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Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks

文献类型：期刊论文

作者Jin, Long1; Li, Shuai1; Hung Manh La2; Luo, Xin3
刊名IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
出版日期2017-06-01
卷号64期号:6页码:4710-4720
关键词Dynamic neural network kinematic control manipulability optimization redundancy resolution
ISSN号0278-0046
DOI10.1109/TIE.2017.2674624
通讯作者Li, S (reprint author), Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China. ; Luo, X (reprint author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China.
英文摘要For solving the singularity problem arising in the control of manipulators, an efficient way is to maximize itsmanipulability. However, it is challenging to optimize manipulability effectively because it is a nonconvex function to the joint angles of a robotic arm. In addition, the involvement of an inversion operation in the expression of manipulability makes it even hard for timely optimization due to the intensively computational burden for matrix inversion. In this paper, we make progress on real-time manipulability optimization by establishing a dynamic neural network for recurrent calculation of manipulability-maximal control actions for redundant manipulators under physical constraints in an inverse-free manner. By expressing position tracking and matrix inversion as equality constraints, physical limits as inequality constraints, and velocity-level manipulability measure, which is affine to the joint velocities, as the objective function, the manipulability optimization scheme is further formulated as a constrained quadratic program. Then, a dynamic neural network with rigorously provable convergence is constructed to solve such a problem online. Computer simulations are conducted and show that, compared to the existing methods, the proposed scheme can raise the manipulability almost 40% on average, which substantiates the efficacy, accuracy, and superiority of the proposed manipulability optimization scheme.
资助项目National Natural Science Foundation of China[61401385] ; Hong Kong Research Grants Council Early Career Scheme[25214015] ; Departmental General Research Fund of Hong Kong Polytechnic University[G.61.37.UA7L] ; Pioneer Hundred Talents Program of the Chinese Academy of Sciences ; International Joint Project - Royal Society of the U.K.[61611130209] ; International Joint Project - National Natural Science Foundation of China[61611130209]
WOS研究方向Automation & Control Systems ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:000401328500038
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://172.16.51.4:88/handle/2HOD01W0/220]  
专题大数据挖掘及应用中心
通讯作者Li, Shuai; Luo, Xin
作者单位1.Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China
2.Univ Nevada, Dept Comp Sci & Engn, Reno, NV 89557 USA
3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
推荐引用方式
GB/T 7714		 Jin, Long,Li, Shuai,Hung Manh La,et al. Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2017,64(6):4710-4720.
APA Jin, Long,Li, Shuai,Hung Manh La,&Luo, Xin.(2017).Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,64(6),4710-4720.
MLA Jin, Long,et al."Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 64.6(2017):4710-4720.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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