雷达威胁环境下的多无人机协同航迹规划

针对雷达威胁环境下的多无人机协同航迹规划问题，提出一种基于Voronoi图与蚁群算法结合的智能规划方法. 根据雷达威胁分布建立赋权Voronoi 图，将连续可飞空域离散化为网格点. 通过选取适当的参数放宽蚁群算法的最优性，将每架无人机寻优得到的多组解作为多条备选航迹，建立以协同时间为约束的协同函数，根据协同时间最优决策方法选出每架无人机的最终飞行航迹，进行航迹平滑处理后得到实际可飞航迹. 仿真结果表明，所提出的智能航迹规划方法具有时间协同和整体最优等优点.

Collaborative Path-Planning of Multiple UAV in Radar Threatening Environment

To solve the problem of collaborative path planning of multiple unmanned aerial vehicles (UAV) under radar threatening environment, an intelligent method based on Voronoi diagram and ant colony optimization (ACO) algorithm is proposed. According to known radar threat sources, a weighted Voronoi diagram
is created to discretize the flying space into a grid. ACO is then used to obtain a set of selectable paths for each UAV. The parameters are properly chosen to relax the optimality of ACO so that multi-solutions can be found. A coordination function is created, and the estimated team arrival time (ETA) is taken as the time constraint to pick out the final path of each UAV. The path is smoothed for actual flight. Simulation results show that the proposed method can satisfy the requirements of time coordination and overall optimal.