Probabilistic Approach to Feedback Control Enhances Multilegged Locomotion on Rugged Landscapes

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 (Ijspeert and Daley, 2023).We hypothesize that a multilegged robotic system can leverage morphological redundancy from additional legs to minimize sensing requirements when traversing challenging terrains. Studies suggest (Chong et al., 2023), (Chong et al., 2023) that a multilegged 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 feedback control framework to enhance speed of multilegged robots on challenging terrains remains underexplored due to diverse environmental interactions. Such complexity makes it difficult to identify the key parameters to control in these high-degree-of-freedom systems. Here, using laboratory and field experiments, we demonstrate that a vertical body undulation wave helps mitigate environmental disturbances that affect robot speed. These findings are supported by probabilistic models. Using such insights, 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 50% to 60% increase in speed and a 30% to 50% reduction in speed variance compared to the open-loop controller. In addition, the controller operates in complex terrains outside the lab, including pine straw, robot-sized rocks, mud, and leaves.