基于混合蚁群算法的无人机航路规划

Path Planning for UAV Based on Mixed Ant Colony Algorithm

无人机(UAV)航路规划的热点和难点在于如何满足安全性和实时性的同时,兼顾全局路径规划和局部路径重规划,以提高无人机的作战效率和生存概率。针对这一问题,在现有无人机航路规划研究基础之上,提出采用蚁群算法与人工势场法相结合的方法。蚁群算法用于全局航路规划,人工势场法用于局部路径重规划。仿真结果表明,两种算法结合所得优化航路较好反映了算法的有效性,可以为航路规划辅助决策研究提供借鉴和参考。

The key and difficult problem of UAV path planning is how to satisfy safety and real-time environment,meanwhile,a global path-planning and a local path-planning are considered to improve operational efficiency and survival probability.For this question,according to the existing research of UAV path planning,the method of synthesizing Ant Colony Algorithm（ACA） and Artificial Potential Field（APF） was discussed.ACA was used as a global route-planning algorithm,and APF was used as a local route-planning algorithm.Simulation results verify that the efficiency of the algorithm can provide some reference value to related researchers.