Improved Heuristic Algorithm-based Uav Route Planning

In recent years,considerable attention has been focused on UAV(unmanned aerial vehicle) technology. UAV has already been used in many military and civil applications. Path planning problem is one of core content of UAV technology. The problem is addressed in this paper. An improved heuristic algorithm is proposed to solve three dimensional path planning problem. Firstly, UAV flying space was translated to two dimensionnal surface by the virtual terrain method. Then heuristic A* algorithm is employed to computing the feasible path.In real flying scenario, terrain information, threat distribution and intensity, and the UAV maneuverability should be taken into accout. The above information should be modeled, and handled based on digital map. The digital map technology is also discussed in the paper such as linear interpolation and terrain smoothing. Terrain model is built by DEM (digital elevation model) data which can represent real terrain. UAV model is simplied by simple kinetics. Because radar is the main threat to UAV when flying across the hostile zone, radar threat model is discussed in detail among threat models.Hierarchical method is inrodued to UAV path planning. In a large two dimensional area, the Voronoi diagram is used to partition planning area according to threat distribution. Then the shortest path algorithm is applied to generate path. The path must be smoothed because it may contain sharp turns which UAV can't follow. In view of this, B-Spline method is introduced for path smoothing which makes the generated path fit UAV performance limits. For three dimensional path planning, since three dimensional space will largely reduce computing efficiency. A novel virtual terrain method which incorporates UAV performance limits are used to reduce searching space by an order of magnitude. The developed path planning algorithm, named heuristic A*, is featured with various searching steps and weighting factor for each cost component. The weighting factor can balance the different cost in path planning. The simulation results have been done to validate effectiveness of the algorithm. In the last, the algorithm analysis is presented based on numerical results. In the end of this paper, several approaches are given to UAV path planning problem for further enhancement.