Unmanned aircraft vehicle path planning based on image skeleton and greedy algorithm

To ensure the mission success rate for low attitude penetration, a path to meet the restriction of the unmanned aerial vehicle-s(UAV) capability with high survivability must be planned.By improved the traditional artificial potential field approach, a novel method was presented based on image skeleton and the greedy algorithm generating optimal UAV flight paths over hostile territory. A weighted graph was generated by skeletonizing the permitted area and calculating flight cost of the skeleton branches based on a threat model. The rough estimate of flight path was given by searching in the graph using Dijkstra algorithm. The flight path was dynamically estimated through minimization of an objective function. The minimization was done efficiently using an improved greedy algorithm with the curvature controllable for local optimization to meet the requirements of the minimum turning radius and the shortest range. The experimental results demonstrate the potential of the proposed method.