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

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.

I. Introduction

Recently, robot manipulators and wheeled mobile robot manipulators are more and more applied in a variety of complex environments, such as family service [1]–[3], spray painting [4], welding [5]–[7], rescue after disaster [8], and so on [9]. The aforementioned situations require the robot manipulators or the wheeled mobile robot manipulators with flexible configuration abilities and optional optimization properties. Redundant robot manipulators are preferred because they are more flexible due to their redundant degrees-of-freedoms (DOFs). With the redundant DOFs, the robot manipulators can be applied to finishing subtasks when executing the main end-effector task.