基于并行蚁群算法的无人机协同任务/航迹规划应用研究

无人机协同任务/航迹规划问题具有多类复杂的约束条件,针对该问题本文提出并行蚁群算法的求解思路。首先采用蚁群算法构造无人机航迹的解空间,然后对解空间提出基于整数编码的遗传算法,对参与作战的无人机、目标任务、可选航迹进行编码,来提高解空间的求解效率。本文以无人机的SEAD任务为想定,对单任务进行了仿真实验。结果表明,并行蚁群算法可以有效地解决无人机协同任务/航迹规划问题,满足各类约束条件,提高问题解的可行性。

Research of UAV Cooperative Task / Track Planning Based on Parallel Ant Colony Algorithm

The cooperative task/track planning problem of UAV has many complex constraints. In this paper, the solution of parallel ant colony algorithm is proposed. Firstly, solution space of UAV track is constructed by ant colony algorithm. Then, the integer coding based genetic algorithm is proposed for the solution space, coding the UAV, the target task and the alternative track to improve the efficiency of the solution. The SEAD task of unmanned aerial vehicle (UAV) is determined for simulating the single task in this paper. The experimental results show that the parallel ant colony algorithm can effectively solve the UAV cooperative task/track planning problem, satisfy all kinds of constraints and improve the feasibility of the problem solution