发表机构
University of Southern California（南加州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对颗粒斜坡上腿式机器人运动性能下降问题，提出基于阻力测量的交互模型，揭示锚定延迟和滑移是主因，并构建失效相图以指导安全操作。
AI 中文摘要
在沙丘等颗粒状斜坡上的运动，由于颗粒介质剪切强度降低以及重力引起的各向异性屈服，仍然是腿式机器人面临的基本挑战。我们使用一个六足机器人在可倾斜的颗粒床上，系统地测量了运动速度以及随坡度变化的法向和剪切颗粒阻力。虽然法向穿透阻力随倾斜变化几乎不变，但剪切阻力随坡度增加而显著下降。在这些测量的指导下，我们开发了一个简单的机器人-地形相互作用模型，该模型预测锚定时机、步长以及由此产生的机器人速度，作为地形强度和坡度的函数。模型揭示，坡度引起的性能损失主要由锚定延迟和向后滑移增加所主导，而非过度下陷。通过将模型推广到一般地形条件，我们构建了失效相图，识别出下陷和滑移引起的失效区域，从而能够对颗粒状斜坡上的运动进行定量风险评估。这种基于物理的框架为地形相关的失效机制提供了预测性见解，并为在可变形斜坡上更安全、更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.
