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

2025-07-19

AI Summary

To address the lack of formal safety guarantees for mobile robot navigation in unknown cluttered environments, this paper proposes a Model Predictive Contouring Control (MPCC) framework integrating Control Lyapunov Functions (CLFs) and Control Barrier Functions (CBFs). The method constructs a geometry-aware safety corridor from the free space in the trajectory neighborhood, explicitly encoding obstacle-avoidance constraints. Crucially, it introduces Soft Actor-Critic (SAC) reinforcement learning for the first time to enable online, adaptive tuning of CBF parameters—balancing safety enforcement with control feasibility. Within a constrained optimization formulation, the framework jointly ensures closed-loop stability and forward invariance of the safe set. Extensive simulations and real-robot experiments demonstrate significant improvements in unknown cluttered environments: obstacle-avoidance success rate increases by 23.6%, and trajectory tracking accuracy improves with a 41.2% reduction in RMSE—achieving a unified optimization of safety and agility.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Ensuring safe navigation in unknown cluttered environments
• Providing formal safety assurances for MPCC methods
• Adapting CBF parameters dynamically for feasibility

Innovation

• CLF and CBF enabled MPCC framework
• Dynamic CBF adaptation via SAC policy
• Free-space corridor safety constraints