Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control

TL;DR

This paper introduces a multi-layered control framework combining Control Barrier Functions and Model Predictive Control to enhance safe and stable legged robot locomotion over rough terrain, validated in simulation and real-world experiments.

Contribution

It unifies CBFs with MPC for simultaneous safe foot placement and dynamic stability, addressing limitations of prior heuristic or short-term safety approaches.

Findings

Successful simulation of 3D stepping-stone scenario

Experimental validation on ANYmal quadruped platform

Enhanced safety and stability in dynamic locomotion

Abstract

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.