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2000
Volume 18, Issue 8
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Background

The application of Unmanned Aerial Vehicle (UAV) is a major turning point in the history of power line inspection and is of increasing interest to a growing number of researchers.

Objective

This study aimed to clarify the research status, hotspots, and evolutionary trends of UAV power line inspection to help researchers understand the dynamic evolution of research topics and provide guidance for future research directions.

Methods

737 high-quality papers were collected from the Web of Science Core Collection (WoSCC) database from 2001 to 2023, and descriptive statistical analysis, cooperation network analysis, keyword co-occurrence analysis, keyword clustering analysis, and keyword citation burst analysis were conducted using VOSviewer and CiteSpace.

Results

The popularity of research on UAV power line inspection is increasing, with an average annual growth rate of 26.33% in publications between 2001 and 2023. China (444 publications, 60.24%) and USA (77 publications, 10.45%) are the most prominent countries. However, the level of cooperation between different countries, academic institutions, and scholars is low. The research topics are wide-ranging and interdisciplinary, mainly focusing on 4 areas: fault detection and diagnosis, path planning, and the application of deep learning in intelligent inspection. The research hotspot and trend is the integration of artificial intelligence, deep learning, and modern information technology to achieve UAV autonomous intelligent inspection. Some of the challenges faced in the development of the field are also summarized, and possible solutions are proposed.

Conclusion

This study provides a comprehensive and systematic review, and the results provide a quick overview of the research status, hotspots, and evolutionary trends.

© 2025 Bentham Science Publishers
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References

  1. LiZ. ZhangY. WuH. SuzukiS. NamikiA. WangW. Design and application of a UAV autonomous inspection system for high-voltage hower transmission lines.Remote Sens.202315386510.3390/rs15030865
     [Google Scholar]
  2. LuoY. ZhangX. YangD. SunQ. Emission trading based optimal scheduling strategy of energy hub with energy storage and integrated electric vehicles.J. Mod. Power Syst. Clean Energy20208226727510.35833/MPCE.2019.000144
     [Google Scholar]
  3. MatikainenL. LehtomäkiM. AhokasE. HyyppäJ. KarjalainenM. JaakkolaA. KukkoA. HeinonenT. Remote sensing methods for power line corridor surveys.ISPRS J. Photogramm. Remote Sens.2016119103110.1016/j.isprsjprs.2016.04.011
     [Google Scholar]
  4. GuerreiroB.J. SilvestreC. CunhaR. CabecinhasD. LiDAR-based control of autonomous rotorcraft for the inspection of pierlike structures.IEEE Trans. Control Syst. Technol.20182641430143810.1109/TCST.2017.2705058
     [Google Scholar]
  5. MorrellB. ThakkerR. MerewetherG. ReidR. RigterM. TzanetosT. ChamitoffG. Comparison of trajectory optimization algorithms for high-speed quadrotor flight near obstacles.IEEE Robot. Autom. Lett.2018344399440610.1109/LRA.2018.2868866
     [Google Scholar]
  6. ParkJ.Y. LeeJ.K. ChoB.H. OhK.Y. An inspection robot for live-line suspension insulator strings in 345-kV power lines.IEEE Trans. Power Deliv.201227263263910.1109/TPWRD.2011.2182620
     [Google Scholar]
  7. SongL. WangH. ChenP. Automatic patrol and inspection method for machinery diagnosis robot—Sound signal-based fuzzy search approach.IEEE Sens. J.202020158276828610.1109/JSEN.2020.2978396
     [Google Scholar]
  8. HeT. ZengY. HuZ. Research of multi-rotor UAVs detailed autonomous inspection technology of transmission lines based on route planning.IEEE Access2019711495511496510.1109/ACCESS.2019.2935551
     [Google Scholar]
  9. PengX. WangK. XiaoX. WuK. GuW. Broadband satellite communication system in the intelligent inspection of electric power line base on large scale unmanned helicopter.High Volt. Eng2019452368376
     [Google Scholar]
  10. NguyenV.N. JenssenR. RoversoD. Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning.Int. J. Electr. Power Energy Syst.20189910712010.1016/j.ijepes.2017.12.016
     [Google Scholar]
  11. LuoY. YuX. YangD. ZhouB. A survey of intelligent transmission line inspection based on unmanned aerial vehicle.Artif. Intell. Rev.202356117320110.1007/s10462‑022‑10189‑2
     [Google Scholar]
  12. YangL. FanJ. LiuY. LiE. PengJ. LiangZ. A review on state-of-the-art power line inspection techniques.IEEE Trans. Instrum. Meas.202069129350936510.1109/TIM.2020.3031194
     [Google Scholar]
  13. ChenB. MiaoX. Distribution line pole detection and counting based on YOLO using UAV inspection line video.J. Electr. Eng. Technol.202015144144810.1007/s42835‑019‑00230‑w
     [Google Scholar]
  14. CerónA. MondragónI. PrietoF. Onboard visual-based navigation system for power line following with UAV.Int. J. Adv. Robot. Syst.201815210.1177/1729881418763452
     [Google Scholar]
  15. LiX. LiZ. WangH. LiW. Unmanned aerial vehicle for transmission line inspection: status, standardization, and perspectives.Front. Energy Res.2021971363410.3389/fenrg.2021.713634
     [Google Scholar]
  16. WangF. TanB. ChenY. FangX. JiaG. WangH. ChengG. ShaoZ. A visual knowledge map analysis of mine fire research based on CiteSpace.Environ. Sci. Pollut. Res. Int.20222951776097762410.1007/s11356‑022‑20993‑635680744
     [Google Scholar]
  17. BichengD. AdnanN. HarjiM.B. RavindranL. Evolution and hotspots of peer instruction: A visualized analysis using CiteSpace.Educ. Inf. Technol.20232822245226210.1007/s10639‑022‑11218‑x
     [Google Scholar]
  18. XuY. WangY. HeJ. ZhuW. Antibacterial properties of lactoferrin: A bibliometric analysis from 2000 to early 2022.Front. Microbiol.20221394710210.3389/fmicb.2022.94710236060777
     [Google Scholar]
  19. CroninB. Bibliometrics and beyond: Some thoughts on web-based citation analysis.J. Inf. Sci.20012711710.1177/016555150102700101
     [Google Scholar]
  20. XuZ. YuD. A Bibliometrics analysis on big data research (2009–2018).J. Data. Infor. Manag.201911-231510.1007/s42488‑019‑00001‑2
     [Google Scholar]
  21. SharifiA. SimanganD. KanekoS. Three decades of research on climate change and peace: A bibliometrics analysis.Sustain. Sci.202116410791095
     [Google Scholar]
  22. ChoudhriA.F. SiddiquiA. KhanN.R. CohenH.L. Understanding bibliometric parameters and analysis.Radiographics201535373674610.1148/rg.201514003625969932
     [Google Scholar]
  23. ChenC. Searching for intellectual turning points: Progressive knowledge domain visualization.P. Natl. Acad. Sci.2004101S15303531010.1073/pnas.0307513100
     [Google Scholar]
  24. XuY. LyuJ. LiuH. XueY. A bibliometric and visualized analysis of the global literature on black soil conservation from 1983–2022 based on CiteSpace and VOSviewer.Agronomy20221210243210.3390/agronomy12102432
     [Google Scholar]
  25. ChenC. HuZ. LiuS. TsengH. Emerging trends in regenerative medicine: A scientometric analysis in CiteSpace.Expert Opin. Biol. Ther.201212559360810.1517/14712598.2012.67450722443895
     [Google Scholar]
  26. MarkscheffelB. SchröterF. Comparison of two science mapping tools based on software technical evaluation and bibliometric case studies.COLLNET J. Scientometr. Inform. Manag.202115236539610.1080/09737766.2021.1960220
     [Google Scholar]
  27. ZhangJ. QuoquabF. MohammadJ. Plastic and sustainability: A bibliometric analysis using VOSviewer and CiteSpace.Arab Gulf J. Sci. Res.20244214467
     [Google Scholar]
  28. WuH. LiY. TongL. WangY. SunZ. Worldwide research tendency and hotspots on hip fracture: A 20-year bibliometric analysis.Arch. Osteoporos.20211617310.1007/s11657‑021‑00929‑233866438
     [Google Scholar]
  29. BrodyT. HarnadS. CarrL. Earlier Web usage statistics as predictors of later citation impact.J. Am. Soc. Inf. Sci. Technol.20065781060107210.1002/asi.20373
     [Google Scholar]
  30. LiY. DuQ. ZhangJ. JiangY. ZhouJ. YeZ. Visualizing the intellectual landscape and evolution of transportation system resilience: A bibliometric analysis in CiteSpace.Develop. Built Environ.20231410014910.1016/j.dibe.2023.100149
     [Google Scholar]
  31. van EckN.J. WaltmanL. Citation-based clustering of publications using CitNetExplorer and VOSviewer.Scientometrics201711121053107010.1007/s11192‑017‑2300‑728490825
     [Google Scholar]
  32. Van EckN. WaltmanL. Software survey: VOSviewer, a computer program for bibliometric mapping.Scientometrics2010842523538
     [Google Scholar]
  33. NandiyantoA.B.D. Al HusaeniD.F. A bibliometric analysis of materials research in Indonesian journal using VOSviewer.J. Eng. Res-Kuwait2021202110.36909/jer.ASSEEE.16037
     [Google Scholar]
  34. OyewolaD.O. DadaE.G. Exploring machine learning: A scientometrics approach using bibliometrix and VOSviewer.SN Appl. Sci.20224514310.1007/s42452‑022‑05027‑735434524
     [Google Scholar]
  35. ChangL. WatanabeT. XuH. HanJ. Knowledge mapping on Nepal’s protected areas using CiteSpace and VOSviewer.Land2022117110910.3390/land11071109
     [Google Scholar]
  36. ZhavoronokA. ChubA. YakushkoI. KotelevetsD. LozychenkoO. KupchyshynaO. Regulatory policy: Bibliometric analysis using the VOSviewer program.IJCSNS Int. J. Comp. Sci. Network Secur.2022223948
     [Google Scholar]
  37. KatochR. IoT research in supply chain management and logistics: A bibliometric analysis using vosviewer software.Mater. Today Proc.2022562505251510.1016/j.matpr.2021.08.272
     [Google Scholar]
  38. GuoY. GengX. ChenD. ChenY. Sustainable building design development knowledge map: A visual analysis using CiteSpace.Buildings202212796910.3390/buildings12070969
     [Google Scholar]
  39. HuangR. YangX. Analysis and research hotspots of ceramic materials in textile application.J. Ceram. Process. Res.2022233312319
     [Google Scholar]
  40. ZhuH. ZhangY. QuB. LiaoQ. WangH. GaoR. Thermodynamic characteristics of methane adsorption about coking coal molecular with different sulfur components: Considering the influence of moisture contents.J. Nat. Gas Sci. Eng.20219410405310.1016/j.jngse.2021.104053
     [Google Scholar]
  41. TianD. ShiZ. A two-stage hybrid probabilistic topic model for refining image annotation.Int. J. Mach. Learn. Cybern.202011241743110.1007/s13042‑019‑00983‑w
     [Google Scholar]
  42. WuM. WangY. YanC. ZhaoY. Study on subclinical hypothyroidism in pregnancy: A bibliometric analysis via CiteSpace.J. Matern. Fetal Neonatal Med.202235355656710.1080/14767058.2020.172973132106735
     [Google Scholar]
  43. WangW. LuC. Visualization analysis of big data research based on Citespace.Soft Comput.202024118173818610.1007/s00500‑019‑04384‑7
     [Google Scholar]
  44. ZhangY. HeH. KhandelwalM. DuK. ZhouJ. Knowledge mapping of research progress in blast-induced ground vibration from 1990 to 2022 using CiteSpace-based scientometric analysis.Environ. Sci. Pollut. Res. Int.2023304710353410355510.1007/s11356‑023‑29712‑137707731
     [Google Scholar]
  45. XuZ. ShaoT. DongZ. LiS. Research progress of heavy metals in desert—visual analysis based on CiteSpace.Environ. Sci. Pollut. Res. Int.20222929436484366110.1007/s11356‑022‑20216‑y35426556
     [Google Scholar]
  46. LeeY. LeeY. SeongJ. StanescuA. HwangC.S. A comparison of network clustering algorithms in keyword network analysis: a case study with geography conference presentations.Int.J. Geospat. Environ. Res.2020731
     [Google Scholar]
  47. JoungJ. KimK. Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data.Technol. Forecast. Soc. Change201711428129210.1016/j.techfore.2016.08.020
     [Google Scholar]
  48. ZhangZ. Visualization analysis of the development trajectory of knowledge sharing in virtual communities based on CiteSpace.Multimedia Tools Appl.20197821296432965710.1007/s11042‑018‑6061‑y
     [Google Scholar]
  49. XuC. YangT. WangK. GuoL. LiX. Knowledge domain and hotspot trends in coal and gas outburst: A scientometric review based on CiteSpace analysis.Environ. Sci. Pollut. Res. Int.20223011290862909910.1007/s11356‑022‑23879‑936401701
     [Google Scholar]
  50. AmjadT. ShahidN. DaudA. KhatoonA. Citation burst prediction in a bibliometric network.Scientometrics202212752773279010.1007/s11192‑022‑04344‑3
     [Google Scholar]
  51. BalestrieriE. DaponteP. De VitoL. LamonacaF. Sensors and measurements for unmanned systems: An overview.Sensors2021214151810.3390/s2104151833671642
     [Google Scholar]
  52. YasinJ.N. MohamedS.A.S. HaghbayanM.H. HeikkonenJ. TenhunenH. PlosilaJ. Unmanned aerial vehicles (uavs): Collision avoidance systems and approaches.IEEE Access2020810513910515510.1109/ACCESS.2020.3000064
     [Google Scholar]
  53. NebikerS. AnnenA. ScherrerM. OeschD. A light-weight multispectral sensor for micro UAV—Opportunities for very high resolution airborne remote sensing.Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci200837B111931200
     [Google Scholar]
  54. OmarT. NehdiM.L. Remote sensing of concrete bridge decks using unmanned aerial vehicle infrared thermography.Autom. Construct.20178336037110.1016/j.autcon.2017.06.024
     [Google Scholar]
  55. WangZ. GaoQ. XuJ. LiD. A review of UAV power line inspection.Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020, October 23–25, 2020, Tianjin, China, pp.3147-3159, 202210.1007/978‑981‑15‑8155‑7_263
     [Google Scholar]
  56. ZhangD. WatsonR. MacLeodC. DobieG. GalbraithW. PierceG. Implementation and evaluation of an autonomous airborne ultrasound inspection system.Nondestruct. Test. Eval.202237112110.1080/10589759.2021.1889546
     [Google Scholar]
  57. ChenC. JinA. YangB. MaR. SunS. WangZ. ZongZ. ZhangF. DCPLD-Net: A diffusion coupled convolution neural network for real-time power transmission lines detection from UAV-Borne LiDAR data.Int. J. Appl. Earth Obs. Geoinf.202211210296010.1016/j.jag.2022.102960
     [Google Scholar]
  58. TakayaK. OhtaH. KroumovV. ShibayamaK. NakamuraM. Development of UAV system for autonomous power line inspection.2019 23rd International Conference on System Theory, Control and Computing (ICSTCC), 09-11 October 2019, Sinaia, Romania, 2019.10.1109/ICSTCC.2019.8885596
     [Google Scholar]
  59. GuanH. SunX. SuY. HuT. WangH. WangH. PengC. GuoQ. UAV-lidar aids automatic intelligent powerline inspection.Int. J. Electr. Power Energy Syst.202113010698710.1016/j.ijepes.2021.106987
     [Google Scholar]
  60. ZhaiY. WangD. ZhangM. WangJ. GuoF. Fault detection of insulator based on saliency and adaptive morphology.Multimedia Tools Appl.2017769120511206410.1007/s11042‑016‑3981‑2
     [Google Scholar]
  61. ZhaoZ. LiuN. WangL. Localization of multiple insulators by orientation angle detection and binary shape prior knowledge.IEEE Trans. Dielectr. Electr. Insul.20152263421342810.1109/TDEI.2015.004741
     [Google Scholar]
  62. WuQ. AnJ. LinB. A texture segmentation algorithm based on PCA and global minimization active contour model for aerial insulator images.IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.2012551509151810.1109/JSTARS.2012.2197672
     [Google Scholar]
  63. LiaoS. AnJ. A robust insulator detection algorithm based on local features and spatial orders for aerial images.IEEE Geosci. Remote Sens. Lett.201512596396710.1109/LGRS.2014.2369525
     [Google Scholar]
  64. ZhaiY. ChenR. YangQ. LiX. ZhaoZ. Insulator fault detection based on spatial morphological features of aerial images.IEEE Access20186353163532610.1109/ACCESS.2018.2846293
     [Google Scholar]
  65. JiangW. MaX. ChuJ. GaoL. LiM. HanJ. Research on transmission line sensing technology and interconnection capability enhancement methods.4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), pp.581-588, 2022.10.1117/12.2640360
     [Google Scholar]
  66. LiC. ChenX. LiZ. ZhangY. YueK. MaJ. WangM. ZhaoL. QiE. Comprehensive evaluation method of transmission line operating status based on improved combination weighting evaluation model.Energy Rep.2022838739710.1016/j.egyr.2022.01.207
     [Google Scholar]
  67. YangC. MaJ. QiS. TianJ. ZhengS. TianX. Directional support value of Gaussian transformation for infrared small target detection.Appl. Opt.20155492255226510.1364/AO.54.00225525968508
     [Google Scholar]
  68. ZhouY. SuY. XieA. KongL. A newly bio-inspired path planning algorithm for autonomous obstacle avoidance of UAV.Chin. J. Aeronauti.202134919920910.1016/j.cja.2020.12.018
     [Google Scholar]
  69. KendoulF. Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems.J. Field Robot.201229231537810.1002/rob.20414
     [Google Scholar]
  70. LuY. XueZ. XiaG.S. ZhangL. A survey on vision-based UAV navigation.Geo Spat. Inf. Sci.2018211213210.1080/10095020.2017.1420509
     [Google Scholar]
  71. VetrellaA. FasanoG. AccardoD. MocciaA. Differential GNSS and vision-based tracking to improve navigation performance in cooperative multi-UAV systems.Sensors20161612216410.3390/s1612216427999318
     [Google Scholar]
  72. WangD. LiW. LiuX. LiN. ZhangC. UAV environmental perception and autonomous obstacle avoidance: A deep learning and depth camera combined solution.Comput. Electron. Agric.202017510552310.1016/j.compag.2020.105523
     [Google Scholar]
  73. SuJ. YiD. SuB. MiZ. LiuC. HuX. XuX. GuoL. ChenW-H. Aerial visual perception in smart farming: Field study of wheat yellow rust monitoring.IEEE Trans. Industr. Inform.20211732242224910.1109/TII.2020.2979237
     [Google Scholar]
  74. RadmaneshM. KumarM. GuentertP.H. SarimM. Overview of path-planning and obstacle avoidance algorithms for UAVs: A comparative study.Unmanned Systems2018629511810.1142/S2301385018400022
     [Google Scholar]
  75. LiJ. DengG. LuoC. LinQ. YanQ. MingZ. A hybrid path planning method in unmanned air/ground vehicle (UAV/UGV) cooperative systems.IEEE Trans. Vehicular Technol.201665129585959610.1109/TVT.2016.2623666
     [Google Scholar]
  76. MandloiD. AryaR. VermaA.K. Unmanned aerial vehicle path planning based on A* algorithm and its variants in 3d environment.Int. J. Syst. Assur. Eng. Manag.2021125990100010.1007/s13198‑021‑01186‑9
     [Google Scholar]
  77. LatipN.B.A. OmarR. DebnathS.K. Optimal path planning using equilateral spaces oriented visibility graph method.Int. J. Electr. Comput.20177630463051
     [Google Scholar]
  78. ChengZ. WangE. TangY. WangY. Real-time path planning strategy for UAV based on improved particle swarm optimization.J. Comput.20149120921410.4304/jcp.9.1.209‑214
     [Google Scholar]
  79. TsardouliasE.G. IliakopoulouA. KargakosA. PetrouL. A review of global path planning methods for occupancy grid maps regardless of obstacle density.J. Intell. Robot. Syst.2016841-482985810.1007/s10846‑016‑0362‑z
     [Google Scholar]
  80. LvJ.X. YanL-J. ChuS-C. CaiZ-M. PanJ-S. HeX-K. XueJ-K. A new hybrid algorithm based on golden eagle optimizer and grey wolf optimizer for 3D path planning of multiple UAVs in power inspection.Neural Comput. Appl.20223414119111193610.1007/s00521‑022‑07080‑0
     [Google Scholar]
  81. XieR. MengZ. WangL. LiH. WangK. WuZ. Unmanned aerial vehicle path planning algorithm based on deep reinforcement learning in large-scale and dynamic environments.IEEE Access20219248842490010.1109/ACCESS.2021.3057485
     [Google Scholar]
  82. DoukhiO. LeeD.J. Deep reinforcement learning for end-to-end local motion planning of autonomous aerial robots in unknown outdoor environments: Real-time flight experiments.Sensors2021217253410.3390/s2107253433916624
     [Google Scholar]
  83. ChenY. LuoG. MeiY. YuJ. SuX. UAV path planning using artificial potential field method updated by optimal control theory.Int. J. Syst. Sci.20164761407142010.1080/00207721.2014.929191
     [Google Scholar]
  84. YuX. LuoY. LiuY. A novel adaptive two-stage approach to dynamic optimal path planning of UAV in 3-D unknown environments.Multimedia Tools Appl.20238212187611877910.1007/s11042‑022‑14254‑4
     [Google Scholar]
  85. ZhaoY. ZhengZ. LiuY. Survey on computational-intelligence-based UAV path planning.Knowl. Base. Syst.2018158546410.1016/j.knosys.2018.05.033
     [Google Scholar]
  86. BaiX. JiangH. CuiJ. LuK. ChenP. ZhangM. UAV path planning based on improved A ∗ and DWA algorithms.Int. J. Aerosp. Eng.2021202111210.1155/2021/4511252
     [Google Scholar]
  87. CuiZ. WangY. UAV path planning based on multi-layer reinforcement learning technique.IEEE Access20219594865949710.1109/ACCESS.2021.3073704
     [Google Scholar]
  88. NguyenV.N. JenssenR. RoversoD. Intelligent monitoring and inspection of power line components powered by UAVs and deep learning.IEEE Power Energy Technol. Syst. J.201961112110.1109/JPETS.2018.2881429
     [Google Scholar]
  89. BasharD.A. Survey on evolving deep learning neural network architectures.J. Artif. Intell. Capsule. Networks201920192738210.36548/jaicn.2019.2.003
     [Google Scholar]
  90. MohammedA. KoraR. A comprehensive review on ensemble deep learning: Opportunities and challenges.J. King Saud Univ-Com2023352757774
     [Google Scholar]
  91. MokayedH. QuanT.Z. AlkhaledL. SivakumarV. Real-time human detection and counting system using deep learning computer vision techniques.Artif. Intell. Appl.202314221229
     [Google Scholar]
  92. XuM. YoonS. FuentesA. ParkD.S. A comprehensive survey of image augmentation techniques for deep learning.arXiv:2205.014912023
     [Google Scholar]
  93. HuangF. ChenS. WangQ. ChenY. ZhangD. Using deep learning in an embedded system for real-time target detection based on images from an unmanned aerial vehicle: vehicle detection as a case study.Int. J. Digit. Earth202316191093610.1080/17538947.2023.2187465
     [Google Scholar]
  94. PartheepanS. SanatiF. HassanJ. Autonomous unmanned aerial vehicles in bushfire management: Challenges and opportunities.Drones2023714710.3390/drones7010047
     [Google Scholar]
  95. DudukcuH. V. TaskiranM. KahramanN. UAV sensor data applications with deep neural networks: A comprehensive survey.Eng. Appl. Artif. Intel.202312310647610.1016/j.engappai.2023.106476
     [Google Scholar]
  96. MiaoX. LiuX. ChenJ. ZhuangS. FanJ. JiangH. Insulator detection in aerial images for transmission line inspection using single shot multibox detector.IEEE Access201979945995610.1109/ACCESS.2019.2891123
     [Google Scholar]
  97. LuoQ. LuanT.H. ShiW. FanP. Deep reinforcement learning based computation offloading and trajectory planning for multi-UAV cooperative target search.IEEE J. Sel. Areas Comm.202341250452010.1109/JSAC.2022.3228558
     [Google Scholar]
  98. WangW. ZhangG. DaQ. LuD. ZhaoY. LiS. LangD. Multiple unmanned aerial vehicle autonomous path planning algorithm based on whale-inspired Deep Q-Network.Drones20237957210.3390/drones7090572
     [Google Scholar]
  99. CaoS. FanQ. Jin YUW. Tao WangL. NiS. ChenJ. Multi-Sensor fusion and data analysis for operating conditions of low power transmission lines.Measurement202219011058610.1016/j.measurement.2021.110586
     [Google Scholar]
  100. CourbonJ. MezouarY. GuénardN. MartinetP. Vision-based navigation of unmanned aerial vehicles.Control Eng. Pract.201018778979910.1016/j.conengprac.2010.03.004
     [Google Scholar]
  101. ArafatM.Y. AlamM.M. MohS. Vision-based navigation techniques for unmanned aerial vehicles: Review and challenges.Drones2023728910.3390/drones7020089
     [Google Scholar]
  102. KakaletsisE. SymeonidisC. TzelepiM. MademlisI. TefasA. NikolaidisN. PitasI. Computer vision for autonomous UAV flight safety: An overview and a vision-based safe landing pipeline example.ACM Comput. Surv.202254913710.1145/3472288
     [Google Scholar]
  103. ChenX. UAV patrol path planning based on machine vision and multi-sensor fusion.Open Comput. Sci.20231312022027610.1515/comp‑2022‑0276
     [Google Scholar]
  104. ParkJ. ChoN. Collision avoidance of hexacopter UAV based on LiDAR data in dynamic environment.Remote Sens.202012697510.3390/rs12060975
     [Google Scholar]
  105. LiuZ. MiaoX. XieZ. JiangH. ChenJ. Power tower inspection simultaneous localization and mapping: A monocular semantic positioning approach for uav transmission tower inspection.Sensors20222219736010.3390/s2219736036236460
     [Google Scholar]
  106. López-EstradaF.R. Astorga-ZaragozaC.M. TheilliolD. PonsartJ.C. PalomoV.G. TorresL. Observer synthesis for a class of Takagi–Sugeno descriptor system with unmeasurable premise variable. Application to fault diagnosis.Int. J. Syst. Sci.201748163419343010.1080/00207721.2017.1384517
     [Google Scholar]
  107. PeñateS. EstradaL.F-R. PalomoV.G. RotondoD. SánchezG.M-E. Actuator and sensor fault estimation based on a proportional multiple‐integral sliding mode observer for linear parameter varying systems with inexact scheduling parameters.Int. J. Robust Nonlinear Control202131178420844110.1002/rnc.5371
     [Google Scholar]
  108. WilsonA.N. KumarA. JhaA. CenkeramaddiL.R. Embedded sensors, communication technologies, computing platforms and machine learning for UAVs: A review.IEEE Sens. J.20222231807182610.1109/JSEN.2021.3139124
     [Google Scholar]
  109. ShihavuddinA.S.M. RashidM.R.A. MarufM.H. HasanM.A. HaqM.A. AshiqueR.H. MansurA.A. Image based surface damage detection of renewable energy installations using a unified deep learning approach.Energy Rep.202174566457610.1016/j.egyr.2021.07.045
     [Google Scholar]
  110. Al-ShareedaM.A. AnbarM. ManickamS. YassinA.A. VPPCS: VANET-based privacy-preserving communication scheme.IEEE Access2020815091415092810.1109/ACCESS.2020.3017018
     [Google Scholar]
  111. Al-ShareedaM.A. AnbarM. HasbullahI.H. ManickamS. HanshiS.M. Efficient conditional privacy preservation with mutual authentication in vehicular Ad Hoc networks.IEEE Access2020814495714496810.1109/ACCESS.2020.3014678
     [Google Scholar]
  112. SrivastavaA. PrakashJ. Future FANET with application and enabling techniques: Anatomization and sustainability issues.Comput. Sci. Rev.20213910035910.1016/j.cosrev.2020.100359
     [Google Scholar]
  113. ShareedaA. Chebyshev polynomial-based scheme for resisting side-channel attacks in 5g-enabled vehicular networks.Appl. Sci-Basel202112125939
     [Google Scholar]
  114. LeiT. YangZ. LinZ. ZhangX. State of art on energy management strategy for hybrid-powered unmanned aerial vehicle.Chin. J. Aeronauti.20193261488150310.1016/j.cja.2019.03.013
     [Google Scholar]
  115. FoudehH.A. LukP.C.K. WhidborneJ.F. An advanced unmanned aerial vehicle (UAV) approach via learning-based control for overhead power line monitoring: A comprehensive review.IEEE Access2021913041013043310.1109/ACCESS.2021.3110159
     [Google Scholar]
  116. LuM. BagheriM. JamesA.P. PhungT. Wireless charging techniques for UAVs: A review, reconceptualization, and extension.IEEE Access20186298652988410.1109/ACCESS.2018.2841376
     [Google Scholar]
  117. SánchezG.M.E. GonzálezH.O. PalomoV.G. RavellM.D.A. EstradaL.F.R. MontañoH.J.A. Robust IDA-PBC for under-actuated systems with inertia matrix dependent of the unactuated coordinates: Application to a UAV carrying a load.Nonlinear Dyn.202110543225323810.1007/s11071‑021‑06776‑7
     [Google Scholar]
  118. SánchezG.M.E. GonzálezH.O. LozanoR. BeltránG.C-D. PalomoV.G. EstradaL.F-R. Energy-based control and LMI-based control for a quadrotor transporting a payload.Mathematics2019711109010.3390/math7111090
     [Google Scholar]
  119. LangåkerH.A. KjerkreitH. SyversenC.L. MooreR.J.D. HolhjemØ.H. JensenI. MorrisonA. TransethA.A. KvienO. BergG. OlsenT.A. HatlestadA. NegårdT. BrochR. JohnsenJ.E. An autonomous drone-based system for inspection of electrical substations.Int. J. Adv. Robot. Syst.202118210.1177/17298814211002973
     [Google Scholar]
  120. da SilvaM.F. HonórioL.M. MarcatoA.L.M. VidalV.F. SantosM.F. Unmanned aerial vehicle for transmission line inspection using an extended Kalman filter with colored electromagnetic interference.ISA Trans.202010032233310.1016/j.isatra.2019.11.00731759684
     [Google Scholar]
/content/journals/raeeng/10.2174/0123520965307948240604074250
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The Progress and Trend of UAV Power Line Inspection: A Bibliome Trics-based Visualization Analysis
Recent Advances in Electrical & Electronic Engineering 18, 1188 (2025); https://doi.org/10.2174/0123520965307948240604074250
/content/journals/raeeng/10.2174/0123520965307948240604074250
/content/journals/raeeng/10.2174/0123520965307948240604074250
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  • Article Type:     Review Article
Keyword(s): bibliometrics analysis; fault detection; intelligent inspection; Power line inspection; UAV; visualized analysis

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