An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection
In recent years, clean energy has gained increasing attention, with offshore wind power playing a crucial role in global energy production. However, the high operating and maintenance costs of offshore wind farms remain a significant challenge. The advent of 5G technology provides a solution for eff...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/47 |
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| author | Congxiao Jiang Lingang Yang Yuqing Gao Jie Zhao Wenne Hou Fangmin Xu |
| author_facet | Congxiao Jiang Lingang Yang Yuqing Gao Jie Zhao Wenne Hou Fangmin Xu |
| author_sort | Congxiao Jiang |
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| description | In recent years, clean energy has gained increasing attention, with offshore wind power playing a crucial role in global energy production. However, the high operating and maintenance costs of offshore wind farms remain a significant challenge. The advent of 5G technology provides a solution for efficiently monitoring and controlling wind power equipment. The use of 5G unmanned aerial vehicles (UAVs) for blade inspections is a promising development. A key challenge is efficiently planning UAV flight paths for fast and effective inspections in complex offshore environments. To address this problem, we conduct an in-depth study of the 5G UAV path optimization method. In this paper, the UAV inspection path problem is modeled as an obstacle avoidance traveling salesman problem (TSP), taking into full account UAV flight constraints and complex sea environment factors, particularly the impact of sea wind on UAV flight speed. We propose a novel Sea Wind-Aware Improved A*-Guided Genetic Algorithm (SWA-IAGA), which integrates an improved A* algorithm to guide the genetic algorithm for efficient path planning, with the assistance of relevant graphical knowledge. This algorithm overcomes the limitations of traditional single-path planning methods, enabling more accurate and efficient path planning. |
| format | Article |
| id | doaj-art-268e362dd1e44c249bca9cd56e47b435 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-268e362dd1e44c249bca9cd56e47b4352025-01-24T13:29:46ZengMDPI AGDrones2504-446X2025-01-01914710.3390/drones9010047An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm InspectionCongxiao Jiang0Lingang Yang1Yuqing Gao2Jie Zhao3Wenne Hou4Fangmin Xu5PowerChina Huadong Engineering Corporation Limited, Hangzhou 311100, ChinaPowerChina Huadong Engineering Corporation Limited, Hangzhou 311100, ChinaPowerChina Huadong Engineering Corporation Limited, Hangzhou 311100, ChinaPowerChina Huadong Engineering Corporation Limited, Hangzhou 311100, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaIn recent years, clean energy has gained increasing attention, with offshore wind power playing a crucial role in global energy production. However, the high operating and maintenance costs of offshore wind farms remain a significant challenge. The advent of 5G technology provides a solution for efficiently monitoring and controlling wind power equipment. The use of 5G unmanned aerial vehicles (UAVs) for blade inspections is a promising development. A key challenge is efficiently planning UAV flight paths for fast and effective inspections in complex offshore environments. To address this problem, we conduct an in-depth study of the 5G UAV path optimization method. In this paper, the UAV inspection path problem is modeled as an obstacle avoidance traveling salesman problem (TSP), taking into full account UAV flight constraints and complex sea environment factors, particularly the impact of sea wind on UAV flight speed. We propose a novel Sea Wind-Aware Improved A*-Guided Genetic Algorithm (SWA-IAGA), which integrates an improved A* algorithm to guide the genetic algorithm for efficient path planning, with the assistance of relevant graphical knowledge. This algorithm overcomes the limitations of traditional single-path planning methods, enabling more accurate and efficient path planning.https://www.mdpi.com/2504-446X/9/1/47offshore wind farm5G UAVpath optimizationgenetic algorithmA* algorithmSWA-IAGA |
| spellingShingle | Congxiao Jiang Lingang Yang Yuqing Gao Jie Zhao Wenne Hou Fangmin Xu An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection Drones offshore wind farm 5G UAV path optimization genetic algorithm A* algorithm SWA-IAGA |
| title | An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection |
| title_full | An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection |
| title_fullStr | An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection |
| title_full_unstemmed | An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection |
| title_short | An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection |
| title_sort | intelligent 5g unmanned aerial vehicle path optimization algorithm for offshore wind farm inspection |
| topic | offshore wind farm 5G UAV path optimization genetic algorithm A* algorithm SWA-IAGA |
| url | https://www.mdpi.com/2504-446X/9/1/47 |
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