Multi-UAV Cooperative Offensive Combat Intelligent Planning Based on Deep Reinforcement Learning

Abstract

Unmanned aerial vehicle(UAV)with the advantages of high effectiveness and flexible autonomy has gradually replaced manned aircraft to combat,and multi-UAV cooperative combat mission planning becomes the hot research issue.An end-to-end cooperative attack intelligent planning method for multi-UAV based on deep reinforcement learning(DRL)is presented to overcome the shortcomings of traditional mission planning algorithms,such as static dependence,low-dimensional simple scenarios and slow on-board computing speed.The suppression of enemy air defense(SEAD)mission planning is modeled as the Markov decision process.The SEAD intelligent planning model based on proximal policy optimization(PPO)algorithm is established,and two groups of experiments are used to verify the effectiveness and robustness of the intelligent planning model.The results show that the DRL-based intelligent planning method can realize fast and fine planning,adapt to unknown,continuous and high-dimensional environment situation.The SEAD intelligent planning model has the capacity of tactics cooperative planning.

Keywords

• multi-uav
• deep learning
• deep reinforcement learning
• ppo algorithm
• generalization
• cooperative combat