面向行星永久阴影区探测的四足机器人本体感受地形映射研究
Towards Proprioceptive Terrain Mapping with Quadruped Robots for Exploration in Planetary Permanently Shadowed Regions
AI总结：
针对月球永久阴影区探测需求，提出一种基于四足机器人本体感受的地形映射框架，通过内部传感估计多项地形交互指标并构建多层2.5D网格地图，仿真验证了其在月球环境下的稳定建图性能。
摘要：
月球两极附近的永久阴影区（Permanently Shadowed Regions, PSRs）因可能蕴藏水冰并保存地质记录，是未来探测的重点区域。其复杂崎岖的地形适合采用足式机器人，这类机器人能够穿越挑战性地形并采集原位数据，且已被证明在配备机载照明时，可在地球模拟环境（包括黑暗洞穴）中有效作业。虽然相机、激光雷达等外感受传感器能捕捉地形几何结构甚至语义信息，但无法量化地形与机器人的物理相互作用，而这一能力可由本体感受传感提供。我们提出了一种适用于四足机器人的地形映射框架，可在运动过程中通过内部传感估计高程、足部滑移、能量消耗和稳定裕度。这些指标被逐步整合到一个多层2.5D网格地图中，从机器人视角反映地形交互特性。该系统在模拟月球环境的仿真器中，使用21千克重的四足机器人Aliengo进行了评估，结果显示其在月球重力和地形条件下具备稳定的建图性能。
英文摘要：
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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