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.

翻译：在复杂地形上实现鲁棒的多足运动，由于机器人-环境相互作用的高度不确定性而面临挑战。双足与四足机器人的最新进展已能在崎岖地形上展现出良好的移动能力，但由于其高重心与窄支撑基底导致的低静态稳定性，它们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冗余性，在穿越挑战性地形时最小化对传感的需求。研究表明，具有足够数量腿的多足系统能够在无传感与控制的情况下可靠地穿越噪声地形，尽管其速度较低，最高仅达每周期0.1体长（BLC）。然而，由于复杂的环境相互作用，在挑战性地形上提升速度的控制框架仍未得到充分探索，这使得难以确定在这些高自由度系统中需要控制的关键参数。本文提出一种受生物启发的垂直体干波动波，作为减轻影响机器人速度的环境干扰的新方法，并通过实验和概率模型予以支持。最后，我们引入一个控制框架，该框架利用二值足-地接触传感器监测崎岖地形上的足-地接触模式，以估计地形崎岖度。控制器根据肢体平均实际-理想足-地接触比的偏差来调整垂直体干波，从而在实验室崎岖地形上实现了高达0.235 BLC的显著性能提升。与开环控制器相比，我们观察到速度提升了约50%，速度方差降低了约40%。此外，该控制器亦能在实验室外的复杂地形（包括松针、机器人尺寸的岩石、泥泞和落叶）中运行。