Autonomous Maneuver Decision Making of Dual-UAV Cooperative Air Combat Based on Deep Reinforcement Learning

Abstract

Autonomous maneuver decision making is the core of intelligent warfare, which has become the main research direction to enable unmanned aerial vehicles (UAVs) to independently generate control commands and complete air combat tasks according to environmental situation information. In this paper, an autonomous maneuver decision making method is proposed for air combat by two cooperative UAVs, which is showcased by using the typical olive formation strategy as a practical example. First, a UAV situation assessment model based on the relative situation is proposed, which uses the real-time target and UAV location information to assess the current situation or threat. Second, the continuous air combat state space is discretized into a 13 dimensional space for dimension reduction and quantitative description, and 15 typical action commands instead of a continuous control space are designed to reduce the difficulty of UAV training. Third, a reward function is designed based on the situation assessment which includes the real-time gain due to maneuver and the final combat winning/losing gain. Fourth, an improved training data sampling strategy is proposed, which samples the data in the experience pool based on priority to accelerate the training convergence. Fifth, a hybrid autonomous maneuver decision strategy for dual-UAV olive formation air combat is proposed which realizes the UAV capability of obstacle avoidance, formation and confrontation. Finally, the air combat task of dual-UAV olive formation is simulated and the results show that the proposed method can help the UAVs defeat the enemy effectively and outperforms the deep Q network (DQN) method without priority sampling in terms of the convergence speed.