Repetitive Motion Planning of Redundant Robots Based on LVI-Based Primal-Dual Neural Network and PUMA560 Example

摘要

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.