Human-in-the-loop Learning and Control for Robot Teleoperation - Tapa blanda

Acerca de los autores

Dr. Chenguang Yang is a Professor of Robotics with University of the West of England, and leader of Robot Teleoperation Group at the Bristol Robotics Laboratory. He received his Ph.D. degree in control engineering from the National University of Singapore in 2010, and postdoctoral training in human robotics from Imperial College London, U.K. His research interests lie in human–robot interaction and intelligent system design. Dr. Yang was awarded the EU Marie Curie International Incoming Fellowship, the U.K. EPSRC UKRI Innovation Fellowship, and the Best Paper Award of IEEE TRANSACTIONS ON ROBOTICS as well as over ten international conference best paper awards. He is a Co-Chair of the Technical Committee on Bio-Mechatronics and Bio-Robotics Systems, IEEE Systems, Man, and Cybernetics Society; and a Co-Chair of the Technical Committee on Collaborative Automation for Flexible Manufacturing, IEEE Robotics and Automation Society. He serves as an Associate Editor of a number of IEEE Transactions and other international leading journals.

Dr. Jing Luo is currently an Associate Professor with Wuhan Institute of Technology. He received his Ph.D. degree in control science and engineering from the South China University of Technology in 2020. His research interests include robots control, teleoperation and human-robot interaction.

Dr. Ning Wang is a Senior Lecturer of Robotics with the Bristol Robotics Laboratory, University of the West of England, United Kingdom. She received her M.Phil. and Ph.D. degrees in electronics engineering from the Department of Electronics Engineering, The Chinese University of Hong Kong, Hong Kong, in 2007 and 2011, respectively. Ning has rich project experience, she has been key member of EU FP7 Project ROBOT-ERA, EU Regional Development Funded Project ASTUTE 2020 and industrial projects with UK companies.

De la contraportada

Robots have been used in various industrial processes to reduce labor costs and improve work efficiency. However, most robots are only designed to work on repetitive and fixed tasks, leaving a gap with the human desired manufacturing effect. Situations become serious when robots handle complicated tasks in uncontrollable environments due to weak intelligence, inadequate sensing, controlling abilities, and unfit mechanical design. Considering imperfect performance of robotic automation, researchers chose teleoperation technology to add human intelligence to robot manipulation, enabling robots to adapt to complex situations and avoid endangering human lives.

Human-in-the-loop Learning and Control for Robot Teleoperation presents recent research progress on teleoperation and robots, including human-robot interaction, learning, and control for teleoperation with many extensions on intelligent learning techniques. This book integrates cutting-edge research on learning and control algorithms of robot teleoperation, neural motor learning control, wave variable enhancement, EMG-based teleoperation control, and other key aspects related to robot technology, presenting in detail the implementation of these control and learning algorithms with adequate application examples and illustrative interpretations.