Reserch On UAV Path Planning Based On Improved Pigeons-inspired Optimization Algorithm

Unmanned aerial vehicle(UAV)path planning system is the basic guarantee of UAV mission.This paper proposes the improved pigeons-inspired optimization algorithm and the improved particle swarm optimization algorithm to deal with the pre-planning problem of UAV's path in order to plan the optimal path in different envirnment and tasks.Firstly,a detailed experimental analysis of the basic pigeons-inspired optimization algorithm in the UAV path planning is carried out in this paper.It is found that the basic pigeons-inspired optimization algorithm has some drawbacks such as slow convergence rate and falling into the local optimal value.So,this paper proposes that the pigeons-inspired optimization algorithm based on adaptive inertia weight,and proves the advantage of increasing adaptive inertia weight.The adaptive inertia weight is mainly aimed at improving the problem of the slow convergence rate in the geomagnetism operation.The adaptive strategy is used to select the local optimal particle and the global optimal particle in every iteration.Secondly,in order to verify the performance of the improved pigeons-inspired optimization algorithm,this paper compares the improved pigeons-inspired optimization algorithm with the improved swarm algorithm and particle swarm optimization algorithm.However,particle swarm optimization algoritm has many disadvantages such as low convergence rate,and falling into local optimal solution.This paper uses the survival of the fittest strategy,chaos strategy,and linearly decreasing inertia weight to improve the particle swarm optimization algorithm.Then this paper compares particle swarm optimization algorithm with improved pigeons-inspired optimization algorithm.Finally,this paper mainly carries out many experiments in two-dimensional environment and three-dimensional environment respectively.In two-dimensional environment,the adaptived weight pigeons-inspired algorithm has less total distance of path,less threat cost and less running time than other algorithms.In three-dimensional environment,this paper selects twenty typical data for analysis,and analyzes the performance of the algorithm by mean analysis and variance analysis.The results show that the improved pigeons-inspired algorithm has the best average time cost,the particle swarm optimization has the best total distance of path and stability in hill environment.The improved particle swarm optimization algorithm has the best time cost,total distance of path and stability in mountain environment.