Reinforcement Learning Based UAV Swarm Fission-Fusion Approach with Integrated Validation of Perception and Control

Unmanned Aerial Vehicle (UAV) cluster swarm motion is a complex area of research, mainly because UAV cluster system composition contains multiple system components such as perception and control. However, the split swarm motion of UAV swarms in response to unknown multiple dynamic disturbances has received relatively little attention compared to static flight behavior. In this paper, we propose an integrated verification method of swarm control and perception control for UAV swarms in response to multiple unknown dynamic disturbances through reinforcement learning algorithms, which effectively solves the problem of integrating the method and perception control of swarms in response to multiple unknown dynamic disturbances. First, we develop a self-organized swarm control framework for UAV clusters, which realizes the multi-cluster swarm motion of UAV swarms. Second, we propose a reinforcement learning-based sub-cluster adversarial algorithm, aiming at dynamic confrontation with minimal resource consumption against multiple unknown disturbances. Finally, we introduce an integrated perception and control validation System based on airsim (IPCVSA) that realizes the integrated verification of UAV clusters based on real environments. Simulation experiments show that the UAV swarm can successfully perform self-organized sub-swarm motion when working in an environment with multiple unknown disturbances, effectively protecting the main swarm from multiple dynamic disturbances.