Two Hybrid Multiobjective Motion Planning Schemes Synthesized by Recurrent Neural Networks for Wheeled Mobile Robot Manipulators

Abstract

To make manipulators fulfill end-effector maintaining tasks, such as writing or drawing tasks in a complex environment, two hybrid multiobjective motion planing schemes, i.e., end-effector posture-maintaining and obstacle avoidance (hybrid PM-OA) schemes are proposed and investigated for wheeled mobile redundant robot manipulators. Specifically, the end-effector posture maintaining, obstacle avoidance, and joint physical limits are considered in a quadratic programming (QP) problem. With these two hybrid PM-OA schemes, the wheeled mobile robot manipulators can maintain its end-effector posture, avoid the obstacle and joint physical limits during executing end-effector tasks. The hybrid PM-OA schemes are finally formulated into a piecewise-linear projection equations (PLPEs) and solved by a recurrent neural network (RNN). Computer simulations are given to substantiate the effectiveness, accuracy, safety, and practicability of the proposed hybrid PM-OA schemes. Comparisons with other schemes and simulations further show that the proposed hybrid PM-OA schemes are more suitable for applications.