Legged robots leverage ground contacts and the reaction forces they provide to achieve agile locomotion. However, uncertainty coupled with contact discontinuities can lead to failure, especially in real-world environments with unexpected height variations such as rocky hills or curbs. To enable dynamic traversal of extreme terrain, this work introduces 1) a proprioception-based gait planner for estimating unknown hybrid events due to elevation changes and responding by modifying contact schedules and planned footholds online, and 2) a two-degree-of-freedom tail for improving contact-independent control and a corresponding decoupled control scheme for better versatility and efficiency. Simulation results show that the gait planner significantly improves stability under unforeseen terrain height changes compared to methods that assume fixed contact schedules and footholds. Further, testing shows the tail is most effective at maintaining stability when encountering a terrain change with an initial angular disturbance. The results show that these approaches work synergistically to stabilize locomotion with elevation changes up to 1.5 times the leg length and tilted initial states.

翻译：足式机器人通过地面接触及其提供的反作用力实现敏捷运动。然而，接触不连续性与不确定性可能导致运动失败，尤其是在包含岩石丘陵或路缘等意外高度变化的真实环境中。为实现极端地形动态穿越，本研究提出：（1）基于本体感觉的步态规划器，用于估计由高程变化引起的未知混合事件，并通过在线调整接触时序和规划落足点进行响应；（2）一种两自由度尾部机构，用于提升非接触依赖性控制性能，并设计相应的解耦控制方案以提高通用性与效率。仿真结果表明，相较于假设固定接触时序与落足点的方法，该步态规划器在未知地形高度变化下显著提升了稳定性。进一步测试表明，当遭遇伴有初始角扰动的地形变化时，尾部维持稳定性的效果最为显著。结果表明，这些方法协同作用，可在高达1.5倍腿长的高程变化及倾斜初始状态下保持运动稳定性。