基于遗传算法的多无人机塔杆巡检任务分配算法研究
Research on Task Allocation Algorithm for Multi-UAV Pole Inspection Based on Genetic Algorithm
吴张傲1
作者信息
- 1. 国网湖北省电力有限公司荆州供电公司,湖北荆州 434000
- 折叠
摘要
"无人机+巡检车"作为一种新的电力系统巡检方式,受到广泛关注.驻车点的坐标、多无人机巡检所管辖区域内塔杆的分配是决定巡检效率的关键.首先,基于K-means聚类算法找到了所有塔杆坐标的聚类中心,即驻车点坐标;然后,以平均分配无人机巡检时间为目标函数搭建了无人机分配任务数学模型;最后,采用遗传算法优化目标函数,得到了最优无人机巡检任务分配.仿真算例表明,通过该方法分配的无人机的巡检时间十分接近,提高了巡检效率.
Abstract
The combination of"UAV+inspection vehicle"as a new inspection method has gained widespread at-tention.The coordinates of parking points and the allocation of poles within the jurisdictional area of multiple UAVs are crucial for inspection efficiency.Firstly,based on the k-means clustering algorithm,the clustering cen-ters of all pole coordinates,namely the coordinates of parking points,are identified.Then,a mathematical model for UAV task allocation is built with the objective function of evenly distributing UAV inspection time.Finally,the genetic algorithm is employed to optimize the objective function and achieve the optimal allocation of UAV in-spection tasks.Simulation examples demonstrate that the inspection times allocated to UAVs through this method are very close,thereby improving inspection efficiency.
关键词
无人机巡检/任务分配/聚类算法/遗传算法Key words
UAV inspection/task allocation/clustering algorithm/genetic algorithm引用本文复制引用
出版年
2024
NETL
NSTL
万方数据