To address the challenges of low automation and poor adaptability to complex environments in power grid UAV inspections, this study analyzes the architecture and operational constraints of intelligent UAV inspection systems, clarifying optimization goals in terms of efficiency, safety, and intelligence. A closed-loop autonomous flight control method is proposed, integrating high-precision target recognition, dynamic path planning, and reinforcement learning–based optimization. Furthermore, a Transformer-based global obstacle perception and risk assessment approach is designed to construct a holistic “perception–decision–control” technical framework. Field experiments demonstrate that the proposed strategy significantly enhances flight path planning efficiency, obstacle recognition accuracy, and operational safety. The results provide scalable technical support for advancing the digital transformation of power grids and promoting the development of new-type power systems.
| Published in | Science Discovery (Volume 13, Issue 5) |
| DOI | 10.11648/j.sd.20251305.13 |
| Page(s) | 95-100 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Drone, Obstacle Perception, Intelligent Inspection, Dynamic Path Planning, Reinforcement Learning
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APA Style
Gao, S., Liu, Q., Sun, M., Zhang, H. (2025). Key Technologies for Target-Driven Autonomous UAV Inspection in Power Grids Based on Reinforcement Learning. Science Discovery, 13(5), 95-100. https://doi.org/10.11648/j.sd.20251305.13
ACS Style
Gao, S.; Liu, Q.; Sun, M.; Zhang, H. Key Technologies for Target-Driven Autonomous UAV Inspection in Power Grids Based on Reinforcement Learning. Sci. Discov. 2025, 13(5), 95-100. doi: 10.11648/j.sd.20251305.13
AMA Style
Gao S, Liu Q, Sun M, Zhang H. Key Technologies for Target-Driven Autonomous UAV Inspection in Power Grids Based on Reinforcement Learning. Sci Discov. 2025;13(5):95-100. doi: 10.11648/j.sd.20251305.13
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Download@article{10.11648/j.sd.20251305.13,
author = {Shunfeng Gao and Qinghua Liu and Min Sun and Haoran Zhang},
title = {Key Technologies for Target-Driven Autonomous UAV Inspection in Power Grids Based on Reinforcement Learning
},
journal = {Science Discovery},
volume = {13},
number = {5},
pages = {95-100},
doi = {10.11648/j.sd.20251305.13},
url = {https://doi.org/10.11648/j.sd.20251305.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20251305.13},
abstract = {To address the challenges of low automation and poor adaptability to complex environments in power grid UAV inspections, this study analyzes the architecture and operational constraints of intelligent UAV inspection systems, clarifying optimization goals in terms of efficiency, safety, and intelligence. A closed-loop autonomous flight control method is proposed, integrating high-precision target recognition, dynamic path planning, and reinforcement learning–based optimization. Furthermore, a Transformer-based global obstacle perception and risk assessment approach is designed to construct a holistic “perception–decision–control” technical framework. Field experiments demonstrate that the proposed strategy significantly enhances flight path planning efficiency, obstacle recognition accuracy, and operational safety. The results provide scalable technical support for advancing the digital transformation of power grids and promoting the development of new-type power systems.
},
year = {2025}
}
TY - JOUR T1 - Key Technologies for Target-Driven Autonomous UAV Inspection in Power Grids Based on Reinforcement Learning AU - Shunfeng Gao AU - Qinghua Liu AU - Min Sun AU - Haoran Zhang Y1 - 2025/10/29 PY - 2025 N1 - https://doi.org/10.11648/j.sd.20251305.13 DO - 10.11648/j.sd.20251305.13 T2 - Science Discovery JF - Science Discovery JO - Science Discovery SP - 95 EP - 100 PB - Science Publishing Group SN - 2331-0650 UR - https://doi.org/10.11648/j.sd.20251305.13 AB - To address the challenges of low automation and poor adaptability to complex environments in power grid UAV inspections, this study analyzes the architecture and operational constraints of intelligent UAV inspection systems, clarifying optimization goals in terms of efficiency, safety, and intelligence. A closed-loop autonomous flight control method is proposed, integrating high-precision target recognition, dynamic path planning, and reinforcement learning–based optimization. Furthermore, a Transformer-based global obstacle perception and risk assessment approach is designed to construct a holistic “perception–decision–control” technical framework. Field experiments demonstrate that the proposed strategy significantly enhances flight path planning efficiency, obstacle recognition accuracy, and operational safety. The results provide scalable technical support for advancing the digital transformation of power grids and promoting the development of new-type power systems. VL - 13 IS - 5 ER -
School of Computing, Jiangsu University of Science and Technology, Zhenjiang, China
School of Automation, Jiangsu University of Science and Technology, Zhenjiang, China
School of Automation, Jiangsu University of Science and Technology, Zhenjiang, China
Dental Department, Zhengjiang 359 Hospital, Zhenjiang, China
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