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.
@article{arxiv.2507.14700,
title = {Corridor-based Adaptive Control Barrier and Lyapunov Functions for Safe Mobile Robot Navigation},
author = {Nicholas Mohammad and Nicola Bezzo},
journal= {arXiv preprint arXiv:2507.14700},
year = {2025}
}
Comments
To be presented in the 64th IEEE Conference on Decision and Control (CDC 25)