AI based Robot Safe Learning and Control

Zhou, Xuefeng; Xu, Zhihao; Li, Shuai; Wu, Hongmin; Cheng, Taobo; Lv, Xiaojing

doi:10.1007/978-981-15-5503-9

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AI based Robot Safe Learning and Control

Xuefeng Zhou, Zhihao Xu, Shuai Li

, Hongmin Wu, Taobo Cheng, Xiaojing Lv

Swansea University Author: Shuai Li

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DOI (Published version): 10.1007/978-981-15-5503-9

Abstract

Introduction 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,...

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ISBN:	9789811555022 9789811555039
Published:	Singapore Springer Singapore 2020
Online Access:	http://dx.doi.org/10.1007/978-981-15-5503-9
URI:	https://cronfa.swan.ac.uk/Record/cronfa55087
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first_indexed	2020-08-27T10:49:07Z
last_indexed	2022-06-15T03:11:15Z
id	cronfa55087
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spelling	2022-06-14T15:02:04.0466632 v2 55087 2020-08-27 AI based Robot Safe Learning and Control 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-08-27 MECH Introduction 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. Book Springer Singapore Singapore 9789811555022 9789811555039 Safe Control, Deep Reinforcement Learning, Recurrent Neural Network, Force Contro, lObstacle Ovoidance, Adaptive Control, Trajectory Tracking, Open Access 1 1 2020 2020-01-01 10.1007/978-981-15-5503-9 http://dx.doi.org/10.1007/978-981-15-5503-9 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2022-06-14T15:02:04.0466632 2020-08-27T11:46:39.4927829 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Xuefeng Zhou 1 Zhihao Xu 2 Shuai Li 0000-0001-8316-5289 3 Hongmin Wu 4 Taobo Cheng 5 Xiaojing Lv 6 55087__18058__48d14c8cea0c4e229f434f2d561d00a1.pdf 55087.pdf 2020-08-27T11:48:20.4958828 Output 9581741 application/pdf Version of Record true Distributed under the terms of a Creative Commons Attribution 4.0 (CC-BY) Licence. Copyright: The Editor(s) (if applicable) and The Author(s) 2020 true eng https://creativecommons.org/licenses/by/4.0
title	AI based Robot Safe Learning and Control
spellingShingle	AI based Robot Safe Learning and Control Shuai Li
title_short	AI based Robot Safe Learning and Control
title_full	AI based Robot Safe Learning and Control
title_fullStr	AI based Robot Safe Learning and Control
title_full_unstemmed	AI based Robot Safe Learning and Control
title_sort	AI based Robot Safe Learning and Control
author_id_str_mv	42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv	42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author	Shuai Li
author2	Xuefeng Zhou Zhihao Xu Shuai Li Hongmin Wu Taobo Cheng Xiaojing Lv
format	Book
publishDate	2020
institution	Swansea University
isbn	9789811555022 9789811555039
doi_str_mv	10.1007/978-981-15-5503-9
publisher	Springer Singapore
college_str	Faculty of Science and Engineering
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hierarchy_top_id	facultyofscienceandengineering
hierarchy_top_title	Faculty of Science and Engineering
hierarchy_parent_id	facultyofscienceandengineering
hierarchy_parent_title	Faculty of Science and Engineering
department_str	School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url	http://dx.doi.org/10.1007/978-981-15-5503-9
document_store_str	1
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description	Introduction 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.
published_date	2020-01-01T04:09:03Z
_version_	1763753636689608704
score	10.999524

AI based Robot Safe Learning and Control

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