论文标题
通过控制屏障功能和模型预测控制的腿部机器人的多层安全性
Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control
论文作者
论文摘要
在粗糙的地形上动态运动的问题需要精确的脚部放置，并着重于动态稳定性。现有的解决此问题的方法优先考虑长期动态稳定性考虑因素，或者将脚部放置的配位和动态稳定性降级到启发式方法上。我们提出了一个多层运动框架，该框架将控制屏障功能（CBF）统一使用模型预测性控制（MPC），以同时实现安全的脚部放置和动态稳定性。我们的方法在低频Kino-Dynamic MPC公式和高频逆动力学跟踪控制器中都包含了基于CBF的安全性约束。这样可以确保在更长的地平线上优化运动时考虑安全至关重要的执行。我们在模拟中的3D踏板线方案中验证了所提出的方法，并在Anymal四倍的平台上实验。
The problem of dynamic locomotion over rough terrain requires both accurate foot placement together with an emphasis on dynamic stability. Existing approaches to this problem prioritize immediate safe foot placement over longer term dynamic stability considerations, or relegate the coordination of foot placement and dynamic stability to heuristic methods. We propose a multi-layered locomotion framework that unifies Control Barrier Functions (CBFs) with Model Predictive Control (MPC) to simultaneously achieve safe foot placement and dynamic stability. Our approach incorporates CBF based safety constraints both in a low frequency kino-dynamic MPC formulation and a high frequency inverse dynamics tracking controller. This ensures that safety-critical execution is considered when optimizing locomotion over a longer horizon. We validate the proposed method in a 3D stepping-stone scenario in simulation and experimentally on the ANYmal quadruped platform.
