发表机构
University of Miami; Carnegie Mellon University（迈阿密大学; 卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过三种控制方法证明占空比比步态类型更能预测四足运动的鲁棒性，并作为低维参数指导狭窄地形下的鲁棒运动选择。
AI 中文摘要
四足机器人越来越多地被部署在运动必须对扰动和受限地形保持鲁棒的环境中。步态类型，如行走或小跑，通常用于表征四足运动。然而，步态类型并不能唯一地定义运动，因为占空比、速度和站立宽度等参数可以在单一一种步态类型内变化。在这项工作中，我们使用三种不同的四足运动控制方法来研究这些步态参数之间的关系。首先，使用带有LQR反馈的全身轨迹优化，我们表明占空比是局部误差收敛的更强预测因子，而非名义步态类型。其次，我们使用学习型运动控制器研究占空比选择，表明占空比如何作为低维参数用于在狭窄地形环境中调整运动鲁棒性。最后，我们表明这些趋势在质心模型预测控制框架下持续存在，并通过在物理四足机器人上的狭窄地形实验进行验证。这些结果表明，占空比为理解和选择跨步态类型和控制架构的鲁棒四足运动提供了一个简单而有效的基础。
英文摘要
Quadrupedal robots are increasingly deployed in environments where locomotion must remain robust to disturbances and constrained terrain. Gait type, such as walking or trotting, is commonly used to characterize quadrupedal locomotion. However, gait type does not uniquely define locomotion, as parameters such as duty factor, speed, and stance width can vary within a single gait type. In this work, we investigate the relationship between these gait parameters using three distinct quadrupedal locomotion control approaches. First, using whole body trajectory optimization with LQR feedback, we show that duty factor is a stronger predictor of local error convergence than nominal gait type. Second, we investigate duty factor selection with a learned locomotion controller, suggesting how duty factor may serve as a low-dimensional parameter for adapting locomotion robustness in narrow-terrain environments. Finally, we show that these trends persist under a centroidal model predictive control framework and validate them through narrow-terrain experiments on a physical quadruped. These results show that duty factor provides a simple and effective basis for understanding and selecting robust quadrupedal locomotion across gait types and control architectures.
