An Improved Artificial Potential Field Method for Path Planning of Mobile Robot with Subgoal Adaptive Selection

Abstract

As a simple and effective method, artificial potential field method is often used in robot path planning. Based on this, an improved artificial potential field model is proposed to solve the local minimum problem by using a subgoal strategy. In order to show the subgoal adaptive selection feature of the robot, an obstacle potential field function is established and the effectiveness of the adaptive feature is verified by path planning simulation. A double closed-loop control strategy is adopted to track the trajectory planned by the improved artificial potential field method, and simulation results show that the improved artificial potential method is reliable and the robot can well track the trajectory under the action of the controller.