基于图像骨架和贪婪算法的无人机航路规划

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

针对无人机在执行低空突防任务时最大生存概率以及自身飞行约束的要求,对传统的人工势场法进行改进,提出基于图像骨架和贪婪算法的航路规划方法.对可飞区域提取图像骨架生成赋权图,采用Dijkstra方法搜索最小代价路径实现航路初规划;提出了曲率可控的贪婪算法对初规划结果进行优化,使最终的路径同时满足最小转弯半径和最短航程的要求.仿真结果表明该方法是一种有效的航路规划方法.

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.