无人机群协同作战中,如何确定各无人机的航迹是整个规划问题的基础和关键,直接影响到作战效率.采用层次分解策略,首先对威胁场进行Voronoi图环境建模,然后利用改进蚁群算法,提出带有方向性引导性的信息素更新策略,减小迷失蚂蚁对算法收敛性的影响.同时,从时域和空域方面考虑多机协同问题,在满足最小时间窗基础上,最后仿真得到了航迹规划层上多无人机的协同航迹.结果表明:该算法有效地克服了早熟停滞现象,解决了求解多样性问题,并加快了算法的求解效率.%For the cooperative missions of UAVs, how to determine the trajectories of each UAV is the basis and key for the whole planning, and has direct influence on operational effectiveness. A hierarchical decomposition strategy was adopted to solve the initial multi-path problem. First,the Voronoi Diagram was created according to the known threat sources. Then an improved ant colony algorithm with direction guiding policy was developed, which could reduce the effect of lost ants on convergence property. Additionally, the temporal and spatial cooperation were analyzed. Finally simulation was carried out on cooperative path planning with time window constraints. The results show that the proposed method can overcome the premature stagnation, resolve the diversity problem and improve the efficiency of the algorithm.
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