Research On Master Slave UAV Formation Path Planning Based On Positioning

This article focuses on the localization based UAV formation trajectory planning problem in electronic reconnaissance systems.Based on the master-slave UAV formation control scheme,a UAV formation route planning method and a positioning accuracy oriented UAV formation optimization method are proposed.The main research content of this article includes:To deal with the issue that the formation shape of UAV formations can affect the positioning accuracy during time difference positioning,this article takes the Cramer Rao Lower Bound(CRLB)as the optimization objective in terms of positioning accuracy,and introduces it into the UAV formation cost system along with distance cost and threat cost,so as to maintain a good formation shape during the flight process and ensure the accuracy of time difference positioning.At the same time,a master-slave UAV formation control model is introduced to simplify the solution of formation route planning problems.In terms of route planning for master UAV: In static environments,in response to the shortcomings of traditional sparse A* algorithms such as poor accuracy,susceptibility to a large number of redundant points,and susceptibility to dimensional explosion in large map environments,adaptive step size and particle swarm optimization node selection strategies are introduced to propose a hybrid A*algorithm to improve the effectiveness and efficiency of route planning.Corresponding obstacle avoidance strategies are also proposed for obstacle group environments;In a dynamic environment with sudden obstacles,in response to the shortcomings of traditional artificial potential field(APF)such as unreachable targets and local minima,the traditional APF is optimized by improving the potential field function and introducing a vertical force field to have better dynamic adaptability and achieve local route re planning.Finally,simulation experiments show that the hybrid A* algorithm has better comprehensive performance in static environments and can achieve good planning results even in obstacle swarm environments;In a dynamic environment,the improved APF can generate reliable reprogrammed tracks.To deal with the optimization problem of UAV formation for target positioning accuracy,Based on the Multi objective Particle Swarm Optimization(MPSO)algorithm,a multi-objective optimization strategy,particle mixing update strategy,non inferior solution advantage selection strategy,and Y-type station layout strategy are introduced into the algorithm,and an improved Multi objective Quantum having Particle Swarm Optimization(IMQPSO)is proposed.Introducing a spherical coordinate system to simplify the mathematical model of slave UAV route planning.The final simulation results show that the IMQPSO algorithm can plan a reliable trajectory with good positioning performance.Compared to the MPSO algorithm,the overall cost is reduced by 4.7% while the algorithm running time is basically the same.Compared to the Multi objective Quantum having Particle Swarm Optimization(MQPSO)algorithm,the overall cost is reduced by 1.4%.