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 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 undergraduateand graduate students in colleges and universities.

Is the first book on the safe control of robotic systems based on dynamic neural networks Presents a general theoretical framework for robot systems with redundant DOFs, which is capable of enhancing safety and robustness, and optimizing flexibility in uncertain dynamic environments Provides examples of typical simulations and experiments for robot systems in situations such as motion planning and force control, which readers can easily implement Is an open access book

Autorentext

Dr. Xuefeng Zhou is an Associate Professor and Leader of the Robotics Team at Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Science. He received his Ph.D. degree in Manufacturing and Automation from South China University of Technology in 2011. His research mainly focuses on motion planning and control, force control, and legged robots. He has published more than 40 journal articles and conference papers.

Dr. Zhihao Xu is a Researcher at Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Science. He received his Ph.D. degree in Control Science and Engineering from Nanjing University of Science and Technology, China, in 2016. His research mainly focuses on intelligent control theory, motion planning and control and force control. He has published more than 30 journal articles and conference papers.

Prof. Shuai Li is a Ph.D. Supervisor and Associate Professor (Reader) at the College of Engineering, Swansea University, UK. He received his Ph.D. degree in Electrical and Computer Engineering from Stevens Institute of Technology, New Jersey, USA, in 2014. His research interests are robot manipulation, automation and instrumentation, artificial intelligence and industrial robots. He has published over 80 papers in journals such as IEEE TAC, TII, TCYB, TIE and TNNLS. He serves as Editor-in-Chief of the International Journal of Robotics and Control and was the General Co-Chair of the 2018 International Conference on Advanced Robotics and Intelligent Control.

Dr. Hongmin Wu is a Researcher at Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Science. He received his Ph.D. degree in Mechanical Engineering from Guangdong University of Technology, Guangzhou, China, in 2019. His research mainly focuses on robot learning, autonomous manipulation, deep learning and human­-robot collaboration. He has published more than 20 journal articles and conference papers.

Dr. Taobo Cheng received the Ph.D. degree in Welding Engineering, South China University of Technology, Guangzhou, China, in 1998. He is currently the director of Guangdong Institute of Intelligent Manufacturing. His current research interests include intelligent manufacturing technology, automation and information technology.

Dr. Xiaojing Lv is a Researcher at the School of Aircraft Maintenance Engineering, Guangzhou Civil Aviation College. She received her Ph.D. degree in Engineering Mechanics from Nanjing University of Science and Technology, China, in 2016. Her research mainly focuses on fault diagnosis and engineering mechanics.

Inhalt
Adaptive Jacobian based Trajectory Tracking for Redundant Manipulators with Model Uncertainties in Repetitive Tasks.- RNN based Trajectory Control for Manipulators with Uncertain Kinematic Parameters.- RNN Based Adaptive Compliance Control for Robots with Model Uncertainties.- Deep RNN based Obstacle Avoidance Control for Redundant Manipulators