文中针对无人飞行器的协同航迹规划,提出了一种基于遗传算法的协同航迹规划算法。该算法采用双层进化机制,不同的飞行器生成各自的可行航迹种群用于下层进化;对不同飞行器的可行航迹进行组合形成协同航迹组种群用于上层进化。实验表明,该算法能够在较短的时间内规划出较优的协同航迹组;同时,由于下层进化中利用了状态表征矩阵和新的引导信息,使得收敛速度比标准遗传算法更快。

A collaborative path planning algorithm for multiple unmanned aerial vehicles (UAV) based on genetic algorithm (GA) was presented, which adopts Double-layer evolution mechanism. Each UAV generates a population containing multiple feasible paths for the evolution of lower layer. The population which contains different combination of specific paths is generated for the evolution of upper layer. Experimental results demonstrate that the proposed algorithm can obtain near-optimal paths quickly and makes the convergence speed faster under the guidance of state matrix and new guide information than standard GA.