Decentralized and autonomous behavior decision-making for UAV cluster

It is difficult for conventional methods like the diagram theory in a complex environment to carry out modeling and calculation so as to make large-scale cluster behavior decisions. Hence, this paper studies small fixed wings and establishes the decentralized behavior decision-making model for a UAV cluster that has communication limitations and scale ceiling effects. The idea of swarm intelligence is combined with the decoupling multi-agent deep deterministic strategy gradient (DE-MADDPG) for the constructed model to do adaptive learning. Finally, the optimal behavior decision of the UAV cluster is made. Simulations are carried out to verify the model. The consistent movement of the UAV cluster and the maneuvering obstacle avoidance behavior in complex environments are realized. Compared with the MADDPG, the DE-MADDPG exhibits superior precision and real-time capability.