Motion Planning of Manipulators for Simultaneous Obstacle Avoidance and Target Tracking: An RNN Approach With Guaranteed Performance

Abstract

Motion planning is a core issuein the field of robotic control, which directly affects the programming efficiency of robots. In this article, we study the motion planning problem of manipulators for simultaneous obstacle avoidance and target tracking, and propose a novel real-time planning method in a complex workspace. One important feature of the proposed method is that the robot can avoid colliding with obstacles by easily defining “virtual fences,” which are described by a group of level set functions. Thus, the feasible space can be abstracted as inequality constraints. Taking the predefined task, physical constraints, and feasible space constraints into consideration, the motion planning problem is formulated into a quadratic programming (QP) one, in which the redundant degrees of freedom are used to optimize the velocities of the robot. Then, the control command is obtained by an established recurrent neural network, which is capable of solving the QP problem in an online manner. Theoretical conduction and verification in several typical workspaces demonstrate the efficacy of the established method, such as the abilityto remove physical fences, quick rearrangement, and performance optimization.