基于改进蚁群算法的无人机动态航路规划

UAV Route Planning in Dynamic Environment Based on Modified Ant Colony Opimization

摘要:

研究无人机航路规划,针对基本蚁群算法易于陷入局部最优、规划航路耗时长的问题,对基本蚁群算法进行了改进；引入航路点的动态自适应选择策略和信息素挥发因子动态自适应调整准则,有效克服了基本蚁群算法的不足,并对规划出的航路进行了平滑处理,使其更加满足无人机实际飞行需求；通过仿真分别规划出无人机在静态威胁和动态威胁中的航迹,仿真结果表明,与基本蚁群算法和遗传算法相比,改进的蚁群算法在两种飞行环境中均能规划出较优的航路。

Abstract:

On the research of route planning for UAVs, the basic Ant Colony Optimization algorithm is easily trapped into local optimal and takes very long time to plan the route, so the Ant Colony Optimization is modified in this paper; by introducing dynamic adaptive route selection and pheromone evaporation factor dynamic adaptive criteria, we overcome the shortage of basic Ant Colony Optimization algorithm effectively, at the same time measures are taken to smooth the planned route to make it meet the demand of UAVs’ flight. By simulation, we get the routes in static threat environment and dynamic threat environment respectively, the results show that modified Ant Colony Algorithm can map out optimal routes in both environment.