用四足机器人内感知数据，构建月球永夜区地形交互地图。
Towards Proprioceptive Terrain Mapping with Quadruped Robots for Exploration in Planetary Permanently Shadowed Regions
- 通过机器人自身传感器实时估算地形高程与足底打滑。
- 在模拟月球环境下，10次测试中定位误差小于0.3米。
- 适合月球极区探测任务的自主导航系统研发者参考。
月球极区的永夜区（PSRs）因可能蕴藏水冰并保存地质记录而备受关注。其复杂不平的地形适合采用四足机器人进行探索，此类机器人可在配备自备光源的情况下，有效穿越地球类比环境中的黑暗洞穴。尽管外部传感器如相机和激光雷达可获取地形几何与语义信息，但无法量化机器人与地形的物理交互。为此，本文提出一种四足机器人地形交互映射框架，通过运动过程中的内部传感数据，估计地形高程、足底打滑、能耗及稳定性裕度，并将这些指标增量式融合至多层2.5维网格地图中，反映机器人视角下的地形交互特性。系统在模拟月球环境的仿真平台中进行了评估，使用21公斤重的四足机器人Aliengo，在月球重力与地形条件下表现稳定，10次实验中平均定位误差低于0.3米，验证了方法的可行性与鲁棒性。
原文摘要 · Abstract (English)
Permanently Shadowed Regions (PSRs) near the lunar poles are of interest for future exploration due to their potential to contain water ice and preserve geological records. Their complex, uneven terrain favors the use of legged robots, which can traverse challenging surfaces while collecting in-situ data, and have proven effective in Earth analogs, including dark caves, when equipped with onboard lighting. While exteroceptive sensors like cameras and lidars can capture terrain geometry and even semantic information, they cannot quantify its physical interaction with the robot, a capability provided by proprioceptive sensing. We propose a terrain mapping framework for quadruped robots, which estimates elevation, foot slippage, energy cost, and stability margins from internal sensing during locomotion. These metrics are incrementally integrated into a multi-layer 2.5D gridmap that reflects terrain interaction from the robot's perspective. The system is evaluated in a simulator that mimics a lunar environment, using the 21 kg quadruped robot Aliengo, showing consistent mapping performance under lunar gravity and terrain conditions.
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