中国空间科学技术 ›› 2026, Vol. 46 ›› Issue (2): 126-137.doi: 10.16708/j.cnki.1000-758X.2026.0031

• 载人月球探测专刊 • 上一篇    下一篇

月面科学探测智能体训练支持系统与应用探索

董镇远1,2,赫世敬 1,2,姜佳慧1,2,林茜1,2,杨瀚哲2,王之2,严冬2,刘成保2,张鹏2,*   

  1. 1.中国科学院大学,北京100049
    2.中国科学院空间应用工程与技术中心,北京100094
  • 收稿日期:2025-10-13 修回日期:2026-01-24 录用日期:2026-01-31 发布日期:2026-03-20 出版日期:2026-03-31

Intelligent agent training support system for lunar scientific exploration and application

DONG Zhenyuan1,2,HE Shijing1,2,JIANG Jiahui1,2,LIN Qian1,2,YANG Hanzhe2,WANG Zhi2,YAN Dong2, LIU Chengbao2,ZHANG Peng2,*   

  1. 1.University of Chinese Academy of Sciences, Beijing 100049, China
    2.Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China
  • Received:2025-10-13 Revision received:2026-01-24 Accepted:2026-01-31 Online:2026-03-20 Published:2026-03-31

摘要: 针对月面探测器在复杂环境下的智能化需求,提升智能体训练决策能力具有关键意义。当前面向真实月面任务,仍缺乏高保真仿真环境以精确复现复杂地形与光照条件,同时也亟需构建覆盖长距离探测极端工况的多样化训练数据。实现了“月面场景-装备模型-场景感知”的联合仿真,建立了高保真月面移动智能体训练支持系统:基于数字月球技术,构建厘米级精度的月面地形地貌环境模型,为智能体训练提供逼近真实的仿真场景;搭建耦合月面环境与探测器装备模型的综合仿真场景,实现对月面活动任务的全过程模拟;建立探测器感知、导航、规划和控制算法与仿真系统之间的实时双向数据交互链路,使智能体能够在闭环仿真中持续学习与优化。为月球车等移动探测器智能体的算法训练与验证提供关键技术平台及数据支撑,对中国载人月球探测任务的实施具有重要的工程应用价值。

关键词: 载人月球探测;月面探测器;联合仿真, 智能体;月面环境仿真

Abstract: To meet the intelligent requirements of the lunar rover in complex environments, enhancing the decision-making capabilities of agents through training is of critical importance. Currently, for lunar missions there remains a lack of high-fidelity simulation environments capable of accurately reproducing complex terrain and lighting conditions. There is also an urgent need to construct diverse training data that covers extreme scenarios encountered during long-distance exploration. This achieves a joint simulation of "lunar terrain scenarios, equipment models, and environmental perception", establishing a high-fidelity training support system for lunar surface mobile intelligent agents. Firstly, utilizing digital moon technology, a centimeter-level precision lunar terrain model was constructed, providing a physically accurate foundation for agent training. And an integrated simulation scenario coupling lunar environment and rover dynamics models was established to simulate the entire workflow of lunar surface operations. Lastly, a real-time bidirectional data exchange links were established between the rover's perception, navigation, planning, and control algorithms and the simulation platform, enabling continuous learning and evolution of the intelligent agent within a closed-loop simulation. This system provides a critical technological platform for training autonomous capabilities and validating algorithms for mobile lunar rover agents, offering essential data support. It holds significant engineering value for the implementation of China's manned lunar exploration missions.

Key words: manned lunar exploration, lunar rover, joint simulation, intelligent agents, lunar environment simulation