Study On Cooperative Path Planning Of Multiple-UAV Based On Intelligent Optimization Algorithm

With the rapid development of military aviation technology in various countries,advanced technologies such as UAV reconnaissance operations have been applied to modern scientific and technological warfare,and research on single UAV mission planning has gradually matured.Faced with the increasingly complex combat environment of modern warfare,single UAV will not be able to cope with various emergencies in the execution of tasks.Therefore,multi-UAV reconnaissance and combat missions will become an important direction for future development of UAV technology,and multi-UAV collaborative missions will gradually become the focus of research by various military powers.UAV path planning refers to planning an effective path with the least comprehensive navigation cost to the target point for the UAV in a complex combat environment,and path planning is one of the most important parts of the UAV to perform combat tasks.At present,there are many contents of single UAV path planning,and the research on multi-UAV path planning is in its infancy.In this paper,we will study the multi-UAV coordinated path planning content based on the multi-UAV coordinated mission.First of all,this paper studies the UAV path planning algorithm in three-dimensional environment.As an intelligent optimization algorithm,particle swarm optimization algorithm has the advantages of simple structure,fewer parameters,and fast search speed.At the same time,it also has problems such as weak global exploration ability,low search accuracy,and easy to fall into the local optimal solution.Aiming at the shortcomings of the particle swarm optimization algorithm,this paper proposes to improve the particle swarm algorithm based on the chicken swarm grouping update strategy.By introducing a group update strategy,all particles are grouped according to the initial fitness,and the optimal particles in the group are selected to guide the group.By grouping particles to explore different regions,each sub-population pays more attention to the search of different global regions at the initial stage of the algorithm iteration,and enhances the algorithm's global exploration capabilities.At the same time,a simulated annealing mechanism is introduced in the sub-group particle search area to improve the local search accuracy of the particles,and the validity of the algorithm is verified by MATLAB simulation.Then,based on the UAV path planning algorithm,the research of multi-UAV cooperative track planning is carried out.This paper proposes a multi-UAV coordinated speed strategy to balance the UAV alternative flight path strategy.The multi-UAV flight path alternative strategy refers to planning multiple feasible trajectories for each UAV,and based on the coordinated time from Select the route from the alternative tracks.In this method,there is a possibility that each drone meets the coordination time,but there may be a large gap in the speed of the whole journey,making it impossible for multiple aircraft to fly in an integrated formation.The cooperative speed strategy refers to finding the speed range of each track and determining the cooperative speed of each drone among the tracks of each drone meeting the coordination time.The introduction of a coordinated speed strategy allows each UAV to maintain a relatively uniform formation during the mission,avoids the UAV moving too fast or too slowly out of formation,and increases the chance that a single UAV cannot escape the threat during flight.According to MATLAB simulation results,according to the cooperative speed,each UAV can select a feasible track with a similar track length and a speed within a small difference range,which effectively improves the quality of multi-UAV formation cooperative tasks.At the same time,the range of cooperative speed can be controlled according to the real-time nature of the mission,providing more choice space for track selection.This paper has 34 figures,8 tables and 82 references.