Research On Cooperative Flight Path Planning Of Multiple Uav Based On Improved Ant Colony Algorithm

Since the 21 st century,with the application of anti-unmanned aerial systems in military battlefields,the modern battlefield environment has become complicated and changeable,which requires multiple drones to cooperate with each other to complete the combat mission.At the same time,as the key to the realization of uav cooperative operation,multi-uav track planning can improve the overall effectiveness of each uav in the execution of combat missions.This paper refers to the improved ant colony algorithm with adaptive diffusion mechanism to study the cooperative trajectory planning of multi-UAV.Firstly,introduce the basic principles of traditional ant colony algorithm,and analyze the reasons that cause the algorithm to converge prematurely and the simulation result is the local optimal solution.Secondly,by setting the pheromone concentration threshold and random pheromone volatilization coefficient based on the traditional ant colony algorithm,and introducing the pheromone diffusion mechanism and roulette selection rules to improve the algorithm,avoid the pheromone distribution in the global path search process.At the same time,multiple ant populations are set in the algorithm for multi-UAV trajectory planning.Then,a comprehensive trajectory cost function that takes into account external environmental threats and UAV maneuverability constraints is established.At the same time,a grid environment model is constructed,and an improved ant colony algorithm is used to simulate multiple UAV trajectories in different threat environments.When planning for multi-UAV coordinated trajectory in 3D space,the minimum threat surface method is used to project the threats inside and outside the3 D space onto the 2D plane.Finally,according to the simulation results,the influence of parameters such as pheromone importance,heuristic factor importance and pheromone volatilization coefficient and the search methods of four-neighborhood and eight-neighborhood on the simulation results of track planning are analyzed.In order to verify the feasibility of the improved ant colony algorithm to solve the problem of multi-UAV trajectory planning and the optimization effect of trajectory planning,this paper uses Matlab to conduct simulation experiments and comparatively analyze the planned trajectory of the traditional ant colony algorithm and the improved ant colony algorithm.According to the simulation results,the improved ant colony algorithm can ensure that each UAV can obtain the optimal planned flight path that satisfies its own maneuver performance and external threat constraints,and theperformance indexes of planned flight path are better than the traditional ant colony algorithm,in which the flight path length is shortened by 4.3% and the algorithm operation time is reduced by 12.5%.