School of Astronautics, Harbin Institute of Technology, Harbin 150002, China.
China Academy of Space Technology, Beijing 100094, China.
Sensors (Basel). 2021 Jan 25;21(3):796. 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.
路径规划是月球车实现安全高效自主探测任务的关键技术，本文提出了一种具有安全约束的基于学习的端到端路径规划算法。首先，使用 Gazebo 仿真环境构建了一个集成真实月面地形数据的训练环境，并在其中创建了一个月球车模拟器，以模拟真实的月面环境和月球车系统。然后，设计了一种基于深度强化学习方法的端到端路径规划算法，包括状态空间、动作空间、网络结构、考虑滑移行为的奖励函数以及基于近端策略优化的训练方法。此外，为了提高对不同月面地形和不同规模环境的泛化能力，采用课程学习的思想设置了多种训练场景来训练网络模型。仿真结果表明，所提出的规划算法能够成功实现月球车的端到端路径规划，并且与经典路径规划算法相比，所生成的路径具有更高的安全保障。
