Walking in Narrow Spaces: Safety-critical Locomotion Control for Quadrupedal Robots with Duality-based Optimization

TL;DR

This paper introduces a novel control framework combining exponential DCBFs and NMPC to enable quadrupedal robots to safely navigate in cluttered, tight environments, surpassing previous spherical approximation methods.

Contribution

The work develops a duality-based obstacle avoidance method integrated into NMPC for quadrupedal locomotion, allowing precise collision avoidance with complex obstacle shapes.

Findings

Successfully navigates tight spaces in real-world tests

Uses polytopes for accurate obstacle shape representation

Outperforms spherical approximation methods

Abstract

This paper presents a safety-critical locomotion control framework for quadrupedal robots. Our goal is to enable quadrupedal robots to safely navigate in cluttered environments. To tackle this, we introduce exponential Discrete Control Barrier Functions (exponential DCBFs) with duality-based obstacle avoidance constraints into a Nonlinear Model Predictive Control (NMPC) with Whole-Body Control (WBC) framework for quadrupedal locomotion control. This enables us to use polytopes to describe the shapes of the robot and obstacles for collision avoidance while doing locomotion control of quadrupedal robots. Compared to most prior work, especially using CBFs, that utilize spherical and conservative approximation for obstacle avoidance, this work demonstrates a quadrupedal robot autonomously and safely navigating through very tight spaces in the real world. (Our open-source code is available at github.com/HybridRobotics/quadruped_nmpc_dcbf_duality, and the video is available at youtu.be/p1gSQjwXm1Q.)