用腿式探测器实时测绘地形强度,帮轮式车避开危险区域。
Scout-Rover cooperation: online terrain strength mapping and traversal risk estimation for planetary-analog explorations
- 腿式机器人通过自身运动感知土壤强度,动态生成地形图。
- 实现针对轮式车的路径风险评估,确保安全抵达科学目标点。
- 适合火星沙丘、月球陨石坑等松软地形的探测任务。
行星表面的机器人探索对理解地质过程至关重要,但火星沙丘和月球陨石坑等区域因松软可变形的风化层仍具危险性。本文提出一种侦察-巡视车协作框架,采用腿式与轮式机器人组成的异构团队扩展安全可达范围。高机动性腿式机器人作为移动侦察员,利用本体感知的腿部-地形相互作用,在行进中估算风化层强度并构建空间解析的地形地图。这些地图与巡视车运动模型结合,用于估计通行风险并指导路径规划。在NASA Ames月壤模拟试验场和怀特山沙地场的类比任务中验证了该框架:(1)通过腿式行走实现在线地形强度测绘;(2)生成面向特定巡视车的通行风险评估,实现安全导航至科学目标。结果表明,侦察生成的地图能可靠捕捉空间变异性,并预测移动失效模式,支持风险感知路径规划以规避危险区域。通过融合具身地形感知与异构机器人协作,该框架提升了操作鲁棒性,扩大了可探索的科学工作空间。
原文摘要 · Abstract (English)
Robot-aided exploration of planetary surfaces is essential for understanding geologic processes, yet many scientifically valuable regions, such as Martian dunes and lunar craters, remain hazardous due to loose, deformable regolith. We present a scout-rover cooperation framework that expands safe access to such terrain using a hybrid team of legged and wheeled robots. In our approach, a high-mobility legged robot serves as a mobile scout, using proprioceptive leg-terrain interactions to estimate regolith strength during locomotion and construct spatially resolved terrain maps. These maps are integrated with rover locomotion models to estimate traversal risk and inform path planning. We validate the framework through analogue missions at the NASA Ames Lunar Simulant Testbed and the White Sands Dune Field. Experiments demonstrate (1) online terrain strength mapping from legged locomotion and (2) rover-specific traversal-risk estimation enabling safe navigation to scientific targets. Results show that scout-generated terrain maps reliably capture spatial variability and predict mobility failure modes, allowing risk-aware path planning that avoids hazardous regions. By combining embodied terrain sensing with heterogeneous rover cooperation, this framework enhances operational robustness and expands the reachable science workspace in deformable planetary environments.
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