Power inspection UAV task assignment matrix reversal genetic algorithm

Kai Liu , Meizhao Liu , Ming Tang , Chen Zhang
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引用次数: 0

Abstract

Traditional manual power inspections are characterized by low efficiency, lengthy processes, and high costs. Existing research on UAV-based power inspections has often overlooked critical factors such as the risk levels of target tasks, the duration of tasks executed by UAVs, and the utility per unit task. To address these gaps, this paper proposes a task allocation method for UAV power inspections based on the Time Window Matrix Reversal Genetic Algorithm (TMGA). Firstly, the proposed cost model accounts for the risk levels of inspection tasks and the impact of low-altitude flight on energy consumption. Secondly, an inspection task allocation model is constructed with the goal of maximizing UAV inspection unit utility. The model is then optimized using two-point crossover and single-point reversal mutation operations, which enhance the UAV unit utility and generate an optimal allocation matrix. The performance of TMGA is evaluated through simulation experiments in three different scenarios, comparing it with existing algorithms. The results show that TMGA outperforms these algorithms in terms of average task time, task completion rate, and unit utility. Specifically, TMGA reduces the average task time by 37% compared to the Cluster Grouping Consensus-base Bundle Algorithm and improves task unit utility by 56.91% compared to the Genetic Algorithm.
电力巡检无人机任务分配矩阵反转遗传算法
传统的人工巡检效率低、流程长、成本高。现有的基于无人机的电力检测研究往往忽略了目标任务的风险水平、无人机执行任务的持续时间以及单位任务的效用等关键因素。针对这些不足,提出了一种基于时间窗矩阵反转遗传算法(TMGA)的无人机电源巡检任务分配方法。首先,提出的成本模型考虑了检查任务的风险水平和低空飞行对能耗的影响。其次,以无人机巡检单元效用最大化为目标,构建了巡检任务分配模型;然后采用两点交叉和单点反转突变操作对模型进行优化,提高了无人机的单位效用,生成了最优分配矩阵。通过三种不同场景下的仿真实验,对TMGA的性能进行了评价,并与现有算法进行了比较。结果表明,TMGA在平均任务时间、任务完成率和单位效用方面优于这些算法。具体来说,TMGA比基于共识的聚类分组算法减少了37%的平均任务时间,比遗传算法提高了56.91%的任务单元利用率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
8.40
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0.00%
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