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METHOD AND APPARATUS FOR JOINT OPTIMIZATION OF MULTI-UAV TASK ASSIGNMENT AND PATH PLANNING

机译:多无人机任务分配和路径规划联合优化的方法和装置

摘要

The embodiments of the present invention disclose a method and apparatus for joint optimization of multi-UAV task assignment and path planning. The method comprises: obtaining the location information of a plurality of UAVs and a plurality of target points, the dispersion of groundspeed course angle, and motion parameters of each UAV and wind field; constructing an initial population based on the location information, the dispersion of groundspeed course angle and a preset genetic algorithm; determining the flight status of each UAV and the flight time taken by each UAV to complete a path segment of the corresponding Dubins flight path based on the initial population and the motion parameters, obtaining the total time taken by all the UAVs corresponding to each chromosome to complete the task based on the flight time of the path segment; and subjecting the chromosomes in the initial population to crossover and mutation based on the genetic algorithm and, when a predetermined number of iterations is reached, selecting the optimal Dubins flight path as the joint optimization result. In the embodiments of the present invention, the UAV flight path planning problem is combined with the actual flight environment of the UAV, so that the optimal flight path obtained is superior to the solution in which the UAV speed is constant.
机译:本发明实施例公开了一种联合优化多UAV任务分配和路径规划的方法和装置。该方法包括:获取多个无人机和多个目标点的位置信息,地速航向角的离散度以及每个无人机和风场的运动参数;根据位置信息,地速航向角的离散度和预设的遗传算法构造初始种群;根据初始种群和运动参数,确定各无人机的飞行状态和完成相应杜宾斯飞行路径的航路段所花费的飞行时间,求出与每个染色体相对应的所有无人机的总时间。根据航路段的飞行时间完成任务;根据遗传算法对初始种群的染色体进行交叉和突变,当达到预定的迭代次数时,选择最优的杜宾斯飞行路径作为联合优化结果。在本发明的实施例中,将无人飞行器的飞行路径规划问题与无人飞行器的实际飞行环境相结合,使得获得的最优飞行路径优于无人飞行器速度恒定的解决方案。

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