电力巡检中的无人机群路径规划算法  

UAVs Routing Planning Algorithm in Power Inspection

作　　者：李晓辉[1] 张路 刘传水 赵毅 董媛 LI Xiao-Hui;ZHANG Lu;LIU Chuan-Shui;ZHAO Yi;DONG Yuan(School of Electronics and Control Engineering,Chang’an University,Xi’an 710064,China;North China Petroleum Steel Pipe Co.Ltd.,CNPC Bohai Equipment Manufacturing Co.Ltd.,Cangzhou 062658,China)

机构地区：[1]长安大学电子与控制工程学院,西安710064 [2]渤海装备华油钢管有限公司,沧州062658

出　　处：《计算机系统应用》2022年第3期241-247,共7页Computer Systems & Applications

基　　金：国家重点研发计划(2020YFB1600400)。

摘　　要：随着无人机技术的飞速发展,无人机被广泛用于各种领域的巡检任务.近年来,电力网络的规模和长度都在快速增长,无人机因其独特的性能和优势成为了电力巡检的首选,无人机巡检不仅能保证安全性,还能有效地提高巡检效率,而路径规划是其在实际应用中的关键一步.本文提出了一种新的混合元启发式方法,用于解决电力巡检中带有多个站点的无人机群路径规划问题.该算法在自适应大邻域搜索的框架下添加变邻域下降为下属策略,加强邻域搜索能力,增加找到更优解的可能.实验结果表明,本文提出的算法能够有效地解决该问题,并且具有较好的稳定性和鲁棒性.另外,通过实验对比了本算法和其他元启发式算法,验证了本算法能有效地减少巡检中使用的无人机数量和时间成本.With the rapid development of unmanned aerial vehicle(UAV) technology, UAVs are widely used in inspection tasks of various fields. In recent years, the scale and length of power networks have been growing rapidly, and UAVs have become the first choice for power inspection due to their unique performance and advantages. They can not only ensure safety, but also effectively improve inspection efficiency. Regarding inspection tasks, the path planning of UAVs is crucial in practical application. In this study, a new hybrid meta-heuristic algorithm is proposed to solve the UAVs routing planning problem with multiple depots in power inspection. In the framework of adaptive large neighborhood search, the variable neighborhood descent strategy is added to enhance the neighborhood search ability and increase the possibility of finding a better solution. Experimental results show that the proposed algorithm can effectively solve the problem and has good stability and robustness. In addition, the proposed algorithm is compared with other metaheuristic algorithms experimentally, and the comparison results verify that this algorithm can effectively reduce the number and time cost of UAVs used in inspection.

关 键 词：电力巡检 无人机巡检 多站点的无人机群路径规划 自适应大邻域搜索算法 变邻域下降 

分 类 号：TM75[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]