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Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks
Jin, Long1; Li, Shuai1; Hung Manh La2; Luo, Xin3
2017-06-01
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.
关键词Dynamic neural network kinematic control manipulability optimization redundancy resolution
DOI10.1109/TIE.2017.2674624
发表期刊IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
ISSN0278-0046
卷号64期号:6页码:4710-4720
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.
收录类别SCI
WOS记录号WOS:000401328500038
语种英语