Safety-Critical Path Planning for Obstacle Avoidance Based on Reinforcement Learning and Control Barrier Functions

This article presents a safety-critical control framework for navigation in complex environments with numerous obstacles. An online robust path planning scheme is developed by integrating reinforcement learning (RL) with control barrier functions (CBFs). First, a disturbance observer is designed to estimate the unknown disturbance along with a derived upper bound of the estimation error. Then, a nominal controller is designed using RL, where a critic neural network (NN) structure is established by using state-following (StaF) kernel function. Additionally, by employing a state extrapolation technique, the learning process leverages both real-time and simulated experience data. To ensure safety, obstacle avoidance is formulated as a forward invariance problem of safe sets defined by CBFs. Subsequently, the CBF-based safety-critical constraints are integrated into a quadratic programming (QP) framework to modify the nominal controller. Furthermore, these CBFs are incorporated into a composite CBF using smooth approximation, enabling efficient constraint consolidation. Then, an explicit safe control policy is proposed that guarantees collision-free path planning. Finally, the effectiveness of the proposed scheme is demonstrated through numerical simulations, and comparative results show the advantages over the existing methods in motion trajectory.