Route Planning of UAV Based On Improved GSO Algorithm

GSO(glowworm swarm optimization) as a new kind of heuristic bionic algorithm has been applied in many fields, including robot, function solving and signal processing, etc. This article first applies the algorithm to route planning of unmanned aerial vehicle (UAV). Common used method of route planning are ant colony algorithm, A*algorithm and genetic algorithm, etc. But these methods have the convergence time too long, easy to fall into local optimum and other shortcomings. In order to significantly improve the convergence speed and local search capability, this paper uses the per-path rejection and embedded chaotic operator improved method. This method can accidentally eliminate the wrong choice before the path searching, saving the select time. Meanwhile, when conducting neighborhood search operators choose to use chaotic search for local small-scale, eliminating the possibility of the system into local optimum. From the simulation results, this improved GSO not only able to choose a shorter path, but also can double reduce the convergence time. These all show that this improved algorithm is superior to other bionic algorithm in UAV route planning.