四足机器人在颗粒斜坡上运动的失效机制与风险估计
Failure Mechanisms and Risk Estimation for Legged Robot Locomotion on Granular Slopes
AI总结：
研究四足机器人在颗粒斜坡上的运动失效机制，通过实验建立机器人-地形交互模型，预测锚定时机和步长，揭示斜坡导致性能下降的主要因素是延迟锚定和增加后滑，从而构建失效相图以量化风险。
摘要：
颗粒斜坡（如沙丘）上的运动仍是四足机器人面临的基本挑战，因为颗粒介质的剪切强度降低和重力诱导的各向异性屈服。使用六足机器人在可调颗粒床中，我们系统地测量了运动速度以及与斜坡相关的法向和剪切颗粒阻力。虽然法向穿透阻力随倾斜度变化不大，但剪切阻力随着斜坡角度增加显著下降。基于这些测量，我们开发了一个简单的机器人-地形交互模型，预测锚定时机、步长和由此产生的机器人速度，作为地形强度和斜坡角度的函数。该模型揭示，斜坡导致的性能损失主要由延迟锚定和增加的后滑而非过度下沉引起。通过将模型扩展到一般化地形条件，我们构建了失效相图，识别了由下沉和滑动引起的失效区域，从而为颗粒斜坡上的运动提供定量风险估计。该物理指导框架为地形相关的失效机制提供了预测见解，并为在变形斜坡上更安全和稳健的机器人操作提供了指导。
英文摘要：
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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