Reinforcement Learning-Based UAV Swarm Fission–Fusion Approach With Real-World Data-Integrated Validation

The motion of unmanned aerial vehicle (UAV) swarms is a complex research area due to the involvement of various system components, including perception, control, and decision-making policies. However, compared to static flight behaviors, the fission–fusion motion of UAV swarms in response to multiple unknown dynamic disturbances has received relatively less attention. This paper proposes a reinforcement learning–based UAV swarm fission–fusion approach with real-world data integrated validation for the swarm’s fission-fusion behavior in response to multiple unknown dynamic disturbances, along with a system validation method utilizing real-world data. The proposed approach effectively integrates fission–fusion dynamics with perception and control to enable UAV swarms to function robustly in the presence of such disturbances. First, we develop a self-organized control framework that facilitates the coordinated motion of multiple UAV swarms. Second, we introduce a reinforcement learning–based fission–fusion confrontation algorithm designed to minimize resource consumption while effectively responding to multiple unknown dynamic disturbances. Finally, we present a real-world data-based validation system based on AirSim, which allows comprehensive evaluation of UAV swarm performance in actual environments. Simulation results demonstrate that when UAV swarms operate in environments with multiple unknown disturbances, they can successfully perform self-organized fission-fusion motion, effectively protecting the parent-swarm from the impact of multiple unknown dynamic disturbances.