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

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arxiv 2212.14199 v3 pith:PFWJNX5Q submitted 2022-12-29 cs.RO

Qiayuan Liao , Zhongyu Li , Akshay Thirugnanam , Jun Zeng , Koushil Sreenath This is my paper · ORCID
classification cs.RO
keywords controlquadrupedallocomotionrobotsavoidanceavailableduality-basedexponential
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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.)

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observation

    cs.RO 2026-07 conditional novelty 7.0 of 10

    Constraining the adversarial imitation learning discriminator to discrete-time control barrier functions recovers safety barriers from unlabeled observations and reduces collisions in navigation.

  2. Omni-Perception: Omnidirectional Collision Avoidance for Legged Locomotion in Dynamic Environments

    cs.RO 2025-05 conditional novelty 6.0 of 10

    Omni-Perception is an end-to-end RL policy for legged robots that processes raw LiDAR point clouds with PD-RiskNet to achieve omnidirectional collision avoidance, validated in simulation and on a Unitree G1.

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