Learning-Based End-to-End Path Planning for Lunar Rovers with Safety Constraints

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 3; p. 796
• Main Authors: Yu, Xiaoqiang, Wang, Ping, Zhang, Zexu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 25.01.2021; MDPI AG
• Subjects: deep reinforcement learning; learning-based; lunar rovers; path planning; lunar rovers; deep reinforcement learning; path planning; learning-based
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21030796

Path planning is an essential technology for lunar rover to achieve safe and efficient autonomous exploration mission, this paper proposes a learning-based end-to-end path planning algorithm for lunar rovers with safety constraints. Firstly, a training environment integrating real lunar surface terrain data was built using the Gazebo simulation environment and a lunar rover simulator was created in it to simulate the real lunar surface environment and the lunar rover system. Then an end-to-end path planning algorithm based on deep reinforcement learning method is designed, including state space, action space, network structure, reward function considering slip behavior, and training method based on proximal policy optimization. In addition, to improve the generalization ability to different lunar surface topography and different scale environments, a variety of training scenarios were set up to train the network model using the idea of curriculum learning. The simulation results show that the proposed planning algorithm can successfully achieve the end-to-end path planning of the lunar rover, and the path generated by the proposed algorithm has a higher safety guarantee compared with the classical path planning algorithm.