Trajectory planning for multiple UAVs in three-dimensional suppression of enemy air defense missions

Abstract

Multiple unmanned aerial vehicles (UAVs) performing suppression of enemy air defense (SEAD) missions need to plan their flight trajectories, taking into account flight distance and energy consumption. Our goal is to provide a solution to the UAV trajectory planning problem in a three-dimensional (3D) environment. We model the task in the context of the SEAD mission, where UAV range trajectories, flight energy consumption, and threat values are used as optimization objectives. To solve the model and overcome the inefficiency of traditional ant colony optimization (ACO) algorithms in dealing with complex UAV trajectory planning problems in a 3D environment, we propose the Improved ant colony optimization (IACO) algorithm for multi-UAV trajectory planning. First, we improve the transfer probability function, heuristic function, and pheromone updating rule of the original ACO algorithm to improve the practicality of the algorithm in complex environments. Second, the unevenly distributed initial pheromone and the turn angle restriction are introduced to make the planning trajectory more consistent with the actual flight requirements of UAVs. Finally, simulation comparison experiments are carried out in a 3D environment with the background of multiple UAVs performing SEAD tasks cooperatively. The experimental results show that the IACO algorithm performs better, with planned trajectories passing through fewer nodes and achieving improved performance in terms of duration, energy consumption, and threat value. It effectively solves the problem of multiple UAV trajectory planning for SEAD-based missions.