反馈控制的概率方法增强了多足机器人在崎岖地形上的运动能力
Probabilistic approach to feedback control enhances multi-legged locomotion on rugged landscapes
AI总结：
针对多足机器人在崎岖地形运动中提升速度的难题，提出仿生垂直身体波动波方法，并设计基于二值足-地接触传感器的概率控制框架，通过监测接触比率偏差调整身体波，显著提升了运动速度并降低了速度方差。
摘要：
由于机器人与环境相互作用的高度不确定性，在复杂地形上实现鲁棒的足式运动面临挑战。双足和四足机器人的最新进展展示了在崎岖地形上的良好机动性，但由于质心高和支撑面窄导致的低静态稳定性，严重依赖传感器来维持稳定。我们假设多足机器人系统可以利用额外腿带来的形态学冗余，在穿越具有挑战性的地形时最小化感知需求。研究表明，具有足够腿数量的多足系统可以在没有感知和控制的情况下可靠地在嘈杂地形中导航，尽管速度较低，仅为每周期0.1体长（BLC）。然而，由于复杂的环境相互作用，在具有挑战性的地形上提升速度的控制框架仍未被充分探索，这使得难以识别这些高自由度系统中需要控制的关键参数。在此，我们提出了一种仿生的垂直身体波动波作为一种新方法来减轻影响机器人速度的环境干扰，并由实验和概率模型提供支持。最后，我们引入了一个控制框架，该框架使用二值足-地接触传感器监测粗糙地形上的足-地接触模式，以估计地形粗糙度。控制器基于肢体平均实际与理想足-地接触比率的偏差来调整垂直身体波，在粗糙实验室地形上实现了高达0.235 BLC的显著提升。我们观察到，与开环控制器相比，速度提升了约50%，速度方差降低了约40%。此外，该控制器在实验室外的复杂地形中也能运行，包括松针、机器人尺寸的岩石、泥浆和树叶。
英文摘要：
Achieving robust legged locomotion on complex terrains poses challenges due to the high uncertainty in robot-environment interactions. Recent advances in bipedal and quadrupedal robots demonstrate good mobility on rugged terrains but rely heavily on sensors for stability due to low static stability from a high center of mass and a narrow base of support. We hypothesize that a multi-legged robotic system can leverage morphological redundancy from additional legs to minimize sensing requirements when traversing challenging terrains. Studies suggest that a multi-legged system with sufficient legs can reliably navigate noisy landscapes without sensing and control, albeit at a low speed of up to 0.1 body lengths per cycle (BLC). However, the control framework to enhance speed on challenging terrains remains underexplored due to the complex environmental interactions, making it difficult to identify the key parameters to control in these high-degree-of-freedom systems. Here, we present a bio-inspired vertical body undulation wave as a novel approach to mitigate environmental disturbances affecting robot speed, supported by experiments and probabilistic models. Finally, we introduce a control framework which monitors foot-ground contact patterns on rugose landscapes using binary foot-ground contact sensors to estimate terrain rugosity. The controller adjusts the vertical body wave based on the deviation of the limb's averaged actual-to-ideal foot-ground contact ratio, achieving a significant enhancement of up to 0.235 BLC on rugose laboratory terrain. We observed a $\sim$ 50\% increase in speed and a $\sim$ 40\% reduction in speed variance compared to the open-loop controller. Additionally, the controller operates in complex terrains outside the lab, including pine straw, robot-sized rocks, mud, and leaves.
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