (1 to investigate whether a significant relationship exists between graduate employability capacities, self-esteem and career adaptability, (2 to ascertain if a set of graduate employability capacities, when combined with self-esteem, has a significant relationship with a set of career adaptability capacities and (3 to identify the major variables that contribute to this relationship. Motivation for the study: The potential for career adaptability, graduate employability capacities and self-esteem of young adults promotes employability among graduates, thereby addressing and possibly reducing youth unemployment in South Africa. Research approach, design and method: A quantitative, cross-sectional research design approach was utilised in which descriptive statistics, Pearson product-moment correlations and canonical correlation analysis were employed to accomplish the objectives of this study. Respondents (N = 332 were enrolled at further education and training (FET colleges and were predominantly black (98.5% and female (62% students between the ages of 18 and 29. Main findings: The results displayed positive multivariate relationships between the variables and furthermore showed that graduate employability capacities contributed the most in terms of clarifying the respondents’ career adaptability as compared to their self-esteem. Practical and managerial implications: This study proposes that young adults’ career adaptability can be enhanced through the development of their self-esteem and particularly their graduate employability capacities, thus making them more employable. Contributions: Theoretically, this study proves useful because of the significant

Effects of electromyography-driven robot-aided hand training with neuromuscular electrical stimulation on hand control performance after chronic stroke.

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Rong, Wei; Tong, Kai Yu; Hu, Xiao Ling; Ho, Sze Kit

2015-03-01

An electromyography-driven robot system integrated with neuromuscular electrical stimulation (NMES) was developed to investigate its effectiveness on post-stroke rehabilitation. The performance of this system in assisting finger flexion/extension with different assistance combinations was evaluated in five stroke subjects. Then, a pilot study with 20-sessions training was conducted to evaluate the training's effectiveness. The results showed that combined assistance from the NMES-robot could improve finger movement accuracy, encourage muscle activation of the finger muscles and suppress excessive muscular activities in the elbow joint. When assistances from both NMES and the robot were 50% of their maximum assistances, finger-tracking performance had the best results, with the lowest root mean square error, greater range of motion, higher voluntary muscle activations of the finger joints and lower muscle co-contraction in the finger and elbow joints. Upper limb function improved after the 20-session training, indicated by the increased clinical scores of Fugl-Meyer Assessment, Action Research Arm Test and Wolf Motor Function Test. Muscle co-contraction was reduced in the finger and elbow joints reflected by the Modified Ashworth Scale. The findings demonstrated that an electromyography-driven NMES-robot used for chronic stroke improved hand function and tracking performance. Further research is warranted to validate the method on a larger scale. Implications for Rehabilitation The hand robotics and neuromuscular electrical stimulation (NMES) techniques are still separate systems in current post-stroke hand rehabilitation. This is the first study to investigate the combined effects of the NMES and robot on hand rehabilitation. The finger tracking performance was improved with the combined assistance from the EMG-driven NMES-robot hand system. The assistance from the robot could improve the finger movement accuracy and the assistance from the NMES could reduce the

A Self-Adaptive Hidden Markov Model for Emotion Classification in Chinese Microblogs

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Li Liu

2015-01-01

we propose a modified version of hidden Markov model (HMM classifier, called self-adaptive HMM, whose parameters are optimized by Particle Swarm Optimization algorithms. Since manually labeling large-scale dataset is difficult, we also employ the entropy to decide whether a new unlabeled tweet shall be contained in the training dataset after being assigned an emotion using our HMM-based approach. In the experiment, we collected about 200,000 Chinese tweets from Sina Weibo. The results show that the F-score of our approach gets 76% on happiness and fear and 65% on anger, surprise, and sadness. In addition, the self-adaptive HMM classifier outperforms Naive Bayes and Support Vector Machine on recognition of happiness, anger, and sadness.

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

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Eduard eGrinke

2015-10-01

Full Text Available Walking animals, like insects, with little neural computing can effectively perform complex behaviors. They can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a walking robot is a challenging task. In this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors in the network to generate different turning angles with short-term memory for a biomechanical walking robot. The turning information is transmitted as descending steering signals to the locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations as well as escaping from sharp corners or deadlocks. Using backbone joint control embedded in the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments.

Flowrate behavior and clustering of self-driven robots in a channel

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Tian, Bo; Sun, Wang-Ping; Li, Ming; Jiang, Rui; Hu, Mao-Bin

2018-03-01

In this paper, the collective motion of self-driven robots is studied experimentally and theoretically. In the channel, the flowrate of robots increases with the density linearly, even if the density of the robots tends to 1.0. There is no abrupt drop in the flowrate, similar to the collective motion of ants. We find that the robots will adjust their velocities by a serial of tiny collisions. The speed-adjustment will affect both robots involved in the collision, and will help to maintain a nearly uniform velocity for the robots. As a result, the flowrate drop will disappear. In the motion, the robots neither gather together nor scatter completely. Instead, they form some clusters to move together. These clusters are not stable during the moving process, but their sizes follow a power-law-alike distribution. We propose a theoretical model to simulate this collective motion process, which can reproduce these behaviors well. Analytic results about the flowrate behavior are also consistent with experiments. Project supported by the Key Research and Development Program, China (Grant No. 2016YFC0802508) and the National Natural Science Foundation of China (Grant Nos. 11672289 and 11422221).

A Vision-Based Self-Calibration Method for Robotic Visual Inspection Systems

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Yin, Shibin; Ren, Yongjie; Zhu, Jigui; Yang, Shourui; Ye, Shenghua

2013-01-01

A vision-based robot self-calibration method is proposed in this paper to evaluate the kinematic parameter errors of a robot using a visual sensor mounted on its end-effector. This approach could be performed in the industrial field without external, expensive apparatus or an elaborate setup. A robot Tool Center Point (TCP) is defined in the structural model of a line-structured laser sensor, and aligned to a reference point fixed in the robot workspace. A mathematical model is established to formulate the misalignment errors with kinematic parameter errors and TCP position errors. Based on the fixed point constraints, the kinematic parameter errors and TCP position errors are identified with an iterative algorithm. Compared to the conventional methods, this proposed method eliminates the need for a robot-based-frame and hand-to-eye calibrations, shortens the error propagation chain, and makes the calibration process more accurate and convenient. A validation experiment is performed on an ABB IRB2400 robot. An optimal configuration on the number and distribution of fixed points in the robot workspace is obtained based on the experimental results. Comparative experiments reveal that there is a significant improvement of the measuring accuracy of the robotic visual inspection system. PMID:24300597

Reviewing Clinical Effectiveness of Active Training Strategies of Platform-Based Ankle Rehabilitation Robots

Directory of Open Access Journals (Sweden)

Xiangfeng Zeng

2018-01-01

Full Text Available Objective. This review aims to provide a systematical investigation of clinical effectiveness of active training strategies applied in platform-based ankle robots. Method. English-language studies published from Jan 1980 to Aug 2017 were searched from four databases using key words of “Ankle∗” AND “Robot∗” AND “Effect∗ OR Improv∗ OR Increas∗.” Following an initial screening, three rounds of discrimination were successively conducted based on the title, the abstract, and the full paper. Result. A total of 21 studies were selected with 311 patients involved; of them, 13 studies applied a single group while another eight studies used different groups for comparison to verify the therapeutic effect. Virtual-reality (VR game training was applied in 19 studies, while two studies used proprioceptive neuromuscular facilitation (PNF training. Conclusion. Active training techniques delivered by platform ankle rehabilitation robots have been demonstrated with great potential for clinical applications. Training strategies are mostly combined with one another by considering rehabilitation schemes and motion ability of ankle joints. VR game environment has been commonly used with active ankle training. Bioelectrical signals integrated with VR game training can implement intelligent identification of movement intention and assessment. These further provide the foundation for advanced interactive training strategies that can lead to enhanced training safety and confidence for patients and better treatment efficacy.

Robot Visual Tracking via Incremental Self-Updating of Appearance Model

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Danpei Zhao

2013-09-01

Full Text Available This paper proposes a target tracking method called Incremental Self-Updating Visual Tracking for robot platforms. Our tracker treats the tracking problem as a binary classification: the target and the background. The greyscale, HOG and LBP features are used in this work to represent the target and are integrated into a particle filter framework. To track the target over long time sequences, the tracker has to update its model to follow the most recent target. In order to deal with the problems of calculation waste and lack of model-updating strategy with the traditional methods, an intelligent and effective online self-updating strategy is devised to choose the optimal update opportunity. The strategy of updating the appearance model can be achieved based on the change in the discriminative capability between the current frame and the previous updated frame. By adjusting the update step adaptively, severe waste of calculation time for needless updates can be avoided while keeping the stability of the model. Moreover, the appearance model can be kept away from serious drift problems when the target undergoes temporary occlusion. The experimental results show that the proposed tracker can achieve robust and efficient performance in several benchmark-challenging video sequences with various complex environment changes in posture, scale, illumination and occlusion.

Construction of a Urologic Robotic Surgery Training Curriculum: How Many Simulator Sessions Are Required for Residents to Achieve Proficiency?

Science.gov (United States)

Wiener, Scott; Haddock, Peter; Shichman, Steven; Dorin, Ryan

2015-11-01

To define the time needed by urology residents to attain proficiency in computer-aided robotic surgery to aid in the refinement of a robotic surgery simulation curriculum. We undertook a retrospective review of robotic skills training data acquired during January 2012 to December 2014 from junior (postgraduate year [PGY] 2-3) and senior (PGY4-5) urology residents using the da Vinci Skills Simulator. We determined the number of training sessions attended and the level of proficiency achieved by junior and senior residents in attempting 11 basic or 6 advanced tasks, respectively. Junior residents successfully completed 9.9 ± 1.8 tasks, with 62.5% completing all 11 basic tasks. The maximal cumulative success rate of junior residents completing basic tasks was 89.8%, which was achieved within 7.0 ± 1.5 hours of training. Of senior residents, 75% successfully completed all six advanced tasks. Senior residents attended 6.3 ± 3.5 hours of training during which 5.1 ± 1.6 tasks were completed. The maximal cumulative success rate of senior residents completing advanced tasks was 85.4%. When designing and implementing an effective robotic surgical training curriculum, an allocation of 10 hours of training may be optimal to allow junior and senior residents to achieve an acceptable level of surgical proficiency in basic and advanced robotic surgical skills, respectively. These data help guide the design and scheduling of a residents training curriculum within the time constraints of a resident's workload.

Robot vision

International Nuclear Information System (INIS)

Hall, E.L.

1984-01-01

Almost all industrial robots use internal sensors such as shaft encoders which measure rotary position, or tachometers which measure velocity, to control their motions. Most controllers also provide interface capabilities so that signals from conveyors, machine tools, and the robot itself may be used to accomplish a task. However, advanced external sensors, such as visual sensors, can provide a much greater degree of adaptability for robot control as well as add automatic inspection capabilities to the industrial robot. Visual and other sensors are now being used in fundamental operations such as material processing with immediate inspection, material handling with adaption, arc welding, and complex assembly tasks. A new industry of robot vision has emerged. The application of these systems is an area of great potential

Effects of self-paced interval and continuous training on health markers in women.

Science.gov (United States)

Connolly, Luke J; Bailey, Stephen J; Krustrup, Peter; Fulford, Jonathan; Smietanka, Chris; Jones, Andrew M

2017-11-01

To compare the effects of self-paced high-intensity interval and continuous cycle training on health markers in premenopausal women. Forty-five inactive females were randomised to a high-intensity interval training (HIIT; n = 15), continuous training (CT; n = 15) or an inactive control (CON; n = 15) group. HIIT performed 5 × 5 min sets comprising repetitions of 30-s low-, 20-s moderate- and 10-s high-intensity cycling with 2 min rest between sets. CT completed 50 min of continuous cycling. Training was completed self-paced, 3 times weekly for 12 weeks. Peak oxygen uptake (16 ± 8 and 21 ± 12%), resting heart rate (HR) (-5 ± 9 and -4 ± 7 bpm) and visual and verbal learning improved following HIIT and CT compared to CON (P HIIT (P HIIT and CT, and there were no changes in fasting serum lipids, fasting blood [glucose] or [glucose] during an oral glucose tolerance test following either HIIT or CT (P > 0.05). No outcome variable changed in the CON group (P > 0.05). Twelve weeks of self-paced HIIT and CT were similarly effective at improving cardiorespiratory fitness, resting HR and cognitive function in inactive premenopausal women, whereas blood pressure, submaximal HR, well-being and body mass adaptations were training-type-specific. Both training methods improved established health markers, but the adaptations to HIIT were evoked for a lower time commitment.

“I just have diabetes”: children’s need for diabetes self-management support and how a social robot can accommodate their needs

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Blanson Henkemans OA

2012-07-01

Full Text Available Olivier A Blanson Henkemans, Vera Hoondert, Femke Schrama-Groot, Rosemarijn Looije, Laurence L Alpay, Mark A NeerincxTNO, Lifestyle, Leiden, The NetherlandsPurpose: Children with type 1 diabetes need to self-manage their illness to minimize its impact on their long-term health. However, because children are still developing cognitively and emotionally, self-management is challenging. The European FP7 project, ALIZ-E, looks at how social robots can support children aged 8–12 years with their diabetes self-management. To acquire user requirements for such a robot, we studied how diabetes self-management is organized for children and how they experience their illness and its management regarding their quality of life.Methods: We conducted semistructured interviews with diabetes caregivers (n = 6 and children 8–12 with type 1 diabetes (n = 9, and surveyed their parents (n = 9.Results: Results of the interviews with caregivers show that parents play a prominent role in diabetes self-management and, accordingly, children do not experience significant problems. However, because children develop a need for autonomy during puberty, it is important that they become more proficient in their self-management at an earlier age. Results of the interviews with children show that they accept diabetes as a part of their life and want to be seen as regular children. Also, children experience difficulties in unusual situations (eg, doing sports and vacationing and at school. The illness comes at the cost of the child’s mental well-being (eg, insecurity, fear, and worry and physical well-being (eg, listlessness and tiredness. Regarding social well-being, children enjoy attending diabetes camps and having friends with diabetes, due to a common understanding of their condition. Finally, parents are not always fully aware of how children experience their illness.Conclusion: Children could benefit from social robots offering motivation, training, and (parental

Does robot-assisted gait training ameliorate gait abnormalities in multiple sclerosis? A pilot randomized-control trial.

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Straudi, S; Benedetti, M G; Venturini, E; Manca, M; Foti, C; Basaglia, N

2013-01-01

Gait disorders are common in multiple sclerosis (MS) and lead to a progressive reduction of function and quality of life. Test the effects of robot-assisted gait rehabilitation in MS subjects through a pilot randomized-controlled study. We enrolled MS subjects with Expanded Disability Status Scale scores within 4.5-6.5. The experimental group received 12 robot-assisted gait training sessions over 6 weeks. The control group received the same amount of conventional physiotherapy. Outcomes measures were both biomechanical assessment of gait, including kinematics and spatio-temporal parameters, and clinical test of walking endurance (six-minute walk test) and mobility (Up and Go Test). 16 subjects (n = 8 experimental group, n = 8 control group) were included in the final analysis. At baseline the two groups were similar in all variables, except for step length. Data showed walking endurance, as well as spatio-temporal gait parameters improvements after robot-assisted gait training. Pelvic antiversion and reduced hip extension during terminal stance ameliorated after aforementioned intervention. Robot-assisted gait training seems to be effective in increasing walking competency in MS subjects. Moreover, it could be helpful in restoring the kinematic of the hip and pelvis.

Robot-assisted gait training in patients with Parkinson disease: a randomized controlled trial.

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Picelli, Alessandro; Melotti, Camilla; Origano, Francesca; Waldner, Andreas; Fiaschi, Antonio; Santilli, Valter; Smania, Nicola

2012-05-01

. Gait impairment is a common cause of disability in Parkinson disease (PD). Electromechanical devices to assist stepping have been suggested as a potential intervention. . To evaluate whether a rehabilitation program of robot-assisted gait training (RAGT) is more effective than conventional physiotherapy to improve walking. . A total of 41 patients with PD were randomly assigned to 45-minute treatment sessions (12 in all), 3 days a week, for 4 consecutive weeks of either robotic stepper training (RST; n = 21) using the Gait Trainer or physiotherapy (PT; n = 20) with active joint mobilization and a modest amount of conventional gait training. Participants were evaluated before, immediately after, and 1 month after treatment. Primary outcomes were 10-m walking speed and distance walked in 6 minutes. . Baseline measures revealed no statistical differences between groups, but the PT group walked 0.12 m/s slower; 5 patients withdrew. A statistically significant improvement was found in favor of the RST group (walking speed 1.22 ± 0.19 m/s [P = .035]; distance 366.06 ± 78.54 m [P < .001]) compared with the PT group (0.98 ± 0.32 m/s; 280.11 ± 106.61 m). The RAGT mean speed increased by 0.13 m/s, which is probably not clinically important. Improvements were maintained 1 month later. . RAGT may improve aspects of walking ability in patients with PD. Future trials should compare robotic assistive training with treadmill or equal amounts of overground walking practice.

A new training model for robot-assisted urethrovesical anastomosis and posterior muscle-fascial reconstruction: the Verona training technique.

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Cacciamani, G; De Marco, V; Siracusano, S; De Marchi, D; Bizzotto, L; Cerruto, M A; Motton, G; Porcaro, A B; Artibani, W

2017-06-01

A training model is usually needed to teach robotic surgical technique successfully. In this way, an ideal training model should mimic as much as possible the "in vivo" procedure and allow several consecutive surgical simulations. The goal of this study was to create a "wet lab" model suitable for RARP training programs, providing the simulation of the posterior fascial reconstruction. The second aim was to compare the original "Venezuelan" chicken model described by Sotelo to our training model. Our training model consists of performing an anastomosis, reproducing the surgical procedure in "vivo" as in RARP, between proventriculus and the proximal portion of the esophagus. A posterior fascial reconstruction simulating Rocco's stitch is performed between the tissues located under the posterior surface of the esophagus and the tissue represented by the serosa of the proventriculus. From 2014 to 2015, during 6 different full-immersion training courses, thirty-four surgeons performed the urethrovesical anastomosis using our model and the Sotelo's one. After the training period, each surgeon was asked to fill out a non-validated questionnaire to perform an evaluation of the differences between the two training models. Our model was judged the best model, in terms of similarity with urethral tissue and similarity with the anatomic unit urethra-pelvic wall. Our training model as reported by all trainees is easily reproducible and anatomically comparable with the urethrovesical anastomosis as performed during radical prostatectomy in humans. It is suitable for performing posterior fascial reconstruction reported by Rocco. In this context, our surgical training model could be routinely proposed in all robotic training courses to develop specific expertise in urethrovesical anastomosis with the reproducibility of the Rocco stitch.

Modular ankle robotics training in early subacute stroke: a randomized controlled pilot study.

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Forrester, Larry W; Roy, Anindo; Krywonis, Amanda; Kehs, Glenn; Krebs, Hermano Igo; Macko, Richard F

2014-09-01

BACKGROUND. Modular lower extremity robotics may offer a valuable avenue for restoring neuromotor control after hemiparetic stroke. Prior studies show that visually guided and visually evoked practice with an ankle robot (anklebot) improves paretic ankle motor control that translates into improved overground walking. To assess the feasibility and efficacy of daily anklebot training during early subacute hospitalization poststroke. Thirty-four inpatients from a stroke unit were randomly assigned to anklebot (n = 18) or passive manual stretching (n = 16) treatments. All suffered a first stroke with residual hemiparesis (ankle manual muscle test grade 1/5 to 4/5), and at least trace muscle activation in plantar- or dorsiflexion. Anklebot training employed an "assist-as-needed" approach during >200 volitional targeted paretic ankle movements, with difficulty adjusted to active range of motion and success rate. Stretching included >200 daily mobilizations in these same ranges. All sessions lasted 1 hour and assessments were not blinded. Both groups walked faster at discharge; however, the robot group improved more in percentage change of temporal symmetry (P = .032) and also of step length symmetry (P = .038), with longer nonparetic step lengths in the robot (133%) versus stretching (31%) groups. Paretic ankle control improved in the robot group, with increased peak (P ≤ .001) and mean (P ≤ .01) angular speeds, and increased movement smoothness (P ≤ .01). There were no adverse events. Though limited by small sample size and restricted entry criteria, our findings suggest that modular lower extremity robotics during early subacute hospitalization is well tolerated and improves ankle motor control and gait patterning. © The Author(s) 2014.

Anticipatory Driving for a Robot-Car Based on Supervised Learning

DEFF Research Database (Denmark)

Markelic, I.; Kulvicius, Tomas; Tamosiunaite, M.

2009-01-01

Using look ahead information and plan making improves hu- man driving. We therefore propose that also autonomously driving systems should dispose over such abilities. We adapt a machine learning approach, where the system, a car-like robot, is trained by an experienced driver by correlating visual...

Validation of ergonomic instructions in robot-assisted surgery simulator training.

Science.gov (United States)

Van't Hullenaar, C D P; Mertens, A C; Ruurda, J P; Broeders, I A M J

2018-05-01

Training in robot-assisted surgery focusses mainly on technical skills and instrument use. Training in optimal ergonomics during robotic surgery is often lacking, while improved ergonomics can be one of the key advantages of robot-assisted surgery. Therefore, the aim of this study was to assess whether a brief explanation on ergonomics of the console can improve body posture and performance. A comparative study was performed with 26 surgical interns and residents using the da Vinci skills simulator (Intuitive Surgical, Sunnyvale, CA). The intervention group received a compact instruction on ergonomic settings and coaching on clutch usage, while the control group received standard instructions for usage of the system. Participants performed two sets of five exercises. Analysis was performed on ergonomic score (RULA) and performance scores provided by the simulator. Mental and physical load scores (NASA-TLX and LED score) were also registered. The intervention group performed better in the clutch-oriented exercises, displaying less unnecessary movement and smaller deviation from the neutral position of the hands. The intervention group also scored significantly better on the RULA ergonomic score in both the exercises. No differences in overall performance scores and subjective scores were detected. The benefits of a brief instruction on ergonomics for novices are clear in this study. A single session of coaching and instruction leads to better ergonomic scores. The control group showed often inadequate ergonomic scores. No significant differences were found regarding physical discomfort, mental task load and overall performance scores.

A crossover pilot study evaluating the functional outcomes of two different types of robotic movement training in chronic stroke survivors using the arm exoskeleton BONES.

Science.gov (United States)

Milot, Marie-Hélène; Spencer, Steven J; Chan, Vicky; Allington, James P; Klein, Julius; Chou, Cathy; Bobrow, James E; Cramer, Steven C; Reinkensmeyer, David J

2013-12-19

To date, the limited degrees of freedom (DOF) of most robotic training devices hinders them from providing functional training following stroke. We developed a 6-DOF exoskeleton ("BONES") that allows movement of the upper limb to assist in rehabilitation. The objectives of this pilot study were to evaluate the impact of training with BONES on function of the affected upper limb, and to assess whether multijoint functional robotic training would translate into greater gains in arm function than single joint robotic training also conducted with BONES. Twenty subjects with mild to moderate chronic stroke participated in this crossover study. Each subject experienced multijoint functional training and single joint training three sessions per week, for four weeks, with the order of presentation randomized. The primary outcome measure was the change in Box and Block Test (BBT). The secondary outcome measures were the changes in Fugl-Meyer Arm Motor Scale (FMA), Wolf Motor Function Test (WMFT), Motor Activity Log (MAL), and quantitative measures of strength and speed of reaching. These measures were assessed at baseline, after each training period, and at a 3-month follow-up evaluation session. Training with the robotic exoskeleton resulted in significant improvements in the BBT, FMA, WMFT, MAL, shoulder and elbow strength, and reaching speed (p robotic training programs. However, for the BBT, WMFT and MAL, inequality of carryover effects were noted; subsequent analysis on the change in score between the baseline and first period of training again revealed no difference in the gains obtained between the types of training. Training with the 6 DOF arm exoskeleton improved motor function after chronic stroke, challenging the idea that robotic therapy is only useful for impairment reduction. The pilot results presented here also suggest that multijoint functional robotic training is not decisively superior to single joint robotic training. This challenges the idea that

"You gotta try it all": Parents' Experiences with Robotic Gait Training for their Children with Cerebral Palsy.

Science.gov (United States)

Beveridge, Briony; Feltracco, Deanna; Struyf, Jillian; Strauss, Emily; Dang, Saniya; Phelan, Shanon; Wright, F Virginia; Gibson, Barbara E

2015-01-01

Innovative robotic technologies hold strong promise for improving walking abilities of children with cerebral palsy (CP), but may create expectations for parents pursuing the "newest thing" in treatment. The aim of this qualitative study was to explore parents' values about walking in relation to their experiences with robotic gait training for their children. Semi-structured interviews were conducted with parents of five ambulatory children with CP participating in a randomized trial investigating robotic gait training effectiveness. Parents valued walking, especially "correct" walking, as a key component of their children's present and future well-being. They continually sought the "next best thing" in therapy and viewed the robotic gait trainer as a potentially revolutionary technology despite mixed experiences. The results can help inform rehabilitation therapists' knowledge of parents' values and perspectives, and guide effective collaborations toward meeting the therapeutic needs of children with CP.

Lower Limb Voluntary Movement Improvement Following a Robot-Assisted Locomotor Training in Spinal Cord Injury

Directory of Open Access Journals (Sweden)

Mirbagheri Mehdi

2011-12-01

Full Text Available Individuals with spinal cord injury (SCI suffer from severe impairments in voluntary movements. Literature reports a reduction in major kinematic and kinetic parameters of lower limbs’ joints. A body weight support treadmill training with robotic assistance has been widely used to improve lower-extremity function and locomotion in persons with SCI. Our objective was to explore the effects of 4-weeks robot-assisted locomotor training on voluntary movement of the ankle musculature in patients with incomplete SCI. In particular, we aimed to characterize the therapeutic effects of Lokomat training on kinematic measures (range of motion, velocity, smoothness during a dorsiflexion movement. We hypothesized that training would improve these measures. Preliminary results show an improvement of kinematic parameters during ankle dorsiflexion voluntary movement after a 4-weeks training in the major part of our participants. Complementary investigations are in progress to confirm these results and understand underlying mechanisms associated with the recovery.

Self-rumination, self-reflection, and depression: self-rumination counteracts the adaptive effect of self-reflection.

Science.gov (United States)

Takano, Keisuke; Tanno, Yoshihiko

2009-03-01

Self-focused attention has adaptive and maladaptive aspects: self-reflection and self-rumination [Trapnell, P. D., & Campbell, J. D. (1999). Private self-consciousness and the Five-Factor Model of personality: distinguishing rumination from reflection. Journal of Personality and Social Psychology, 76, 284-304]. Although reflection is thought to be associated with problem solving and the promotion of mental health, previous researches have shown that reflection does not always have an adaptive effect on depression. Authors have examined the causes behind this inconsistency by modeling the relationships among self-reflection, self-rumination, and depression. One hundred and eleven undergraduates (91 men and 20 women) participated in a two-time point assessment with a 3-week interval. Statistical analysis with structural equation modeling showed that self-reflection significantly predicted self-rumination, whereas self-rumination did not predict self-reflection. With regard to depression, self-reflection was associated with a lower level of depression; self-rumination, with a higher level of depression. The total effect of self-reflection on depression was almost zero. This result indicates that self-reflection per se has an adaptive effect, which is canceled out by the maladaptive effect of self-rumination, because reflectors are likely to ruminate and reflect simultaneously.

Image-guided robotic surgery.

Science.gov (United States)

Marescaux, Jacques; Solerc, Luc

2004-06-01

Medical image processing leads to an improvement in patient care by guiding the surgical gesture. Three-dimensional models of patients that are generated from computed tomographic scans or magnetic resonance imaging allow improved surgical planning and surgical simulation that offers the opportunity for a surgeon to train the surgical gesture before performing it for real. These two preoperative steps can be used intra-operatively because of the development of augmented reality, which consists of superimposing the preoperative three-dimensional model of the patient onto the real intraoperative view. Augmented reality provides the surgeon with a view of the patient in transparency and can also guide the surgeon, thanks to the real-time tracking of surgical tools during the procedure. When adapted to robotic surgery, this tool tracking enables visual serving with the ability to automatically position and control surgical robotic arms in three dimensions. It is also now possible to filter physiologic movements such as breathing or the heart beat. In the future, by combining augmented reality and robotics, these image-guided robotic systems will enable automation of the surgical procedure, which will be the next revolution in surgery.

Adaptive Inverse Optimal Control for Rehabilitation Robot Systems Using Actor-Critic Algorithm

Directory of Open Access Journals (Sweden)

Fancheng Meng

2014-01-01

Full Text Available The higher goal of rehabilitation robot is to aid a person to achieve a desired functional task (e.g., tracking trajectory based on assisted-as-needed principle. To this goal, a new adaptive inverse optimal hybrid control (AHC combining inverse optimal control and actor-critic learning is proposed. Specifically, an uncertain nonlinear rehabilitation robot model is firstly developed that includes human motor behavior dynamics. Then, based on this model, an open-loop error system is formed; thereafter, an inverse optimal control input is designed to minimize the cost functional and a NN-based actor-critic feedforward signal is responsible for the nonlinear dynamic part contaminated by uncertainties. Finally, the AHC controller is proven (through a Lyapunov-based stability analysis to yield a global uniformly ultimately bounded stability result, and the resulting cost functional is meaningful. Simulation and experiment on rehabilitation robot demonstrate the effectiveness of the proposed control scheme.

Distributed Robotics Education

DEFF Research Database (Denmark)

Lund, Henrik Hautop; Pagliarini, Luigi

2011-01-01

Distributed robotics takes many forms, for instance, multirobots, modular robots, and self-reconfigurable robots. The understanding and development of such advanced robotic systems demand extensive knowledge in engineering and computer science. In this paper, we describe the concept of a distribu......Distributed robotics takes many forms, for instance, multirobots, modular robots, and self-reconfigurable robots. The understanding and development of such advanced robotic systems demand extensive knowledge in engineering and computer science. In this paper, we describe the concept...... to be changed, related to multirobot control and human-robot interaction control from virtual to physical representation. The proposed system is valuable for bringing a vast number of issues into education – such as parallel programming, distribution, communication protocols, master dependency, connectivity...

Self-efficacy perception in high school students with mild intellectual disability in practical training

Directory of Open Access Journals (Sweden)

Milanović-Dobrota Biljana

2013-01-01

Full Text Available The main goal of this paper is to determine how students with mild intellectual disability perceive self-efficacy in practical training, with regard to the intellectual level, gender, work field and professional level for which they are being trained. The sample consists of 120 students with mild intellectual disability, of both genders, undergoing vocational training in five work fields for the second and third level professions. Adapted Self-Efficacy to Regulate Training Scale (Bandura, 2006 was used to assess the influence of negative internal and external factors on the students' efficacy at performing tasks in practical training. It was determined that there is a statistically significant difference among the examinees of the same disability category, but different level of intellectual functioning. Girls with lower and higher levels of intellectual functioning were found to perceive self-efficacy in practical training with lower level of confidence than boys with the same levels of intellectual functioning. The examinees undergoing the third level vocational training are more confident in their abilities to coordinate knowledge and skills in training regardless of different distracting factors. There we no statistically significant differences determined with regard to the work field. Assessing self-efficacy in training can direct the development of self-efficacy, help individuals gain a sense of control over their career development, and for professionals involved in finding jobs for persons with intellectual disability provide a predictive success/failure role at work.

An integrated gait rehabilitation training based on Functional Electrical Stimulation cycling and overground robotic exoskeleton in complete spinal cord injury patients: Preliminary results.

Science.gov (United States)

Mazzoleni, S; Battini, E; Rustici, A; Stampacchia, G

2017-07-01

The aim of this study is to investigate the effects of an integrated gait rehabilitation training based on Functional Electrical Stimulation (FES)-cycling and overground robotic exoskeleton in a group of seven complete spinal cord injury patients on spasticity and patient-robot interaction. They underwent a robot-assisted rehabilitation training based on two phases: n=20 sessions of FES-cycling followed by n= 20 sessions of robot-assisted gait training based on an overground robotic exoskeleton. The following clinical outcome measures were used: Modified Ashworth Scale (MAS), Numerical Rating Scale (NRS) on spasticity, Penn Spasm Frequency Scale (PSFS), Spinal Cord Independence Measure Scale (SCIM), NRS on pain and International Spinal Cord Injury Pain Data Set (ISCI). Clinical outcome measures were assessed before (T0) after (T1) the FES-cycling training and after (T2) the powered overground gait training. The ability to walk when using exoskeleton was assessed by means of 10 Meter Walk Test (10MWT), 6 Minute Walk Test (6MWT), Timed Up and Go test (TUG), standing time, walking time and number of steps. Statistically significant changes were found on the MAS score, NRS-spasticity, 6MWT, TUG, standing time and number of steps. The preliminary results of this study show that an integrated gait rehabilitation training based on FES-cycling and overground robotic exoskeleton in complete SCI patients can provide a significant reduction of spasticity and improvements in terms of patient-robot interaction.

SWARM-BOT: Pattern Formation in a Swarm of Self-Assembling Mobile Robots

OpenAIRE

El Kamel, A.; Mellouli, K.; Borne, P.; Sahin, E.; Labella, T.H.; Trianni, V.; Deneubourg, J.-L.; Rasse, P.; Floreano, D.; Gambardella, L.M.; Mondada, F.; Nolfi, S.; Dorigo, M.

2002-01-01

In this paper we introduce a new robotic system, called swarm-bot. The system consists of a swarm of mobile robots with the ability to connect to/disconnect from each other to self-assemble into different kinds of structures. First, we describe our vision and the goals of the project. Then we present preliminary results on the formation of patterns obtained from a grid-world simulation of the system.

A Sit-to-Stand Training Robot and Its Performance Evaluation: Dynamic Analysis in Lower Limb Rehabilitation Activities

Science.gov (United States)

Cao, Enguo; Inoue, Yoshio; Liu, Tao; Shibata, Kyoko

In many countries in which the phenomenon of population aging is being experienced, motor function recovery activities have aroused much interest. In this paper, a sit-to-stand rehabilitation robot utilizing a double-rope system was developed, and the performance of the robot was evaluated by analyzing the dynamic parameters of human lower limbs. For the robot control program, an impedance control method with a training game was developed to increase the effectiveness and frequency of rehabilitation activities, and a calculation method was developed for evaluating the joint moments of hip, knee, and ankle. Test experiments were designed, and four subjects were requested to stand up from a chair with assistance from the rehabilitation robot. In the experiments, body segment rotational angles, trunk movement trajectories, rope tensile forces, ground reaction forces (GRF) and centers of pressure (COP) were measured by sensors, and the moments of ankle, knee and hip joint were real-time calculated using the sensor-measured data. The experiment results showed that the sit-to-stand rehabilitation robot with impedance control method could maintain the comfortable training postures of users, decrease the moments of limb joints, and enhance training effectiveness. Furthermore, the game control method could encourage collaboration between the brain and limbs, and allow for an increase in the frequency and intensity of rehabilitation activities.

Book Review: Training the Party: Party Adaptation and Elite Training in Reform-era China

DEFF Research Database (Denmark)

Brødsgaard, Kjeld Erik

2016-01-01

Review of: Training the Party: Party Adaptation and Elite Training in Reform-era China. Charlotte P. Lee . Cambridge: Cambridge University Press, 2015. xii + 251 pp. $99.99. ISBN 978-1-107-09063-7......Review of: Training the Party: Party Adaptation and Elite Training in Reform-era China. Charlotte P. Lee . Cambridge: Cambridge University Press, 2015. xii + 251 pp. $99.99. ISBN 978-1-107-09063-7...

Friendly network robotics; Friendly network robotics

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-03-01

This paper summarizes the research results on the friendly network robotics in fiscal 1996. This research assumes an android robot as an ultimate robot and the future robot system utilizing computer network technology. The robot aiming at human daily work activities in factories or under extreme environments is required to work under usual human work environments. The human robot with similar size, shape and functions to human being is desirable. Such robot having a head with two eyes, two ears and mouth can hold a conversation with human being, can walk with two legs by autonomous adaptive control, and has a behavior intelligence. Remote operation of such robot is also possible through high-speed computer network. As a key technology to use this robot under coexistence with human being, establishment of human coexistent robotics was studied. As network based robotics, use of robots connected with computer networks was also studied. In addition, the R-cube (R{sup 3}) plan (realtime remote control robot technology) was proposed. 82 refs., 86 figs., 12 tabs.

The Role of Item Feedback in Self-Adapted Testing.

Science.gov (United States)

Roos, Linda L.; And Others

1997-01-01

The importance of item feedback in self-adapted testing was studied by comparing feedback and no feedback conditions for computerized adaptive tests and self-adapted tests taken by 363 college students. Results indicate that item feedback is not necessary to realize score differences between self-adapted and computerized adaptive testing. (SLD)

Explorer-II: Wireless Self-Powered Visual and NDE Robotic Inspection System for Live Gas Distribution Mains

Energy Technology Data Exchange (ETDEWEB)

Carnegie Mellon University

2008-09-30

Carnegie Mellon University (CMU) under contract from Department of Energy/National Energy Technology Laboratory (DoE/NETL) and co-funding from the Northeast Gas Association (NGA), has completed the overall system design, field-trial and Magnetic Flux Leakage (MFL) sensor evaluation program for the next-generation Explorer-II (X-II) live gas main Non-destructive Evaluation (NDE) and visual inspection robot platform. The design is based on the Explorer-I prototype which was built and field-tested under a prior (also DoE- and NGA co-funded) program, and served as the validation that self-powered robots under wireless control could access and navigate live natural gas distribution mains. The X-II system design ({approx}8 ft. and 66 lbs.) was heavily based on the X-I design, yet was substantially expanded to allow the addition of NDE sensor systems (while retaining its visual inspection capability), making it a modular system, and expanding its ability to operate at pressures up to 750 psig (high-pressure and unpiggable steel-pipe distribution mains). A new electronics architecture and on-board software kernel were added to again improve system performance. A locating sonde system was integrated to allow for absolute position-referencing during inspection (coupled with external differential GPS) and emergency-locating. The power system was upgraded to utilize lithium-based battery-cells for an increase in mission-time. The resulting robot-train system with CAD renderings of the individual modules. The system architecture now relies on a dual set of end camera-modules to house the 32-bit processors (Single-Board Computer or SBC) as well as the imaging and wireless (off-board) and CAN-based (on-board) communication hardware and software systems (as well as the sonde-coil and -electronics). The drive-module (2 ea.) are still responsible for bracing (and centering) to drive in push/pull fashion the robot train into and through the pipes and obstacles. The steering modules

A Self-Organizing Interaction and Synchronization Method between a Wearable Device and Mobile Robot.

Science.gov (United States)

Kim, Min Su; Lee, Jae Geun; Kang, Soon Ju

2016-06-08

In the near future, we can expect to see robots naturally following or going ahead of humans, similar to pet behavior. We call this type of robots "Pet-Bot". To implement this function in a robot, in this paper we introduce a self-organizing interaction and synchronization method between wearable devices and Pet-Bots. First, the Pet-Bot opportunistically identifies its owner without any human intervention, which means that the robot self-identifies the owner's approach on its own. Second, Pet-Bot's activity is synchronized with the owner's behavior. Lastly, the robot frequently encounters uncertain situations (e.g., when the robot goes ahead of the owner but meets a situation where it cannot make a decision, or the owner wants to stop the Pet-Bot synchronization mode to relax). In this case, we have adopted a gesture recognition function that uses a 3-D accelerometer in the wearable device. In order to achieve the interaction and synchronization in real-time, we use two wireless communication protocols: 125 kHz low-frequency (LF) and 2.4 GHz Bluetooth low energy (BLE). We conducted experiments using a prototype Pet-Bot and wearable devices to verify their motion recognition of and synchronization with humans in real-time. The results showed a guaranteed level of accuracy of at least 94%. A trajectory test was also performed to demonstrate the robot's control performance when following or leading a human in real-time.

Training self-assessment and task-selection skills to foster self-regulated learning: Do trained skills transfer across domains?

Science.gov (United States)

Raaijmakers, Steven F; Baars, Martine; Paas, Fred; van Merriënboer, Jeroen J G; van Gog, Tamara

2018-01-01

Students' ability to accurately self-assess their performance and select a suitable subsequent learning task in response is imperative for effective self-regulated learning. Video modeling examples have proven effective for training self-assessment and task-selection skills, and-importantly-such training fostered self-regulated learning outcomes. It is unclear, however, whether trained skills would transfer across domains. We investigated whether skills acquired from training with either a specific, algorithmic task-selection rule or a more general heuristic task-selection rule in biology would transfer to self-regulated learning in math. A manipulation check performed after the training confirmed that both algorithmic and heuristic training improved task-selection skills on the biology problems compared with the control condition. However, we found no evidence that students subsequently applied the acquired skills during self-regulated learning in math. Future research should investigate how to support transfer of task-selection skills across domains.

An Adaptive Multi-Objective Particle Swarm Optimization Algorithm for Multi-Robot Path Planning

Directory of Open Access Journals (Sweden)

Nizar Hadi Abbas

2016-07-01

Full Text Available This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In order to evaluate the proposed algorithm in term of finding the best solution, six benchmark test functions are used to make a comparison between AMOPSO and the standard MOPSO. The results show that the AMOPSO has a better ability to get away from local optimums with a quickest convergence than the MOPSO. The simulation results using Matlab 2014a, indicate that this methodology is extremely valuable for every robot in multi-robot framework to discover its own particular proper pa‌th from the start to the destination position with minimum distance and time.

Developing a successful robotics program.

Science.gov (United States)

Luthringer, Tyler; Aleksic, Ilija; Caire, Arthur; Albala, David M

2012-01-01

Advancements in the robotic surgical technology have revolutionized the standard of care for many surgical procedures. The purpose of this review is to evaluate the important considerations in developing a new robotics program at a given healthcare institution. Patients' interest in robotic-assisted surgery has and continues to grow because of improved outcomes and decreased periods of hospitalization. Resulting market forces have created a solid foundation for the implementation of robotic surgery into surgical practice. Given proper surgeon experience and an efficient system, robotic-assisted procedures have been cost comparable to open surgical alternatives. Surgeon training and experience is closely linked to the efficiency of a new robotics program. Formally trained robotic surgeons have better patient outcomes and shorter operative times. Training in robotics has shown no negative impact on patient outcomes or mentor learning curves. Individual economic factors of local healthcare settings must be evaluated when planning for a new robotics program. The high cost of the robotic surgical platform is best offset with a large surgical volume. A mature, experienced surgeon is integral to the success of a new robotics program.

Training induced adaptation in horse skeletal muscle

NARCIS (Netherlands)

Dam, K.G. van

2006-01-01

It appears that the physiological and biochemical adaptation of skeletal muscle to training in equine species shows a lot of similarities with human and rodent physiological adaptation. On the other hand it is becoming increasingly clear that intra-cellular mechanisms of adaptation (substrate

Speeding up the learning of robot kinematics through function decomposition.

Science.gov (United States)

Ruiz de Angulo, Vicente; Torras, Carme

2005-11-01

The main drawback of using neural networks or other example-based learning procedures to approximate the inverse kinematics (IK) of robot arms is the high number of training samples (i.e., robot movements) required to attain an acceptable precision. We propose here a trick, valid for most industrial robots, that greatly reduces the number of movements needed to learn or relearn the IK to a given accuracy. This trick consists in expressing the IK as a composition of learnable functions, each having half the dimensionality of the original mapping. Off-line and on-line training schemes to learn these component functions are also proposed. Experimental results obtained by using nearest neighbors and parameterized self-organizing map, with and without the decomposition, show that the time savings granted by the proposed scheme grow polynomially with the precision required.

Modular Ankle Robotics Training in Early Sub-Acute Stroke: A Randomized Controlled Pilot Study

Science.gov (United States)

Forrester, Larry W.; Roy, Anindo; Krywonis, Amanda; Kehs, Glenn; Krebs, Hermano Igo; Macko, Richard F.

2014-01-01

Background Modular lower extremity (LE) robotics may offer a valuable avenue for restoring neuromotor control after hemiparetic stroke. Prior studies show that visually-guided and visually-evoked practice with an ankle robot (anklebot) improves paretic ankle motor control that translates into improved overground walking. Objective Assess the feasibility and efficacy of daily anklebot training during early sub-acute hospitalization post-stroke. Methods Thirty-four inpatients from a stroke unit were randomly assigned to anklebot (N=18) or passive manual stretching (N=16) treatments. All suffered a first stroke with residual hemiparesis (ankle manual muscle test grade 1/5 to 4/5), and at least trace muscle activation in plantar- or dorsiflexion. Anklebot training employed an “assist-as-needed” approach during > 200 volitional targeted paretic ankle movements, with difficulty adjusted to active range of motion and success rate. Stretching included >200 daily mobilizations in these same ranges. All sessions lasted 1 hour and assessments were not blinded. Results Both groups walked faster at discharge, however the robot group improved more in percent change of temporal symmetry (p=0.032) and also of step length symmetry (p=0.038), with longer nonparetic step lengths in the robot (133%) vs. stretching (31%) groups. Paretic ankle control improved in the robot group, with increased peak (p≤ 0.001) and mean (p≤ 0.01) angular speeds, and increased movement smoothness (p≤ 0.01). There were no adverse events. Conclusion Though limited by small sample size and restricted entry criteria, our findings suggest that modular lower extremity robotics during early sub-acute hospitalization is well tolerated and improves ankle motor control and gait patterning. PMID:24515923

Error amplification to promote motor learning and motivation in therapy robotics.

Science.gov (United States)

Shirzad, Navid; Van der Loos, H F Machiel

2012-01-01

To study the effects of different feedback error amplification methods on a subject's upper-limb motor learning and affect during a point-to-point reaching exercise, we developed a real-time controller for a robotic manipulandum. The reaching environment was visually distorted by implementing a thirty degrees rotation between the coordinate systems of the robot's end-effector and the visual display. Feedback error amplification was provided to subjects as they trained to learn reaching within the visually rotated environment. Error amplification was provided either visually or through both haptic and visual means, each method with two different amplification gains. Subjects' performance (i.e., trajectory error) and self-reports to a questionnaire were used to study the speed and amount of adaptation promoted by each error amplification method and subjects' emotional changes. We found that providing haptic and visual feedback promotes faster adaptation to the distortion and increases subjects' satisfaction with the task, leading to a higher level of attentiveness during the exercise. This finding can be used to design a novel exercise regimen, where alternating between error amplification methods is used to both increase a subject's motor learning and maintain a minimum level of motivational engagement in the exercise. In future experiments, we will test whether such exercise methods will lead to a faster learning time and greater motivation to pursue a therapy exercise regimen.

Weekly Time Course of Neuro-Muscular Adaptation to Intensive Strength Training.

Science.gov (United States)

Brown, Niklas; Bubeck, Dieter; Haeufle, Daniel F B; Weickenmeier, Johannes; Kuhl, Ellen; Alt, Wilfried; Schmitt, Syn

2017-01-01

Detailed description of the time course of muscular adaptation is rarely found in literature. Thus, models of muscular adaptation are difficult to validate since no detailed data of adaptation are available. In this article, as an initial step toward a detailed description and analysis of muscular adaptation, we provide a case report of 8 weeks of intense strength training with two active, male participants. Muscular adaptations were analyzed on a morphological level with MRI scans of the right quadriceps muscle and the calculation of muscle volume, on a voluntary strength level by isometric voluntary contractions with doublet stimulation (interpolated twitch technique) and on a non-voluntary level by resting twitch torques. Further, training volume and isokinetic power were closely monitored during the training phase. Data were analyzed weekly for 1 week prior to training, pre-training, 8 weeks of training and 2 weeks of detraining (no strength training). Results show a very individual adaptation to the intense strength training protocol. While training volume and isokinetic power increased linearly during the training phase, resting twitch parameters decreased for both participants after the first week of training and stayed below baseline until de-training. Voluntary activation level showed an increase in the first 4 weeks of training, while maximum voluntary contraction showed only little increase compared to baseline. Muscle volume increased for both subjects. Especially training status seemed to influence the acute reaction to intense strength training. Fatigue had a major influence on performance and could only be overcome by one participant. The results give a first detailed insight into muscular adaptation to intense strength training on various levels, providing a basis of data for a validation of muscle fatigue and adaptation models.

The AGINAO Self-Programming Engine

Science.gov (United States)

Skaba, Wojciech

2013-01-01

The AGINAO is a project to create a human-level artificial general intelligence system (HL AGI) embodied in the Aldebaran Robotics' NAO humanoid robot. The dynamical and open-ended cognitive engine of the robot is represented by an embedded and multi-threaded control program, that is self-crafted rather than hand-crafted, and is executed on a simulated Universal Turing Machine (UTM). The actual structure of the cognitive engine emerges as a result of placing the robot in a natural preschool-like environment and running a core start-up system that executes self-programming of the cognitive layer on top of the core layer. The data from the robot's sensory devices supplies the training samples for the machine learning methods, while the commands sent to actuators enable testing hypotheses and getting a feedback. The individual self-created subroutines are supposed to reflect the patterns and concepts of the real world, while the overall program structure reflects the spatial and temporal hierarchy of the world dependencies. This paper focuses on the details of the self-programming approach, limiting the discussion of the applied cognitive architecture to a necessary minimum.

Cardiovascular adaptations to exercise training

DEFF Research Database (Denmark)

Hellsten, Ylva; Nyberg, Michael

2016-01-01

Aerobic exercise training leads to cardiovascular changes that markedly increase aerobic power and lead to improved endurance performance. The functionally most important adaptation is the improvement in maximal cardiac output which is the result of an enlargement in cardiac dimension, improved...... and peripheral cardiovascular adaptations with a focus on humans, but also covers animal data. © 2016 American Physiological Society. Compr Physiol 6:1-32, 2016....

Self-Structured Organizing Single-Input CMAC Control for Robot Manipulator

Directory of Open Access Journals (Sweden)

ThanhQuyen Ngo

2011-09-01

Full Text Available This paper represents a self-structured organizing single-input control system based on differentiable cerebellar model articulation controller (CMAC for an n-link robot manipulator to achieve the high-precision position tracking. In the proposed scheme, the single-input CMAC controller is solely used to control the plant, so the input space dimension of CMAC can be simplified and no conventional controller is needed. The structure of single-input CMAC will also be self-organized; that is, the layers of single-input CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The online tuning laws of single-input CMAC parameters are derived in gradient-descent learning method and the discrete-type Lyapunov function is applied to determine the learning rates of proposed control system so that the stability of the system can be guaranteed. The simulation results of robot manipulator are provided to verify the effectiveness of the proposed control methodology.

micROS: a morphable, intelligent and collective robot operating system.

Science.gov (United States)

Yang, Xuejun; Dai, Huadong; Yi, Xiaodong; Wang, Yanzhen; Yang, Shaowu; Zhang, Bo; Wang, Zhiyuan; Zhou, Yun; Peng, Xuefeng

2016-01-01

Robots are developing in much the same way that personal computers did 40 years ago, and robot operating system is the critical basis. Current robot software is mainly designed for individual robots. We present in this paper the design of micROS, a morphable, intelligent and collective robot operating system for future collective and collaborative robots. We first present the architecture of micROS, including the distributed architecture for collective robot system as a whole and the layered architecture for every single node. We then present the design of autonomous behavior management based on the observe-orient-decide-act cognitive behavior model and the design of collective intelligence including collective perception, collective cognition, collective game and collective dynamics. We also give the design of morphable resource management, which first categorizes robot resources into physical, information, cognitive and social domains, and then achieve morphability based on self-adaptive software technology. We finally deploy micROS on NuBot football robots and achieve significant improvement in real-time performance.

Adaptive control of 5 DOF upper-limb exoskeleton robot with improved safety.

Science.gov (United States)

Kang, Hao-Bo; Wang, Jian-Hui

2013-11-01

This paper studies an adaptive control strategy for a class of 5 DOF upper-limb exoskeleton robot with a special safety consideration. The safety requirement plays a critical role in the clinical treatment when assisting patients with shoulder, elbow and wrist joint movements. With the objective of assuring the tracking performance of the pre-specified operations, the proposed adaptive controller is firstly designed to be robust to the model uncertainties. To further improve the safety and fault-tolerance in the presence of unknown large parameter variances or even actuator faults, the adaptive controller is on-line updated according to the information provided by an adaptive observer without additional sensors. An output tracking performance is well achieved with a tunable error bound. The experimental example also verifies the effectiveness of the proposed control scheme. © 2013 ISA. Published by ISA. All rights reserved.

Data-Based Control for Humanoid Robots Using Support Vector Regression, Fuzzy Logic, and Cubature Kalman Filter

Directory of Open Access Journals (Sweden)

Liyang Wang

2016-01-01

Full Text Available Time-varying external disturbances cause instability of humanoid robots or even tip robots over. In this work, a trapezoidal fuzzy least squares support vector regression- (TF-LSSVR- based control system is proposed to learn the external disturbances and increase the zero-moment-point (ZMP stability margin of humanoid robots. First, the humanoid states and the corresponding control torques of the joints for training the controller are collected by implementing simulation experiments. Secondly, a TF-LSSVR with a time-related trapezoidal fuzzy membership function (TFMF is proposed to train the controller using the simulated data. Thirdly, the parameters of the proposed TF-LSSVR are updated using a cubature Kalman filter (CKF. Simulation results are provided. The proposed method is shown to be effective in learning and adapting occasional external disturbances and ensuring the stability margin of the robot.

Individualised and adaptive upper limb rehabilitation with industrial robot using dynamic movement primitives

DEFF Research Database (Denmark)

Nielsen, Jacob; Sørensen, Anders Stengaard; Christensen, Thomas Søndergaard

Stroke is a leading cause of serious long-term disability. Post-stroke rehabilitation is a demanding task for the patient and a costly challenge for both society and healthcare systems. We present a novel approach for training of upper extremities after a stroke by utilising an industrial robotic...

Task oriented evaluation system for maintenance robots

International Nuclear Information System (INIS)

Asame, Hajime; Endo, Isao; Kotosaka, Shin-ya; Takata, Shozo; Hiraoka, Hiroyuki; Kohda, Takehisa; Matsumoto, Akihiro; Yamagishi, Kiichiro.

1994-01-01

The adaptability evaluation of maintenance robots to autonomous plants has been discussed. In this paper, a new concept of autonomous plant with maintenance robots are introduced, and a framework of autonomous maintenance system is proposed. Then, task-oriented evaluation of robot arms is discussed for evaluating their adaptability to maintenance tasks, and a new criterion called operability is proposed for adaptability evaluation. The task-oriented evaluation system is implemented and applied to structural design of robot arms. Using genetic algorithm, an optimal structure adaptable to a pump disassembly task is obtained. (author)

The effect of impedance-controlled robotic gait training on walking ability and quality in individuals with chronic incomplete spinal cord injury: an explorative study.

Science.gov (United States)

Fleerkotte, Bertine M; Koopman, Bram; Buurke, Jaap H; van Asseldonk, Edwin H F; van der Kooij, Herman; Rietman, Johan S

2014-03-04

There is increasing interest in the use of robotic gait-training devices in walking rehabilitation of incomplete spinal cord injured (iSCI) individuals. These devices provide promising opportunities to increase the intensity of training and reduce physical demands on therapists. Despite these potential benefits, robotic gait-training devices have not yet demonstrated clear advantages over conventional gait-training approaches, in terms of functional outcomes. This might be due to the reduced active participation and step-to-step variability in most robotic gait-training strategies, when compared to manually assisted therapy. Impedance-controlled devices can increase active participation and step-to-step variability. The aim of this study was to assess the effect of impedance-controlled robotic gait training on walking ability and quality in chronic iSCI individuals. A group of 10 individuals with chronic iSCI participated in an explorative clinical trial. Participants trained three times a week for eight weeks using an impedance-controlled robotic gait trainer (LOPES: LOwer extremity Powered ExoSkeleton). Primary outcomes were the 10-meter walking test (10 MWT), the Walking Index for Spinal Cord Injury (WISCI II), the six-meter walking test (6 MWT), the Timed Up and Go test (TUG) and the Lower Extremity Motor Scores (LEMS). Secondary outcomes were spatiotemporal and kinematics measures. All participants were tested before, during, and after training and at 8 weeks follow-up. Participants experienced significant improvements in walking speed (0.06 m/s, p = 0.008), distance (29 m, p = 0.005), TUG (3.4 s, p = 0.012), LEMS (3.4, p = 0.017) and WISCI after eight weeks of training with LOPES. At the eight-week follow-up, participants retained the improvements measured at the end of the training period. Significant improvements were also found in spatiotemporal measures and hip range of motion. Robotic gait training using an impedance-controlled robot is feasible in gait

Testing Biological Hypotheses with Embodied Robots: Adaptations, Accidents, and By-Products in the Evolution of Vertebrates

OpenAIRE

Roberts, Sonia F.; Hirokawa, Jonathan; Rosenblum, Hannah G.; Sakhtah, Hassan; Gutierrez, Andres A.; Porter, Marianne E.; Long, John H.

2014-01-01

Evolutionary robotics allows biologists to test hypotheses about extinct animals. In our case, we modeled some of the first vertebrates, jawless fishes, in order to study the evolution of the trait after which vertebrates are named: vertebrae. We tested the hypothesis that vertebrae are an adaptation for enhanced feeding and fleeing performance. We created a population of autonomous embodied robots, Preyro, in which the number of vertebrae, N, were free to evolve. In addition, two other trait...

Adaptive Control for Autonomous Navigation of Mobile Robots Considering Time Delay and Uncertainty

Science.gov (United States)

Armah, Stephen Kofi

Autonomous control of mobile robots has attracted considerable attention of researchers in the areas of robotics and autonomous systems during the past decades. One of the goals in the field of mobile robotics is development of platforms that robustly operate in given, partially unknown, or unpredictable environments and offer desired services to humans. Autonomous mobile robots need to be equipped with effective, robust and/or adaptive, navigation control systems. In spite of enormous reported work on autonomous navigation control systems for mobile robots, achieving the goal above is still an open problem. Robustness and reliability of the controlled system can always be improved. The fundamental issues affecting the stability of the control systems include the undesired nonlinear effects introduced by actuator saturation, time delay in the controlled system, and uncertainty in the model. This research work develops robustly stabilizing control systems by investigating and addressing such nonlinear effects through analytical, simulations, and experiments. The control systems are designed to meet specified transient and steady-state specifications. The systems used for this research are ground (Dr Robot X80SV) and aerial (Parrot AR.Drone 2.0) mobile robots. Firstly, an effective autonomous navigation control system is developed for X80SV using logic control by combining 'go-to-goal', 'avoid-obstacle', and 'follow-wall' controllers. A MATLAB robot simulator is developed to implement this control algorithm and experiments are conducted in a typical office environment. The next stage of the research develops an autonomous position (x, y, and z) and attitude (roll, pitch, and yaw) controllers for a quadrotor, and PD-feedback control is used to achieve stabilization. The quadrotor's nonlinear dynamics and kinematics are implemented using MATLAB S-function to generate the state output. Secondly, the white-box and black-box approaches are used to obtain a linearized

Cerebellar-inspired adaptive control of a robot eye actuated by pneumatic artificial muscles.

Science.gov (United States)

Lenz, Alexander; Anderson, Sean R; Pipe, A G; Melhuish, Chris; Dean, Paul; Porrill, John

2009-12-01

In this paper, a model of cerebellar function is implemented and evaluated in the control of a robot eye actuated by pneumatic artificial muscles. The investigated control problem is stabilization of the visual image in response to disturbances. This is analogous to the vestibuloocular reflex (VOR) in humans. The cerebellar model is structurally based on the adaptive filter, and the learning rule is computationally analogous to least-mean squares, where parameter adaptation at the parallel fiber/Purkinje cell synapse is driven by the correlation of the sensory error signal (carried by the climbing fiber) and the motor command signal. Convergence of the algorithm is first analyzed in simulation on a model of the robot and then tested online in both one and two degrees of freedom. The results show that this model of neural function successfully works on a real-world problem, providing empirical evidence for validating: 1) the generic cerebellar learning algorithm; 2) the function of the cerebellum in the VOR; and 3) the signal transmission between functional neural components of the VOR.

Adaptation of sensor morphology: an integrative view of perception from biologically inspired robotics perspective

Science.gov (United States)

Nurzaman, Surya G.

2016-01-01

Sensor morphology, the morphology of a sensing mechanism which plays a role of shaping the desired response from physical stimuli from surroundings to generate signals usable as sensory information, is one of the key common aspects of sensing processes. This paper presents a structured review of researches on bioinspired sensor morphology implemented in robotic systems, and discusses the fundamental design principles. Based on literature review, we propose two key arguments: first, owing to its synthetic nature, biologically inspired robotics approach is a unique and powerful methodology to understand the role of sensor morphology and how it can evolve and adapt to its task and environment. Second, a consideration of an integrative view of perception by looking into multidisciplinary and overarching mechanisms of sensor morphology adaptation across biology and engineering enables us to extract relevant design principles that are important to extend our understanding of the unfinished concepts in sensing and perception. PMID:27499843

Self-Adaptive Systems for Machine Intelligence

CERN Document Server

He, Haibo

2011-01-01

This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide application