Achieving safe quadrupedal locomotion in real-world environments has attracted much attention in recent years. When walking over uneven terrain, achieving reliable estimation and realising safety-critical control based on the obtained information is still an open question. To address this challenge, especially for low-cost robots equipped solely with proprioceptive sensors (e.g., IMUs, joint encoders, and contact force sensors), this work first presents an estimation framework that generates a 2.5-D terrain map and extracts support plane parameters, which are then integrated into contact and state estimation. Then, we integrate this estimation framework into a safety-critical control pipeline by formulating control barrier functions that provide rigorous safety guarantees. Experiments demonstrate that the proposed terrain estimation method provides smooth terrain representations. Moreover, the coupled estimation framework of terrain, state, and contact reduces the mean absolute error of base position estimation by 64.8%, decreases the estimation variance by 47.2%, and improves the robustness of contact estimation compared to a decoupled framework. The terrain-informed CBFs integrate historical terrain information and current proprioceptive measurements to ensure global safety by keeping the robot out of hazardous areas and local safety by preventing body-terrain collision, relying solely on proprioceptive sensing.

翻译：近年来，在现实环境中实现四足机器人的安全运动引起了广泛关注。在不平坦地形上行走时，如何基于获取的信息实现可靠的估计并执行安全关键控制仍是一个开放性问题。为应对这一挑战，特别是针对仅配备本体感知传感器（如IMU、关节编码器和接触力传感器）的低成本机器人，本研究首先提出一种估计框架，该框架可生成2.5维地形图并提取支撑平面参数，进而将其融合到接触与状态估计中。随后，我们通过构建能提供严格安全保证的控制屏障函数，将该估计框架集成至安全关键控制流程中。实验表明，所提出的地形估计方法能提供平滑的地形表征。此外，相较于解耦框架，地形-状态-接触的耦合估计框架将基座位置估计的平均绝对误差降低了64.8%，估计方差减少了47.2%，并提升了接触估计的鲁棒性。融合地形信息的控制屏障函数通过整合历史地形信息与当前本体感知测量，在仅依赖本体感知的情况下，既通过使机器人远离危险区域保障全局安全，又通过防止机体与地形碰撞确保局部安全。