A Hybrid Dynamic Path-Planning Method for Obstacle Avoidance in Unmanned Aerial Vehicle-Based Power Inspection
Path planning for Unmanned Aerial Vehicles (UAVs) plays a critical role in power line inspection. In complex inspection environments characterized by densely distributed and dynamic obstacles, traditional path-planning algorithms struggle to ensure both efficiency and safety. To address these challe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/1/22 |
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| author | Zheng Huang Chengling Jiang Chao Shen Bin Liu Tao Huang Minghui Zhang |
| author_facet | Zheng Huang Chengling Jiang Chao Shen Bin Liu Tao Huang Minghui Zhang |
| author_sort | Zheng Huang |
| collection | DOAJ |
| description | Path planning for Unmanned Aerial Vehicles (UAVs) plays a critical role in power line inspection. In complex inspection environments characterized by densely distributed and dynamic obstacles, traditional path-planning algorithms struggle to ensure both efficiency and safety. To address these challenges, this study proposes a dynamic path-planning method that integrates an improved Rapidly exploring Random Tree Star (RRT*) algorithm with the Dynamic Window Approach (DWA). The proposed method includes key components such as sampling-point search, random tree growth, global path-node optimization, and local dynamic obstacle avoidance. In the sampling-point search, a target-biased search strategy is introduced to guide the random tree growth toward the target point, while an attractive function is added to enhance search efficiency. Based on a breadth-first search strategy, the path obtained is optimized to reduce path complexity. To address the RRT* algorithm’s limitation in dynamic obstacle avoidance, a local path-planning method combining the improved DWA algorithm is proposed, improving efficiency in areas with dense obstacles. Simulation results show that, compared to traditional algorithms, the proposed method achieves an 8% to 12% optimization in path length, more than 50% in node optimization, and over 95% in planning time optimization. Furthermore, in dynamic obstacle avoidance across different motion directions, the proposed method ensures effective local dynamic obstacle avoidance while minimizing global path fluctuations. |
| format | Article |
| id | doaj-art-26385a9cf6cb4242a48c91dcc8e66716 |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-26385a9cf6cb4242a48c91dcc8e667162025-01-24T13:52:47ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-01-011612210.3390/wevj16010022A Hybrid Dynamic Path-Planning Method for Obstacle Avoidance in Unmanned Aerial Vehicle-Based Power InspectionZheng Huang0Chengling Jiang1Chao Shen2Bin Liu3Tao Huang4Minghui Zhang5State Grid Jiangsu Electric Power., Ltd., Nanjing 210024, ChinaState Grid Jiangsu Electric Power., Ltd., Nanjing 210024, ChinaWuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, ChinaState Grid Jiangsu Electric Power., Ltd., Nanjing 210024, ChinaState Grid Jiangsu Electric Power., Ltd., Nanjing 210024, ChinaWuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, ChinaPath planning for Unmanned Aerial Vehicles (UAVs) plays a critical role in power line inspection. In complex inspection environments characterized by densely distributed and dynamic obstacles, traditional path-planning algorithms struggle to ensure both efficiency and safety. To address these challenges, this study proposes a dynamic path-planning method that integrates an improved Rapidly exploring Random Tree Star (RRT*) algorithm with the Dynamic Window Approach (DWA). The proposed method includes key components such as sampling-point search, random tree growth, global path-node optimization, and local dynamic obstacle avoidance. In the sampling-point search, a target-biased search strategy is introduced to guide the random tree growth toward the target point, while an attractive function is added to enhance search efficiency. Based on a breadth-first search strategy, the path obtained is optimized to reduce path complexity. To address the RRT* algorithm’s limitation in dynamic obstacle avoidance, a local path-planning method combining the improved DWA algorithm is proposed, improving efficiency in areas with dense obstacles. Simulation results show that, compared to traditional algorithms, the proposed method achieves an 8% to 12% optimization in path length, more than 50% in node optimization, and over 95% in planning time optimization. Furthermore, in dynamic obstacle avoidance across different motion directions, the proposed method ensures effective local dynamic obstacle avoidance while minimizing global path fluctuations.https://www.mdpi.com/2032-6653/16/1/22path planningrapidly exploring random tree starartificial potential field methoddynamic window approach |
| spellingShingle | Zheng Huang Chengling Jiang Chao Shen Bin Liu Tao Huang Minghui Zhang A Hybrid Dynamic Path-Planning Method for Obstacle Avoidance in Unmanned Aerial Vehicle-Based Power Inspection World Electric Vehicle Journal path planning rapidly exploring random tree star artificial potential field method dynamic window approach |
| title | A Hybrid Dynamic Path-Planning Method for Obstacle Avoidance in Unmanned Aerial Vehicle-Based Power Inspection |
| title_full | A Hybrid Dynamic Path-Planning Method for Obstacle Avoidance in Unmanned Aerial Vehicle-Based Power Inspection |
| title_fullStr | A Hybrid Dynamic Path-Planning Method for Obstacle Avoidance in Unmanned Aerial Vehicle-Based Power Inspection |
| title_full_unstemmed | A Hybrid Dynamic Path-Planning Method for Obstacle Avoidance in Unmanned Aerial Vehicle-Based Power Inspection |
| title_short | A Hybrid Dynamic Path-Planning Method for Obstacle Avoidance in Unmanned Aerial Vehicle-Based Power Inspection |
| title_sort | hybrid dynamic path planning method for obstacle avoidance in unmanned aerial vehicle based power inspection |
| topic | path planning rapidly exploring random tree star artificial potential field method dynamic window approach |
| url | https://www.mdpi.com/2032-6653/16/1/22 |
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