Robot Path Planning based on Probabilistic Roadmaps and Velocity Potential Field in Complex Environment

Abstract

A robot path planning method based on probabilistic roadmaps and velocity potential field is proposed in this paper. Probabilistic roadmaps can give continuous optimization target points considering the global environment information, and the velocity potential field method generates the driving force of the robot to reach the target point one by one until the final target. The combination of the above two methods considers the local information and global information simultaneously. In this way, this method can make the robot effectively avoid falling into local minimum traps and obtain local adjustment ability to deal with measurement deviation caused by global sensors. The simulation results for a 6-degree-of-freedom robotic arm show the effectiveness of the proposed method.