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.

翻译：腿式机器人能够在运动过程中通过力交互感知地形，提供比遥感更可靠的可通行性估计，并在挑战性环境中作为轮式探测车的引导侦察者。然而，即使腿式侦察机器人在穿越高度可变形或不稳定地形时也面临挑战。本文提出面向颗粒地形的安全主动探索框架，该导航框架使腿式机器人能够利用本体感知（尤其是在视觉输入无法捕捉地形可变形性的情况下）安全探索未知颗粒环境。SAEGT通过腿-地形交互作用，采用高斯过程回归进行可通行性评估，在线估计安全区域与前沿区域，并配备反应式控制器以实现实时安全探索与导航。仿真结果表明，SAEGT能够仅依靠本体感知估计的可通行性信息，安全探索环境并导航至指定目标。