Constructing and Navigating Connected Air Roads: A Safety-Critical Reinforcement Learning Approach for Multi-UAV Systems

Abstract

This paper presents an integrated control method in air road navigation for multi-UAV systems, combining an efficient reinforcement learning (RL) controller with a control barrier function (CBF)-based filter that guarantees flight safety. First, an air road construction method based on arbitrary quadrilateral combinations is proposed, which enables flexible air road design. Second, two specific CBFs are designed: an air road CBF which keeps UAVs within designed air roads, and a collision avoidance CBF which prevents collisions between UAVs. Based on the CBF-based filter, the RL controller is allowed to be trained in a simple, single-agent environment, which reduces computational costs and enhances training efficiency. Furthermore, the RL reward is carefully designed, which considers both the stability during movement and the optimality of energy conservation. The performance, safety, and efficiency of the proposed approach are rigorously validated through comprehensive simulations and real-world experiments.