Corridor-based Adaptive Control Barrier and Lyapunov Functions for Safe Mobile Robot Navigation

TL;DR

This paper introduces a novel control framework combining Lyapunov and Barrier functions with adaptive parameters for safe, efficient mobile robot navigation in complex, unknown environments, validated through simulations and real-world experiments.

Contribution

It presents a general MPCC framework with safety guarantees using CLF and CBF, and introduces adaptive CBF parameters via SAC for improved feasibility.

Findings

Enhanced safety in navigation through corridor-based CBFs

Successful real-world validation on mobile robots

Adaptive CBF parameters improve trajectory feasibility

Abstract

Safe navigation in unknown and cluttered environments remains a challenging problem in robotics. Model Predictive Contour Control (MPCC) has shown promise for performant obstacle avoidance by enabling precise and agile trajectory tracking, however, existing methods lack formal safety assurances. To address this issue, we propose a general Control Lyapunov Function (CLF) and Control Barrier Function (CBF) enabled MPCC framework that enforces safety constraints derived from a free-space corridor around the planned trajectory. To enhance feasibility, we dynamically adapt the CBF parameters at runtime using a Soft Actor-Critic (SAC) policy. The approach is validated with extensive simulations and an experiment on mobile robot navigation in unknown cluttered environments.