Multiple UAVs formation control based on MIX-MAPPO
Single unmanned aerial vehicles(UAVs)struggle to effectively handle complex multi-task scenarios,whereas UAV swarms exhibit significant advantages in addressing such challenges.This paper proposed a drone swarm model based on the Menger sponge fractal to meet the needs of multi-task scenarios and maintaining formation during swarm operation.The model employed multi-agent proximal policy optimization(MAPPO),proximal policy optimization(PPO),and attention mechanism to train the formation control strategy.The approach simplified the establishment of the drone swarm model by assigning weights to all inputs based on each drone's attention to other drones,which enhanced adaptability in dynamic environments.To address the slow convergence and limited adaptability of the MAPPO algorithm with multiple agents,the paper introduced a Menger sponge fractal-based MIX-MAPPO algorithm.Experimental results demonstrate that this method not only achieves stable forma-tions successfully,but also has significantly faster convergence speed and higher reward values compared to algorithms such as DDPG,PPO,MADDPG,and MAPPO.The MIX-MAPPO algorithm thus proves to be superior in the field of swarm control.
万方数据
维普
