Bicriteria Velocity Minimization Approach of Self-Motion for Redundant Robot Manipulators With Varying-Gain Recurrent Neural Network

Abstract

In this article, a varying-gain neural bicriterion velocity minimization self-motion (VGN-BCVM-SM) approach is proposed to solve the self-motion problem for a redundant robot manipulator. First, based on quadratic programming (QP) method and neural dynamic method, the proposed approach is derived in detail. For comparisons, a traditional fixed-parameter neural bicriterion velocity minimization self-motion (FPN-BCVM-SM) approach is also presented. Then, the convergence and robustness of the proposed method is analyzed theoretically. Theoretical analysis shows that the proposed approach has global convergence and can overcome the errors of kinematics measurements. Computer simulations based on a six degrees-of-freedom manipulator demonstrate that the proposed approach can effectively avoid the robot manipulator exceeding the physical limits of joints. Meanwhile, the proposed VGN-BCVM-SM has higher efficiency and accuracy than fixed-parameter approach for solving self-motion problem of a redundant robot manipulator.