Repetitive motion planning of redundant robots based on LVI-based primal-dual neural network and PUMA560 example

Authors:
Yunong Zhang;Xuanjiao Lv;Zhonghua Li;Zhi Yang
Affiliations:
Department of Electronics and Communication Engineering, Sun Yat-Sen University, Guangzhou, China;Department of Electronics and Communication Engineering, Sun Yat-Sen University, Guangzhou, China;Department of Electronics and Communication Engineering, Sun Yat-Sen University, Guangzhou, China;Department of Electronics and Communication Engineering, Sun Yat-Sen University, Guangzhou, China
Venue:
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Year:
2007

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Abstract

A primal-dual neural network based on linear variational inequalities (LVI) is presented in this paper, which is used to solve the repetitive motion planning of redundant robots. To do so, a drift-free criterion is exploited. In addition, the physical constraints such as joint limits and joint velocity limits are incorporated into the problem formulation of such a scheme. The scheme is finally reformulated as a quadratic programming (QP) problem and resolved at the velocity-level. Compared to other computational strategies on inverse kinematics, the LVI-based primal-dual neural network is designed based on the QP-LVI conversion and Karush-Kuhn-Tucker (KKT) conditions. With simple piecewise-linear dynamics and global (exponential) convergence to optimal solutions, it can handle general QP and linear programming (LP) problems in the same inverse-free manner. The repetitive motion planning scheme and the LVI-based primal-dual neural network are simulated based on PUMA560 robot manipulator with effectiveness demonstrated.