Electronic Resource

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.

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Detail Information

Series Title

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Call Number

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Publisher

: Springer Nature Singapore., 2020

Collation

XVII, 127 hlm; ill., lamp.,

Language

English

ISBN/ISSN

9789811555039

Classification

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Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

1

Subject(s)

Artificial Intelligence
Control and Systems Theory
Robotics and Automation

Specific Detail Info

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

Statement of Responsibility

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

Other Information

Cataloger

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Source

https://link.springer.com/book/10.1007/978-981-15-5503-9

Validator

ida

Digital Object Identifier (DOI)

https://doi.org/10.1007/978-981-15-5503-9

Journal Volume

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Journal Issue

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Subtitle

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Parallel Title

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