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oapen-20.500.12657-39583
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oapen-20.500.12657-395832020-06-16T00:45:31Z AI based Robot Safe Learning and Control Zhou, Xuefeng Xu, Zhihao Li, Shuai Wu, Hongmin Cheng, Taobo Lv, Xiaojing Robotics and Automation Control and Systems Theory Artificial Intelligence Robotic Engineering Safe Control Deep Reinforcement Learning Recurrent Neural Network Force Control Obstacle Ovoidance Adaptive Control Trajectory Tracking Open Access Robotics Automatic control engineering Artificial intelligence bic Book Industry Communication::T Technology, engineering, agriculture::TJ Electronics & communications engineering::TJF Electronics engineering::TJFM Automatic control engineering::TJFM1 Robotics bic Book Industry Communication::T Technology, engineering, agriculture::TJ Electronics & communications engineering::TJF Electronics engineering::TJFM Automatic control engineering bic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence 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. 2020-06-15T15:10:50Z 2020-06-15T15:10:50Z 2020 book ONIX_20200615_9789811555039_56 http://library.oapen.org/handle/20.500.12657/39583 eng application/pdf n/a 2020_Book_AIBasedRobotSafeLearningAndCon.pdf https://www.springer.com/9789811555039 Springer Nature Springer 10.1007/978-981-15-5503-9 10.1007/978-981-15-5503-9 6c6992af-b843-4f46-859c-f6e9998e40d5 Springer 127 Singapore open access
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OAPEN
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DSpace
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| language |
English
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| description |
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.
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| title |
2020_Book_AIBasedRobotSafeLearningAndCon.pdf
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| spellingShingle |
2020_Book_AIBasedRobotSafeLearningAndCon.pdf
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| title_short |
2020_Book_AIBasedRobotSafeLearningAndCon.pdf
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| title_full |
2020_Book_AIBasedRobotSafeLearningAndCon.pdf
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| title_fullStr |
2020_Book_AIBasedRobotSafeLearningAndCon.pdf
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| title_full_unstemmed |
2020_Book_AIBasedRobotSafeLearningAndCon.pdf
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| title_sort |
2020_book_aibasedrobotsafelearningandcon.pdf
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| publisher |
Springer Nature
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| publishDate |
2020
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| url |
https://www.springer.com/9789811555039
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| _version_ |
1771297496658608128
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