Research On The Modeling And Optimization Method Of Mission Planning For UAV Swarm System

The high development of the information technology enhances the operational advantage of the unmanned air vehicles(UAV).Mission planning,as an important content in the field of UAV research,is mainly composed of task allocation and trajectory planning.Among them,task allocation of UAV is to find approximate optimal matching scheme between each UAV and task under certain constraints.Trajectory planning is to find feasible path from a starting point to a target point under certain constraints.Based on the increasing demand of the application market of UAV and the excellent technical about UAV,this thesis aims to study the task allocation and trajectory planning of UAV swarm task planning in a static environment.On the one hand,in known task environment information,an improved ant colony algorithm(Ant Colony Optimization,ACO)is proposed to solve the problem of the high reconnaissance cost of performing the specified task for each UAV in the optimal combination scheme which is searched by the existing task allocation algorithm.Firstly,the task allocation modeling is established under certain constraints.Secondly,the initial optimal solution is searched through the first stage of the improved algorithm,and then the optimal solution is found through the second stage of the task allocation algorithm under the initial optimal solution and the constant reconnaissance cost.The simulation results of the above processes verify that the improved ACO algorithm can quickly find the optimal solution of UAV swarm task allocation,and reduce the reconnaissance cost of each UAV to perform the assigned task while sacrificing the distance cost appropriately.On the other hand,in known planning environment,a method of trajectory planning is proposed to solve these problems of slow convergence and low search efficiency of the related algorithms and the collisions between UAVs and the static obstacles,which includes the initial trajectory generation,the trajectory correction,and smooth trajectory planning.Firstly,an improved algorithm called MACO algorithm which introduces the Metropolis criterion into the node screening mechanism of the ACO algorithm is proposed.The results of experiments show that the improved algorithm can effectively avoid falling into the local optimal and stagnation while searching for the initial trajectory,and can find higher quality trajectory than the existing trajectory planning algorithm.Secondly,three trajectory correction schemes are designed to deal with the problem of collisions between the UAVs and static obstacles which is caused by considering the size of the UAV,so as to further optimize the initial trajectory.The experimental results show that the trajectory modified by the correction scheme successfully avoids all obstacles.Thirdly,the inscribed circle(IC)smooth method is presented to solve the problem of discontinuity caused by the sharp turn.And the simulation results show that the smooth method in saving fuel consumption and improving the safety of trajectory has good performance.These results of above processes demonstrate that the proposed method has high feasibility and effectiveness in searching the optimal solution,avoiding collisions and smoothing the trajectory for UAV trajectory planning.