Repetitive Motion Planning of Kinematically Redundant Manipulators Using LVI-based Primal-Dual Neural Network

Abstract

In this paper, a primal-dual neural network based on linear variational inequalities (LVI) is presented for online repetitive motion planning of kinematically redundant manipulators. To do this, 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. As a QP real-time solver, 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 PA 10 robot manipulator with effectiveness demonstrated.