Reinforcement Learning-Based Optimal Formation Tracking for UAVs With Safety Constraints

This article presents a control method for multiple fixed-wing UAVs (unmanned aerial vehicles, or drones) that need to fly together in formation while avoiding collisions and handling unpredictable disturbances. The approach uses reinforcement learning (a type of AI that learns by trial and error) combined with control barrier functions (mathematical tools that enforce safety constraints) to create a system that keeps the UAVs safe and stable while optimizing their performance.