Fine generation method for unmanned aerial vehicle maneuvering flight trajectory based on action feature optimization

The implementation of intelligent maneuver decision-making based on airspace situation information is the key to effectively enhancing the autonomous aerial action capability of unmanned aerial vehicles. However, the maneuver decision-making strategy has problems such as incomplete trajectory optimization theory system and complex rolling iteration solution. This paper proposes an improved method for generating precise maneuvering flight trajectories of unmanned aerial vehicles based on swarm intelligence optimization theory and action feature optimization to address this maneuver decision-making problem. Firstly, based on the intelligent decision-making of tactical maneuvering actions, feature extraction is performed on the maneuvering key points and typical nodes of autonomously selected tactical actions. Then, by designing an evaluation function that meets the requirements of aerial game confrontation tasks, an optimal trajectory control model for specific maneuvering actions is established, and an adaptive intelligent optimization algorithm is used to solve it, ultimately obtaining a refined maneuvering flight trajectory, achieving precise generation of maneuvering trajectories, and completing the entire closed-loop process of intelligent maneuvering decision-making. The simulation results show that the proposed method for generating precise maneuvering flight trajectories of unmanned aerial vehicles can effectively achieve real-time modeling and optimization of tactical maneuvering actions.