Kinematic Control Of Redundant Robot Arms Using Neural Networks

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Kinematic Control of Redundant Robot Arms Using Neural Networks

Author : Shuai Li
Publisher : John Wiley & Sons
ISBN 13 : 1119556996
Total Pages : 278 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Kinematic Control of Redundant Robot Arms Using Neural Networks by : Shuai Li

Download or read book Kinematic Control of Redundant Robot Arms Using Neural Networks written by Shuai Li and published by John Wiley & Sons. This book was released on 2019-02-12 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents pioneering and comprehensive work on engaging movement in robotic arms, with a specific focus on neural networks This book presents and investigates different methods and schemes for the control of robotic arms whilst exploring the field from all angles. On a more specific level, it deals with the dynamic-neural-network based kinematic control of redundant robot arms by using theoretical tools and simulations. Kinematic Control of Redundant Robot Arms Using Neural Networks is divided into three parts: Neural Networks for Serial Robot Arm Control; Neural Networks for Parallel Robot Control; and Neural Networks for Cooperative Control. The book starts by covering zeroing neural networks for control, and follows up with chapters on adaptive dynamic programming neural networks for control; projection neural networks for robot arm control; and neural learning and control co-design for robot arm control. Next, it looks at robust neural controller design for robot arm control and teaches readers how to use neural networks to avoid robot singularity. It then instructs on neural network based Stewart platform control and neural network based learning and control co-design for Stewart platform control. The book finishes with a section on zeroing neural networks for robot arm motion generation. Provides comprehensive understanding on robot arm control aided with neural networks Presents neural network-based control techniques for single robot arms, parallel robot arms (Stewart platforms), and cooperative robot arms Provides a comparison of, and the advantages of, using neural networks for control purposes rather than traditional control based methods Includes simulation and modelling tasks (e.g., MATLAB) for onward application for research and engineering development By focusing on robot arm control aided by neural networks whilst examining central topics surrounding the field, Kinematic Control of Redundant Robot Arms Using Neural Networks is an excellent book for graduate students and academic and industrial researchers studying neural dynamics, neural networks, analog and digital circuits, mechatronics, and mechanical engineering.

Neural Networks for Cooperative Control of Multiple Robot Arms

Author : Shuai Li
Publisher : Springer
ISBN 13 : 9811070377
Total Pages : 74 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Neural Networks for Cooperative Control of Multiple Robot Arms by : Shuai Li

Download or read book Neural Networks for Cooperative Control of Multiple Robot Arms written by Shuai Li and published by Springer. This book was released on 2017-10-29 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book to focus on solving cooperative control problems of multiple robot arms using different centralized or distributed neural network models, presenting methods and algorithms together with the corresponding theoretical analysis and simulated examples. It is intended for graduate students and academic and industrial researchers in the field of control, robotics, neural networks, simulation and modelling.

Repetitive Motion Planning and Control of Redundant Robot Manipulators

Author : Yunong Zhang
Publisher : Springer Science & Business Media
ISBN 13 : 3642375189
Total Pages : 196 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Repetitive Motion Planning and Control of Redundant Robot Manipulators by : Yunong Zhang

Download or read book Repetitive Motion Planning and Control of Redundant Robot Manipulators written by Yunong Zhang and published by Springer Science & Business Media. This book was released on 2014-07-08 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Repetitive Motion Planning and Control of Redundant Robot Manipulators presents four typical motion planning schemes based on optimization techniques, including the fundamental RMP scheme and its extensions. These schemes are unified as quadratic programs (QPs), which are solved by neural networks or numerical algorithms. The RMP schemes are demonstrated effectively by the simulation results based on various robotic models; the experiments applying the fundamental RMP scheme to a physical robot manipulator are also presented. As the schemes and the corresponding solvers presented in the book have solved the non-repetitive motion problems existing in redundant robot manipulators, it is of particular use in applying theoretical research based on the quadratic program for redundant robot manipulators in industrial situations. This book will be a valuable reference work for engineers, researchers, advanced undergraduate and graduate students in robotics fields. Yunong Zhang is a professor at The School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China; Zhijun Zhang is a research fellow working at the same institute.

AI based Robot Safe Learning and Control

Author : Xuefeng Zhou
Publisher : Springer Nature
ISBN 13 : 9811555036
Total Pages : 138 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis AI based Robot Safe Learning and Control by : Xuefeng Zhou

Download or read book AI based Robot Safe Learning and Control written by Xuefeng Zhou and published by Springer Nature. This book was released on 2020-06-02 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.

Robot Manipulator Redundancy Resolution

Author : Yunong Zhang
Publisher : John Wiley & Sons
ISBN 13 : 1119381428
Total Pages : 320 pages
Book Rating : 4.1/5 (193 download)

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Book Synopsis Robot Manipulator Redundancy Resolution by : Yunong Zhang

Download or read book Robot Manipulator Redundancy Resolution written by Yunong Zhang and published by John Wiley & Sons. This book was released on 2017-09-11 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces a revolutionary, quadratic-programming based approach to solving long-standing problems in motion planning and control of redundant manipulators This book describes a novel quadratic programming approach to solving redundancy resolutions problems with redundant manipulators. Known as ``QP-unified motion planning and control of redundant manipulators'' theory, it systematically solves difficult optimization problems of inequality-constrained motion planning and control of redundant manipulators that have plagued robotics engineers and systems designers for more than a quarter century. An example of redundancy resolution could involve a robotic limb with six joints, or degrees of freedom (DOFs), with which to position an object. As only five numbers are required to specify the position and orientation of the object, the robot can move with one remaining DOF through practically infinite poses while performing a specified task. In this case redundancy resolution refers to the process of choosing an optimal pose from among that infinite set. A critical issue in robotic systems control, the redundancy resolution problem has been widely studied for decades, and numerous solutions have been proposed. This book investigates various approaches to motion planning and control of redundant robot manipulators and describes the most successful strategy thus far developed for resolving redundancy resolution problems. Provides a fully connected, systematic, methodological, consecutive, and easy approach to solving redundancy resolution problems Describes a new approach to the time-varying Jacobian matrix pseudoinversion, applied to the redundant-manipulator kinematic control Introduces The QP-based unification of robots' redundancy resolution Illustrates the effectiveness of the methods presented using a large number of computer simulation results based on PUMA560, PA10, and planar robot manipulators Provides technical details for all schemes and solvers presented, for readers to adopt and customize them for specific industrial applications Robot Manipulator Redundancy Resolution is must-reading for advanced undergraduates and graduate students of robotics, mechatronics, mechanical engineering, tracking control, neural dynamics/neural networks, numerical algorithms, computation and optimization, simulation and modelling, analog, and digital circuits. It is also a valuable working resource for practicing robotics engineers and systems designers and industrial researchers.

Biologically Inspired Control of Humanoid Robot Arms

Author : Adam Spiers
Publisher : Springer
ISBN 13 : 3319301608
Total Pages : 276 pages
Book Rating : 4.3/5 (193 download)

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Book Synopsis Biologically Inspired Control of Humanoid Robot Arms by : Adam Spiers

Download or read book Biologically Inspired Control of Humanoid Robot Arms written by Adam Spiers and published by Springer. This book was released on 2016-05-19 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 techniques investigated in this book. The method includes attractive features such as the decoupling of motion into task and posture components. Various developments are made in each of these elements. Simple cost functions inspired by biomechanical “effort” and “discomfort” generate realistic posture motion. Sliding-mode techniques overcome robustness shortcomings for practical implementation. Arm compliance is achieved via a method of model-free adaptive control that also deals with actuator saturation via anti-windup compensation. A neural-network-centered learning-by-observation scheme generates new task motions, based on motion-capture data recorded from human volunteers. In other parts of the book, motion capture is used to test theories of human movement. All developed controllers are applied to the reaching motion of a humanoid robot arm and are demonstrated to be practically realisable. This book is designed to be of interest to those wishing to achieve dynamics-based human-like robot-arm motion in academic research, advanced study or certain industrial environments. The book provides motivations, extensive reviews, research results and detailed explanations. It is not only suited to practising control engineers, but also applicable for general roboticists who wish to develop control systems expertise in this area.

Neural Systems for Robotics

Author : Omid Omidvar
Publisher : Elsevier
ISBN 13 : 008092509X
Total Pages : 346 pages
Book Rating : 4.0/5 (89 download)

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Book Synopsis Neural Systems for Robotics by : Omid Omidvar

Download or read book Neural Systems for Robotics written by Omid Omidvar and published by Elsevier. This book was released on 2012-12-02 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Systems for Robotics represents the most up-to-date developments in the rapidly growing aplication area of neural networks, which is one of the hottest application areas for neural networks technology. The book not only contains a comprehensive study of neurocontrollers in complex Robotics systems, written by highly respected researchers in the field but outlines a novel approach to solving Robotics problems. The importance of neural networks in all aspects of Robot arm manipulators, neurocontrol, and Robotic systems is also given thorough and in-depth coverage. All researchers and students dealing with Robotics will find Neural Systems for Robotics of immense interest and assistance. Key Features * Focuses on the use of neural networks in robotics-one of the hottest application areas for neural networks technology * Represents the most up-to-date developments in this rapidly growing application area of neural networks * Contains a new and novel approach to solving Robotics problems

Inverse Kinematics Problem in Robotics Using Neural Networks

Author : Benjamin B. Choi
Publisher :
ISBN 13 :
Total Pages : 28 pages
Book Rating : 4.:/5 (317 download)

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Book Synopsis Inverse Kinematics Problem in Robotics Using Neural Networks by : Benjamin B. Choi

Download or read book Inverse Kinematics Problem in Robotics Using Neural Networks written by Benjamin B. Choi and published by . This book was released on 1992 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Bioinspired Design and Control of Robots with Intrinsic Compliance

Author : Yongping Pan
Publisher : Frontiers Media SA
ISBN 13 : 2889661571
Total Pages : 132 pages
Book Rating : 4.8/5 (896 download)

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Book Synopsis Bioinspired Design and Control of Robots with Intrinsic Compliance by : Yongping Pan

Download or read book Bioinspired Design and Control of Robots with Intrinsic Compliance written by Yongping Pan and published by Frontiers Media SA. This book was released on 2020-12-04 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

Deep Reinforcement Learning with Guaranteed Performance

Author : Yinyan Zhang
Publisher : Springer Nature
ISBN 13 : 3030333841
Total Pages : 225 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Deep Reinforcement Learning with Guaranteed Performance by : Yinyan Zhang

Download or read book Deep Reinforcement Learning with Guaranteed Performance written by Yinyan Zhang and published by Springer Nature. This book was released on 2019-11-09 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances. It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution. Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.

Competition-Based Neural Networks with Robotic Applications

Author : Shuai Li
Publisher : Springer
ISBN 13 : 9811049475
Total Pages : 121 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Competition-Based Neural Networks with Robotic Applications by : Shuai Li

Download or read book Competition-Based Neural Networks with Robotic Applications written by Shuai Li and published by Springer. This book was released on 2017-05-30 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focused on solving competition-based problems, this book designs, proposes, develops, analyzes and simulates various neural network models depicted in centralized and distributed manners. Specifically, it defines four different classes of centralized models for investigating the resultant competition in a group of multiple agents. With regard to distributed competition with limited communication among agents, the book presents the first distributed WTA (Winners Take All) protocol, which it subsequently extends to the distributed coordination control of multiple robots. Illustrations, tables, and various simulative examples, as well as a healthy mix of plain and professional language, are used to explain the concepts and complex principles involved. Thus, the book provides readers in neurocomputing and robotics with a deeper understanding of the neural network approach to competition-based problem-solving, offers them an accessible introduction to modeling technology and the distributed coordination control of redundant robots, and equips them to use these technologies and approaches to solve concrete scientific and engineering problems.

Intelligent Control of Robotic Systems

Author : Laxmidhar Behera
Publisher : CRC Press
ISBN 13 : 0429944004
Total Pages : 499 pages
Book Rating : 4.4/5 (299 download)

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Book Synopsis Intelligent Control of Robotic Systems by : Laxmidhar Behera

Download or read book Intelligent Control of Robotic Systems written by Laxmidhar Behera and published by CRC Press. This book was released on 2020-04-07 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book illustrates basic principles, along with the development of the advanced algorithms, to realize smart robotic systems. It speaks to strategies by which a robot (manipulators, mobile robot, quadrotor) can learn its own kinematics and dynamics from data. In this context, two major issues have been dealt with; namely, stability of the systems and experimental validations. Learning algorithms and techniques as covered in this book easily extend to other robotic systems as well. The book contains MATLAB- based examples and c-codes under robot operating systems (ROS) for experimental validation so that readers can replicate these algorithms in robotics platforms.

Human-Robot Interaction Control Using Reinforcement Learning

Author : Wen Yu
Publisher : John Wiley & Sons
ISBN 13 : 1119782740
Total Pages : 290 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Human-Robot Interaction Control Using Reinforcement Learning by : Wen Yu

Download or read book Human-Robot Interaction Control Using Reinforcement Learning written by Wen Yu and published by John Wiley & Sons. This book was released on 2021-10-19 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning.

Control of Redundant Robot Manipulators

Author : Rajni V. Patel
Publisher : Springer Science & Business Media
ISBN 13 : 9783540250715
Total Pages : 228 pages
Book Rating : 4.2/5 (57 download)

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Book Synopsis Control of Redundant Robot Manipulators by : Rajni V. Patel

Download or read book Control of Redundant Robot Manipulators written by Rajni V. Patel and published by Springer Science & Business Media. This book was released on 2005-05-04 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides a comprehensive and thorough treatment of the problem of controlling a redundant robot manipulator. It presents the latest research from the field with a good balance between theory and practice. All theoretical developments are verified both via simulation and experimental work on an actual prototype redundant robot manipulator. This book is the first text aimed at graduate students and researchers working in the area of redundant manipulators giving a comprehensive coverage of control of redundant robot manipulators from the viewpoint of theory and experimentation.

Sensor-Based Robots: Algorithms and Architectures

Author : C.S.George Lee
Publisher : Springer Science & Business Media
ISBN 13 : 3642755305
Total Pages : 295 pages
Book Rating : 4.6/5 (427 download)

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Book Synopsis Sensor-Based Robots: Algorithms and Architectures by : C.S.George Lee

Download or read book Sensor-Based Robots: Algorithms and Architectures written by C.S.George Lee and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most industrial robots today have little or no sensory capability. Feedback is limited to information about joint positions, combined with a few interlock and timing signals. These robots can function only in an environment where the objects to be manipulated are precisely located in the proper position for the robot to grasp (i. e. , in a structured environment). For many present industrial applications, this level of performance has been adequate. With the increasing demand for high performance sensor-based robot manipulators in assembly tasks, meeting this demand and challenge can only be achieved through the consideration of: 1) efficient acquisition and processing of intemaVextemal sensory information, 2) utilization and integration of sensory information from various sensors (tactile, force, and vision) to acquire knowledge in a changing environment, 3) exploitation of inherent robotic parallel algorithms and efficient VLSI architectures for robotic computations, and finally 4) system integration into a working and functioning robotic system. This is the intent of the Workshop on Sensor-Based Robots: Algorithms and Architectures - to study the fundamental research issues and problems associated with sensor-based robot manipulators and to propose approaches and solutions from various viewpoints in improving present day robot manipula tors in the areas of sensor fusion and integration, sensory information processing, and parallel algorithms and architectures for robotic computations.

Robot Control and Calibration

Author : Xin Luo
Publisher : Springer Nature
ISBN 13 : 9819957664
Total Pages : 132 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Robot Control and Calibration by : Xin Luo

Download or read book Robot Control and Calibration written by Xin Luo and published by Springer Nature. This book was released on 2023-09-25 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book mainly shows readers how to calibrate and control robots. In this regard, it proposes three control schemes: an error-summation enhanced Newton algorithm for model predictive control; RNN for solving perturbed time-varying underdetermined linear systems; and a new joint-drift-free scheme aided with projected ZNN, which can effectively improve robot control accuracy. Moreover, the book develops four advanced algorithms for robot calibration – Levenberg-Marquarelt with diversified regularizations; improved covariance matrix adaptive evolution strategy; quadratic interpolated beetle antennae search algorithm; and a novel variable step-size Levenberg-Marquardt algorithm – which can effectively enhance robot positioning accuracy. In addition, it is exceedingly difficult for experts in other fields to conduct robot arm calibration studies without calibration data. Thus, this book provides a publicly available dataset to assist researchers from other fields in conducting calibration experiments and validating their ideas. The book also discusses six regularization schemes based on its robot error models, i.e., L1, L2, dropout, elastic, log, and swish. Robots’ positioning accuracy is significantly improved after calibration. Using the control and calibration methods developed here, readers will be ready to conduct their own research and experiments.

Artificial Neural Networks and Machine Learning – ICANN 2018

Author : Věra Kůrková
Publisher : Springer
ISBN 13 : 303001424X
Total Pages : 866 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Artificial Neural Networks and Machine Learning – ICANN 2018 by : Věra Kůrková

Download or read book Artificial Neural Networks and Machine Learning – ICANN 2018 written by Věra Kůrková and published by Springer. This book was released on 2018-10-02 with total page 866 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning.