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:然而,不确定性加上接触不连续可能会导致失败,特别是在现实世界环境中,高地变化出人意料,如岩山或路面。为了能够动态穿越极端地形,这项工作引入了(1) 一种基于自动感知的毛片规划仪,用于估计高地变化引起的未知混合事件,并通过修改接触时间表和在线计划脚站脚来应对;(2) 一种两度自由尾巴,用于改进接触独立的控制,以及相应的分解控制计划,以提高多功能和效率。模拟结果显示,与假定固定接触时间表和脚点的方法相比,在未预见的地形高度变化下,弹尾巴大大改善了稳定性。此外,测试显示,在遇到地形变化时,在最初的角扰动下,尾巴对维持稳定最为有效。结果显示,这些方法合力稳定了升幅,腿长1.5倍,初始状态倾斜。</s>