曹征领,程亮亮,孙斌,李浩言,王华伟,殷志敏
CAO Zhengling,CHENG Liangliang,SUN Bin,LI Haoyan,WANG Huawei,YIN Zhimin

• 1:浙江泰仑电力集团有限责任公司
• 2:国网浙江省电力有限公司湖州供电公司

摘要(Abstract)：

针对架空线路无人机自主巡检路径规划问题，首先考虑无人机访问目标的时间成本，以巡线时间间隔与缺陷等级的几何平均作为访问节点的收益，并将其等价扩展为OP（定向问题）优化模型，以实现在一定时间内获得更大收益。然后提出一种基于LA（自学习）机制改进PSO（粒子群优化算法）的AGA（自适应遗传算法），来求解该模型在基准实例的最优解，以验证所提模型的有效性。最后以架空线路无人机自主巡检任务为应用背景，验证所提模型与算法的稳定性，且为无人机自主巡检路径规划策略的制定提供参考。
For the problem of autonomous inspection path planning of UAVs(unmanned aerial vehicles) on overhead lines, considering the time cost of access target of UAVs, the geometric mean of the line patrol time interval and the defect level is used as the benefit of visiting nodes, which is equivalently extended to the OP(orientation problem) optimization model, in order to achieve the goal of obtaining greater benefits within a certain time limit.Then, an AGA(adaptive genetic algorithm) based on LA(learning automata) mechanism to improve PSO(particle swarm optimization) is proposed to solve the optimal solution of the proposed model in the benchmark instance and verify the validity of the proposed model. Finally, taking the autonomous inspection task of UAVs on overhead lines as the application background, the stability of the proposed model and algorithm is validated, which provides a reference for the formulation of path planning strategy of autonomous inspection for UAVs.

关键词(KeyWords)： 输电线路;无人机自主巡检;定向问题;自适应遗传算法;路径规划策略
transmission line;autonomous inspection for unmanned aerial vehicles;orientation problem;adaptive genetic algorithm;path planning strategy

Abstract:

Keywords:

基金项目(Foundation): 国网浙江省电力有限公司湖州供电公司泰化集团科技项目（TLGZ2112001）

作者(Author): 曹征领,程亮亮,孙斌,李浩言,王华伟,殷志敏
CAO Zhengling,CHENG Liangliang,SUN Bin,LI Haoyan,WANG Huawei,YIN Zhimin

DOI: 10.19585/j.zjdl.202308011

参考文献(References)：

• [1]隋宇，宁平凡，牛萍娟，等.面向架空输电线路的挂载无人机电力巡检技术研究综述[J].电网技术，2021,45(9):3636-3648.SUI Yu,NING Pingfan,NIU Pingjuan,et al.Review on mounted UAV for transmission line inspection[J].Power System Technology,2021,45(9):3636-3648.
• [2]成亮，杨沛，贾燕翀，等.配网线路无人机自主巡检的路径规划方法[J].无线电工程，2022,52(7):1213-1221.CHENG Liang,YANG Pei,JIA Yanchong,et al. Path planning method for autonomous UAV inspection of distribution network[J].Radio Engineering,2022,52(7):1213-1221.
• [3]孙嫱，汤奕琛，沈如榕.输电线路无人机自主巡检航迹优化研究[J].电工技术，2022(9):77-78.SUN Qiang,TANG Yichen,SHEN Rurong.Research on UAV autonomous patrol track optimization of transmission line[J].Electric Engineering,2022(9):77-78.
• [4]黄郑，王红星，翟学锋，等.输电线路无人机自主巡检方法研究与应用[J].计算技术与自动化，2021,40(3):157-161.HUANG Zheng,WANG Hongxing,ZHAI Xuefeng,et al.Research and application of UAV autonomous inspection method for transmission line[J]. Computing Technology and Automation,2021,40(3):157-161.
• [5]梁文勇，吴大伟，谷山强，等.输电线路多旋翼无人机精细化自主巡检航迹优化方法[J].高电压技术，2020,46(9):3054-3061.LIANG Wenyong,WU Dawei,GU Shanqiang,et al.Optimization method for fine autonomous inspection route of transmission lines by multi-rotor unmanned aerial vehicle[J].High Voltage Engineering,2020,46(9):3054-3061.
• [6]邵瑰玮，刘壮，付晶，等.架空输电线路无人机巡检技术研究进展[J].高电压技术，2020,46(1):14-22.SHAO Guiwei,LIU Zhuang,FU Jing,et al. Research progress in unmanned aerial vehicle inspection technology on overhead transmission lines[J].High Voltage Engineering,2020,46(1):14-22.
• [7]胡志坤，赵超越，王振东，等.基于边缘计算和无人机巡检图像的输电杆塔关键部位隐患智能识别[J].浙江电力，2020,39(10):21-27.HU Zhikun,ZHAO Chaoyue,WANG Zhendong,et al.Intelligent identification of hidden dangers in key parts of transmission towers based on edge calculation and UAV inspection images[J]. Zhejiang Electric Power,2020,39(10):21-27.
• [8]曾懿辉，何通，郭圣，等.基于差分定位的输电线路多旋翼无人机智能巡检[J].中国电力，2019,52(7):24-30.ZENG Yihui,HE Tong,GUO Sheng,et al.Research on multi-rotor UAV intelligent power line inspection based on differential positioning[J]. Electric Power,2019,52(7):24-30.
• [9]卢成阳，王文格.复杂城市环境下无人机三维路径规划[J].计算机系统应用，2022,31(5):184-194.LU Chengyang,WANG Wenge. 3D path planning of UAV in complex urban environment[J]. Computer Systems and Applications,2022,31(5):184-194.
• [10]陈玮光，胡晓静，郑鑫，等.基于改进遗传算法的变电站巡检机器人行驶轨迹优化[J].广东电力，2021,34(6):48-54.CHEN Weiguang,HU Xiaojing,ZHENG Xin,et al.Optimization of traveling track of inspection robot in substation based on improved genetic algorithm[J].Guangdong Electric Power,2021,34(6):48-54.
• [11]孔维仁，周德云，赵艺阳，等.基于深度强化学习与自学习的多无人机近距空战机动策略生成算法[J].控制理论与应用，2022,39(2):352-362.KONG Weiren,ZHOU Deyun,ZHAO Yiyang,et al.Maneuvering strategy generation algorithm for multi-UAV in close-range air combat based on deep reinforcement learning and self-play[J]. Control Theory&Applications,2022,39(2):352-362.
• [12] VANSTEENWEGEN P, SOUFFRIAU W, VAN OUDHEUSDEN D. The orienteering problem:a survey[J].European Journal of Operational Research,2011,209(1):1-10.
• [13] NARENDRA K S,THATHACHAR M A L. Learning automata—a survey[J].IEEE Transactions on Systems,Man,and Cybernetics,1974,4(4):323-334.
• [14] KENNEDY J,EBERHART R.Particle swarm optimization[C]//Proceedings of ICNN'95-International Conference on Neural Networks,November 27-December 1,1995,Perth,WA,Australia.IEEE,2002:1942-1948.
• [15] MIRJALILI S.Genetic algorithm[M]//Evolutionary Algorithms and Neural Networks.Cham,Germany:Springer,2019:43-55.
• [16] VAN LAARHOVEN P J M,AARTS E H L.Simulated annealing[M]//Simulated Annealing:Theory and Applications. Dordrecht,Netherlands:Springer Netherlands,1987:7-15.
• [17] TSILIGIRIDES T.Heuristic methods applied to orienteering[J].Journal of the Operational Research Society,1984,35(9):797-809.
• [18]王浩杰，王晓强，朱其萍，等.基于模拟退火的超声滚挤压工艺参数多目标优化[J].塑性工程学报，2022,29(4):14-22.WANG Haojie,WANG Xiaoqiang,ZHU Qiping,et al.Multi-objective optimization of ultrasonic rolling extrusion process parameters based on simulated annealing[J].Journal of Plasticity Engineering,2022,29(4):14-22.