AI based Robot Safe Learning and Control

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...

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Bibliographic Details
Main Author: Zhou, Xuefeng
Other Authors: Xu, Zhihao, Li, Shuai, Wu, Hongmin
Format: eBook
Language:English
Published: Singapore Springer Nature 2020
Subjects:
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary: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.
Item Description:Creative Commons (cc), by/4.0/, http://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (127 p.)
ISBN:978-981-15-5503-9