Multi-UAV cooperative swarm algorithm in air combat based on predictive game tree

Abstract

<p indent="0mm">An unmanned aerial vehicle (UAV) broadly refers to all kinds of remotely controlled aerial vehicles that do not require pilots to board and drive. Due to its small size, low cost, large quantity, and safety, a UAV is widely used in modern air combat. However, current studies on UAV air combat algorithms are mostly conducted in highly simplified scenarios with low precision. In addition, most of the methods used in these studies are limited by existing expert knowledge and cannot fully exploit the advantages of intelligent air combat algorithms. Therefore, this study investigates a multi-UAV cooperative swarm algorithm in air combat based on the predictive game tree. First, Unity3D is used to build a simulation environment close to a real air combat scene. In addition, a human-machine interaction environment, including UI, VR, weather system, and multifunctional screen, is realized. Then, a set of tactical maneuvers is encapsulated on the basis of existing air combat knowledge. Scripted aircraft formations are realized, and a set of air combat evaluation functions is designed. On this basis, an air combat artificial intelligence framework based on the predictive game tree is proposed. This algorithm completes the task of role assignment and maneuver decision-making, and XGBoost is used to transform it into an online real-time algorithm. Using the state machine algorithm as the baseline, the effectiveness of our air combat algorithm is verified through air combat confrontation experiments on a high-precision simulation platform.