Abstract

To address the safety, efficiency, and adaptability challenges of autonomous path planning for lunar rovers in complex and unknown terrain, this study proposes a path planning method that integrates deep reinforcement learning with Hybrid A*. Multi-level obstacle maps are generated from lunar digital elevation models and local terrain reconstruction, and an environment model integrating terrain features and rover states is constructed. A Deep Deterministic Policy Gradient framework is introduced, including a state encoder, a policy network, and dual Q-networks, to dynamically adapt the search parameters of Hybrid A* and optimize the path-cost function online. Simulation experiments are conducted under simple, moderate, and complex scenarios. The results show that the proposed method achieves a 100% path planning success rate in the evaluated baseline scenarios, with comprehensive accuracy scores of 0.931, 0.907, and 0.886, respectively. It outperforms conventional A*, RRT, and Hybrid A* algorithms in path length, smoothness, and safety. The method can support safe and efficient autonomous exploration by lunar rovers under the tested dynamic-illumination and multi-obstacle conditions and provides technical support for future deep-space exploration missions such as Chang’e and Tianwen.

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