基于引导蚁群优化的无人机航路规划

Route Planning of Unmanned Aerial Combat Vehicle(UACV)Based on Guided Ant Colony Optimization(GACO)

为了提高无人机在电力系统输电线路巡检中的时效性和准确性,提出了一种基于引导蚁群优化的路径规划方法。通过测量影响UACV飞行的4个因素(即对方雷达探测、飞行燃料成本、最大射程和高度成本),设计了雷达威胁模型、燃料成本模型、最大范围限制模型和高度代价模型。又通过设置诱导制导,改善了蚂蚁系统在局部搜索中的性能。仿真结果表明,该算法能从根本上提高UACV的飞行任务性能。

In order to improve the performance of UAV in surveillance and combat,the authors propose a path planning method based on guided ant colony optimization.By measuring four factors that affect UACV flight(namely,enemy radar detection,flight fuel cost,maximum range and altitude cost),radar threat model,fuel cost model,maximum range limit model and altitude cost model are designed.Moreover,the performance of ant system in local search is improved by setting guidance.The simulation results show that the algorithm can fundamentally improve the mission performance of UACV.