【摘要】 针对传统蚁群算法用于无人机航迹规划时在大空间多维数转弯次数多、收敛速度慢甚至不收敛等问题，提出了一种改进蚁群算法。根据地图构建三维空间模型，采用对空间切片的方式来避免在寻优过程中跨越多个单元格；通过每一代最优路径来更新信息素以及引入距离启发量的策略，增强了算法的收敛性和效率，得出改进蚁群算法相对于传统蚁群算法和快速搜索随机树算法在搜索效率上分别提高了65.9%和18.1%，在平均转弯角度上分别减少了48%和61.2%，在航迹长度上比传统蚁群算法缩短了38.5%的结果。研究所提出的改进蚁群算法能为无人机救灾快速路径规划提供有效的解决方案。更多还原

【Abstract】 An improved ant colony algorithm was proposed to solve the problems of large space,multiple turns and slow or even non-convergence when the traditional ant colony algorithm was used in UAV flight path planning. The three-dimensional space model is constructed according to the map,and the space slice is adopted to avoid spanning multiple cells in the optimization process. The convergence and efficiency of the algorithm were enhanced by updating the pheromone and introducing the distance heuristic strategy in each generation of optimal paths. It was found that the improved ant colony algorithm improved the search efficiency by 65.9% and 18.1%,and reduced the average turning Angle by 48% and61.2%,respectively,compared with the traditional ant colony algorithm and the fast search random tree algorithm. Compared with the traditional ant colony algorithm,the length of the flight path is shortened by 38.5%. The improved ant colony algorithm proposed by the research institute can provide an effective solution for quick route planning of UAV disaster relief.更多还原