[PDF] Learning For Adaptive And Reactive Robot Control eBook

Author : Aude Billard
Publisher : MIT Press
Page : 425 pages
File Size : 13,72 MB
Release : 2022-02-08
Category : Technology & Engineering
ISBN : 0262367017

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Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.

Author : Xuefeng Zhou
Publisher : Springer Nature
Page : 138 pages
File Size : 18,29 MB
Release : 2020-06-02
Category : Technology & Engineering
ISBN : 9811555036

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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.

Author : Dan Zhang
Publisher : CRC Press
Page : 407 pages
File Size : 40,89 MB
Release : 2017-02-03
Category : Science
ISBN : 1351678922

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The robotic mechanism and its controller make a complete system. As the robotic mechanism is reconfigured, the control system has to be adapted accordingly. The need for the reconfiguration usually arises from the changing functional requirements. This book will focus on the adaptive control of robotic manipulators to address the changed conditions. The aim of the book is to summarise and introduce the state-of-the-art technologies in the field of adaptive control of robotic manipulators in order to improve the methodologies on the adaptive control of robotic manipulators. Advances made in the past decades are described in the book, including adaptive control theories and design, and application of adaptive control to robotic manipulators.

Author : Sadao Kawamura
Publisher : Springer Science & Business Media
Page : 360 pages
File Size : 50,94 MB
Release : 2007-07-17
Category : Technology & Engineering
ISBN : 3540373470

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This volume surveys three decades of modern robot control theory and describes how the work of Suguru Arimoto shaped its development. Twelve survey articles written by experts associated with Suguru Arimoto at various stages in his career treat the subject comprehensively. This book provides an important reference for graduate students and researchers, as well as for mathematicians, engineers and scientists whose work involves robot control theory.

Author : Nabil Derbel
Publisher : Springer
Page : 359 pages
File Size : 16,83 MB
Release : 2019-01-24
Category : Technology & Engineering
ISBN : 9811322120

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This book highlights relevant studies and applications in the area of robotics, which reflect the latest research, from interdisciplinary theoretical studies and computational algorithm development, to representative applications. It presents chapters on advanced control, such as fuzzy, neural, backstepping, sliding mode, adaptive, predictive, diagnosis and fault tolerant control etc. and addresses topics including cloud robotics, cable-driven robots, two-wheeled robots, mobile robots, swarm robots, hybrid vehicle, and drones. Each chapter employs a uniform structure: background, motivation, quantitative development (equations), case studies/illustration/tutorial (simulations, experiences, curves, tables, etc.), allowing readers to easily tailor the techniques to their own applications.

Author : M. Vukobratovic
Publisher : Springer Science & Business Media
Page : 394 pages
File Size : 49,1 MB
Release : 2013-12-11
Category : Computers
ISBN : 3642822010

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The material presented in this monograph is a logical continuation of research results achieved in the control of manipulation robots. This is in a way, a synthesis of many-year research efforts of the associates of Robotics Department, Mihailo Pupin Institute, in the field of dynamic control.of robotic systems. As in Vol. 2 of this Series, all results rely on the mathematical models of dynamics of active spatial mechanisms which offer the possibility for adequate dynamic control of manipula tion robots. Compared with Vol. 2, this monograph has three essential new character istics, and a variety of new tasks arising in the control of robots which have been formulated and solved for the first time. One of these novelties is nonadaptive control synthesized for the case of large variations in payload parameters, under the condition that the practical stability of the overall system is satisfied. Such a case of control synthesis meets the actual today's needs in industrial robot applications. The second characteristic of the monograph is the efficient adaptive control algorithm based on decentralized control structure intended for tasks in which parameter variations cannot be specified in advance. To be objective, this is not the case in industrial robotics today. Thus, nonadaptive control with and without a particular parameter variation is supplemented by adaptive dynamic control algorithms which will cer tainly be applicable in the future industrial practice when parametric identification of workpieces will be required.

Author : Dan Zhang
Publisher : Elsevier
Page : 282 pages
File Size : 17,10 MB
Release : 2020-12-05
Category : Technology & Engineering
ISBN : 0128223154

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Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge technologies in the field of learning-based control. State-of-the-art techniques involving machine learning and artificial intelligence (AI) are covered, as are foundational control theories and more established techniques such as adaptive learning control, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications in robotics, AI, aircraft and other vehicles and complex dynamical systems. Computational methods for control systems, particularly those used for developing AI and other machine learning techniques, are also discussed at length. Provides foundational control theory concepts, along with advanced techniques and the latest advances in adaptive control and robotics Introduces state-of-the-art learning-based control technologies and their applications in robotics and other complex dynamical systems Demonstrates computational techniques for control systems Covers iterative learning impedance control in both human-robot interaction and collaborative robots

Author : Carlos Canudas de Wit
Publisher : Springer
Page : 319 pages
File Size : 41,25 MB
Release : 1991-08-07
Category : Technology & Engineering
ISBN : 9783540541691

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Research in the area of adaptive control, nonlinear system and other advanced control techniques have been carried out in parallel and rather independently. In the last few years, these techniques have been used to improve robot motion accuracy. The aim of the workshop is to present the most recent contributions in the field of robot control and to compare how these advanced control techniques have been used to solve similar problems. The topics covered include: Adaptation and learning.- Control of systems with nonholonomic constraints (mobile robots).- Robot control in the task space.- Control of flexible robots (joints and structure).- Observer-based control.- Control through kinematic singularities.

Author : Pablo Borja
Publisher : Springer Nature
Page : 262 pages
File Size : 48,82 MB
Release : 2023-01-01
Category : Technology & Engineering
ISBN : 303122731X

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This book contains seventeen contributions in the form of independent chapters, covering a broad range of topics related to human–robot interaction at physical and cognitive levels. Each chapter represents a novel piece of work presented during HFR 2022 by researchers in the different areas of robotics, where new theories, methodologies, technologies, challenges, and empirical and experimental studies are discussed. Additionally, this compilation is rich in viewpoints due to the multidisciplinary nature of its authors. Hence, this book represents an excellent opportunity for academics, researchers, and industry partners to get acquainted with the most recent work on human–robot interaction.