揭示足式机器人在沙坡上行进的失稳机制，可预测风险。
Failure Mechanisms and Risk Estimation for Legged Robot Locomotion on Granular Slopes
- 基于实测数据建立地形交互模型，预测步长与速度
- 发现坡度增大导致滑移加剧而非沉陷，是性能下降主因
- 构建失效相图，支持对沙坡行进风险的量化评估
在沙丘等颗粒斜坡上的运动仍是足式机器人的核心挑战，源于颗粒介质剪切强度降低及重力诱导的各向异性屈服。通过六足机器人在可调倾角颗粒床的系统实验，我们测量了运动速度以及随坡度变化的法向和剪切颗粒阻力。结果显示，法向贯入阻力随坡度变化不大，但剪切阻力显著下降。基于这些测量，我们提出一个简化的机器人-地形交互模型，可预测锚定时机、步长和最终速度，取决于地形强度和坡度。模型表明，坡度导致的性能下降主要由锚定延迟和后滑增加引起，而非过度沉陷。将模型扩展至一般地形条件后，构建出失效相图，识别出沉陷与滑移主导的失效区域，实现对颗粒斜坡行进风险的定量估计。该物理启发框架为可变形斜坡上的机器人安全可靠运行提供了预测性洞见。
原文摘要 · Abstract (English)
Locomotion on granular slopes such as sand dunes remains a fundamental challenge for legged robots due to reduced shear strength and gravity-induced anisotropic yielding of granular media. Using a hexapedal robot on a tiltable granular bed, we systematically measure locomotion speed together with slope-dependent normal and shear granular resistive forces. While normal penetration resistance remains nearly unchanged with inclination, shear resistance decreases substantially as slope angle increases. Guided by these measurements, we develop a simple robot-terrain interaction model that predicts anchoring timing, step length, and resulting robot speed, as functions of terrain strength and slope angle. The model reveals that slope-induced performance loss is primarily governed by delayed anchoring and increased backward slip rather than excessive sinkage. By extending the model to generalized terrain conditions, we construct failure phase diagrams that identify sinkage- and slippage-induced failure regimes, enabling quantitative risk estimation for locomotion on granular slopes. This physics-informed framework provides predictive insight into terrain-dependent failure mechanisms and offers guidance for safer and more robust robot operation on deformable inclines.
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