Reinforcement Learning-Based Continuous Action Space Path Planning Method for Mobile Robots

Abstract

A reinforcement learning-based continuous action space path planning method for mobile robots is proposed in this article. First, the kinematic model of the mobile robot is analyzed, and on this basis, the optimal state space is constructed according to the minimum depth of the field value in the depth image to characterize the distance between the robot and the obstacle. Then, by setting the reward function of the mobile robot based on the artificial potential field method, the information of the robot’s distance from obstacles is continuous, and a new reinforcement learning training process is proposed. Finally, by introducing a DDPG algorithm, the path planning of a mobile robot in an unknown environment is described as a Markov decision process, and the optimal planning of the mobile robot’s continuous action space path is realized with a high success rate. The results show that compared with other three comparison methods, the final success rates of the proposed method are the highest, which are 97.2%, 99.1%, 98.4%, and 98.6%, respectively.