用身体感知导航，让机器人在看不见的沙地里安全探索
Safe Active Navigation and Exploration for Planetary Environments Using Proprioceptive Measurements
- 通过肢体与地形的力交互实时评估可通行性
- 在模拟中成功实现无视觉输入下的安全导航至目标
- 适合火星等视觉受限的行星探测场景
腿式机器人可通过运动过程中的力交互感知地形，比远程传感更可靠，能作为轮式探测车的先导。然而，在高度可变形或不稳定的地形上，即使腿式机器人也面临挑战。本文提出安全主动探索颗粒地形（SAEGT）框架，利用本体感受器实现对未知颗粒环境的安全探索，尤其在视觉无法捕捉地形变形时仍有效。SAEGT通过高斯过程回归在线估计安全区域和前沿区域，结合反应式控制器实现实时安全导航。在仿真中，仅依赖本体感知的可通行性估计，成功完成向目标的安全探索。
原文摘要 · Abstract (English)
Legged robots can sense terrain through force interactions during locomotion, offering more reliable traversability estimates than remote sensing and serving as scouts for guiding wheeled rovers in challenging environments. However, even legged scouts face challenges when traversing highly deformable or unstable terrain. We present Safe Active Exploration for Granular Terrain (SAEGT), a navigation framework that enables legged robots to safely explore unknown granular environments using proprioceptive sensing, particularly where visual input fails to capture terrain deformability. SAEGT estimates the safe region and frontier region online from leg-terrain interactions using Gaussian Process regression for traversability assessment, with a reactive controller for real-time safe exploration and navigation. SAEGT demonstrated its ability to safely explore and navigate toward a specified goal using only proprioceptively estimated traversability in simulation.
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