Repetitive motion planning of PA10 robot arm subject to joint physical limits and using LVI-based primal–dual neural network

In this paper, a primal–dual neural network based on linear variational inequalities (LVI) is presented for the online repetitive motion planning of PA10 robot arm, a kinematically redundant manipulator. 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 redundancy-resolution 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) condition. 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 successfully based on PA10 robot arm, with effectiveness demonstrated.