Obstacle Avoidance Based on Virtual Repulsive Potential Fields under Limited Perceptions

Abstract

Aiming at the unknown obstacle environment, an online obstacle avoidance method, which is based on the model-based artificial potential field (MAPF) method and only depends on the information detected by onboard sensors, is proposed. First, the entire mission space is discretized. According to the relative position relation between the detection sector and the detected obstacle surface in the discrete space, the virtual repulsive potential fields (VRPFs) and the corresponding total force fields are constructed to make the robot avoid the detected obstacles and gradually move to the destination. Then, to avoid the local optimum, the memory mechanism for VRPFs and its corresponding path planning solution are introduced. Finally, the effectiveness of the proposed method is demonstrated by the simulation.