Obstacle Avoidance Based on Deep Reinforcement Learning and Artificial Potential Field

Abstract

Obstacle avoidance is an essential part of mobile robot path planning, since it ensures the safety of automatic control. This paper proposes an obstacle avoidance algorithm that combines artificial potential field with deep reinforcement learning (DRL). State regulation is presented so that the pre-defined velocity constraint could be satisfied. To guarantee the isotropy of the robot controller as well as reduce training complexity, coordinate transformation into normal direction and tangent direction is introduced, making it possible to use one-dimension controllers to work in a two-dimension task. Artificial potential field (APF) is modified such that the obstacle directly affects the intermediate target positions instead of the control commands, which can well be used to guide the previously trained one-dimension DRL controller. Experiment results show that the proposed algorithm successfully achieved obstacle avoidance tasks in single-agent and multi-agent scenarios.