A Research On Unman Aerial Vehicle 3D Path Planning Using Probabilistic Roadmap Method

Unmanned aerial vehicle(UAV) attracts more and more attention recently because of its small size, none casualties,good concealment and so on.In application,the UAV should have a ability to find a non-collision path in case of completing tasks precisely.So path planning becomes one of most important step for these applications.Space should be constructed first before path planning.Grid model is widely used because it easy to store and has simple computation.When Space becomes large and complex,grid model will generate lots of redundant nodes and cause very low algorithm efficiency or even useless. Path planning can be classified two-dimention(2D) and three-dimention(3D) in terms of dimension. So far,domestic and foreign brought forward many pat h planning algorithms for UAV in 3D environment,including heuristic algorithm,ant colony algorithm,neural network algorithm and so on.Different algorithms have advantages and disadvantages.Probabilistic Road Map Method(PRM) have many advantages like Configration space can be easy to describe and high-dimetional environment has little influence on algorithm efficiency,So it attracts more and more attentions these years.Algorithm must be highly efficient since UAV path planing needs highly real-time.Octree algorithm can be used consctructing space,because it not only can save lots of storages but also decreases the scope of searching space.The weakness of basic PRM is that it has trouble in finding path through narrow passages.So,I propose an improved PRM algorithm which divides entire Configration space into equal size sub-regions,improving the density of sampling nodes in narrow passage.The results show that the improved PRM algorithm has higher algorithm efficiency and less storage by comparing traditional PRM algorithm and gird model in the narrow passages.