Research On Unmanned Aerial Vehicle Path Planning Algorithms

Aiming at the problems of slow convergence speed and long path length in existing UAV path planning algorithms,the WDO algorithm is applied to search the three-dimensional path of UAV.Additionally,the classical PRM algorithm and classical genetic algorithm are improved in this paper.Finally,through the simulation in matlab2014 environment,the simulation results show that the improved algorithm can find a shorter path than the result of classical algorithm in global path planning problems.The improvement of the full text mainly includes the following parts:(1)The WDO algorithm is applied to search the three-dimensional path of UAV,and the results of the WDO algorithm is compared with the results of the particle swarm optimization algorithm.(2)When the locations of obstacles in the global map are known,the initialization method of random points in classical PRM algorithm is improved.The improved initialization points have more precise attributes and play a key role in the final optimal path.Finally,the final path obtained by path search algorithm is smoothed.(3)In the rasterized map,the method of generating the initial path in the application of classical genetic algorithm to the path planning problem is improved,so that the success rate of connecting the starting point and the target point in the genetic algorithm path planning is higher.(4)In the rasterized map,the method of mutation in classical genetic algorithm is improved in the path planning problem,so that a variety of feasible new paths can be generated by mutation,and the probability of success of connecting the starting point and the target point of the new path will be increased.(5)In the rasterized map,the method of interpolation in classical genetic algorithm is improved in the path planning problem,so that the success rate of the path interpolation is improved and the distance of the interpolation path is shorter when the genetic algorithm is applied to the raster path planning.(6)The path derived from the improved genetic algorithm is optimized in the path planning problem,including path redundancy,invalid path point deletion and so on.