Robot manipulator control using neural networks: A survey
Robot manipulators are playing increasingly significant roles in scientific researches and engineering applications in recent years. Using manipulators to save labors and increase accuracies are becoming common practices in industry. Neural networks, which feature high-speed parallel distributed pro...
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Published in | Neurocomputing (Amsterdam) Vol. 285; pp. 23 - 34 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
12.04.2018
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Abstract | Robot manipulators are playing increasingly significant roles in scientific researches and engineering applications in recent years. Using manipulators to save labors and increase accuracies are becoming common practices in industry. Neural networks, which feature high-speed parallel distributed processing, and can be readily implemented by hardware, have been recognized as a powerful tool for real-time processing and successfully applied widely in various control systems. Particularly, using neural networks for the control of robot manipulators have attracted much attention and various related schemes and methods have been proposed and investigated. In this paper, we make a review of research progress about controlling manipulators by means of neural networks. The problem foundation of manipulator control and the theoretical ideas on using neural network to solve this problem are first analyzed and then the latest progresses on this topic in recent years are described and reviewed in detail. Finally, toward practical applications, some potential directions possibly deserving investigation in controlling manipulators by neural networks are pointed out and discussed. |
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AbstractList | Robot manipulators are playing increasingly significant roles in scientific researches and engineering applications in recent years. Using manipulators to save labors and increase accuracies are becoming common practices in industry. Neural networks, which feature high-speed parallel distributed processing, and can be readily implemented by hardware, have been recognized as a powerful tool for real-time processing and successfully applied widely in various control systems. Particularly, using neural networks for the control of robot manipulators have attracted much attention and various related schemes and methods have been proposed and investigated. In this paper, we make a review of research progress about controlling manipulators by means of neural networks. The problem foundation of manipulator control and the theoretical ideas on using neural network to solve this problem are first analyzed and then the latest progresses on this topic in recent years are described and reviewed in detail. Finally, toward practical applications, some potential directions possibly deserving investigation in controlling manipulators by neural networks are pointed out and discussed. |
Author | Jin, Long Li, Shuai He, Jinbo Yu, Jiguo |
Author_xml | – sequence: 1 givenname: Long surname: Jin fullname: Jin, Long email: jinlong@lzu.edu.cn organization: School of Information Science and Engineering, Lanzhou University, Lanzhou, China – sequence: 2 givenname: Shuai orcidid: 0000-0001-8316-5289 surname: Li fullname: Li, Shuai email: shuaili@polyu.edu.hk organization: Department of Computing, The Hong Kong Polytechnic University, Hong Kong – sequence: 3 givenname: Jiguo surname: Yu fullname: Yu, Jiguo email: jiguoyu@sina.com organization: School of Information Science and Engineering, Qufu Normal University, Rizhao, China – sequence: 4 givenname: Jinbo surname: He fullname: He, Jinbo email: adriano.he@connect.polyu.edu.hk organization: Department of Computing, The Hong Kong Polytechnic University, Hong Kong |
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Keywords | Robot arms Dual neural network Recurrent neural network Parallel manipulators Robot manipulators Winner-take-all Neural network Cable-driven manipulators Feedforward neural network |
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Snippet | Robot manipulators are playing increasingly significant roles in scientific researches and engineering applications in recent years. Using manipulators to save... |
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SubjectTerms | Cable-driven manipulators Dual neural network Feedforward neural network Neural network Parallel manipulators Recurrent neural network Robot arms Robot manipulators Winner-take-all |
Title | Robot manipulator control using neural networks: A survey |
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