Optimal Coverage Path Planning for UAV-Assisted Multiple USVs: Map Modeling and Solutions
With the increasing demand for marine monitoring, the use of coverage path planning based on unmanned aerial vehicle (UAV) aerial images to assist multiple unmanned surface vehicles (USVs) has shown great potential in marine applications. However, achieving accurate map modeling and optimal path pla...
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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/9/1/30 |
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| author | Shaohua Pan Xiaosu Xu Yi Cao Liang Zhang |
| author_facet | Shaohua Pan Xiaosu Xu Yi Cao Liang Zhang |
| author_sort | Shaohua Pan |
| collection | DOAJ |
| description | With the increasing demand for marine monitoring, the use of coverage path planning based on unmanned aerial vehicle (UAV) aerial images to assist multiple unmanned surface vehicles (USVs) has shown great potential in marine applications. However, achieving accurate map modeling and optimal path planning are still key challenges that restrict its widespread application. To this end, an innovative coverage path planning algorithm for UAV-assisted multiple USVs is proposed. First, a semantic segmentation algorithm based on the YOLOv5-assisted prompting segment anything model (SAM) is designed to establish an accurate map model. By refining the axial, length, width, and coordinate information of obstacles, the algorithm enables YOLOv5 to generate accurate object bounding box prompts and then assists SAM in automatically and accurately extracting obstacles and coastlines in complex scenes. Based on this accurate map model, a multi-objective stepwise optimization coverage path planning algorithm is further proposed. The algorithm divides the complete path into two parts, the straight paths and the turning paths, and both the path length and the number of turns is designed, respectively, to optimize each type of path step by step, which significantly improves the coverage effect. Experiments prove that in various complex marine coverage scenarios, the proposed algorithm achieves 100% coverage, the redundancy rate is less than 2%, and it is superior to existing advanced algorithms in path length and number of turns. This research provides a feasible technical solution for efficient and accurate marine coverage tasks and lays the foundation for unmanned marine supervision. |
| format | Article |
| id | doaj-art-16f0c9d52f05489a911c1e9613a77a54 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-16f0c9d52f05489a911c1e9613a77a542025-01-24T13:29:42ZengMDPI AGDrones2504-446X2025-01-01913010.3390/drones9010030Optimal Coverage Path Planning for UAV-Assisted Multiple USVs: Map Modeling and SolutionsShaohua Pan0Xiaosu Xu1Yi Cao2Liang Zhang3Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaKey Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaPurple Mountain Laboratories, Nanjing 211111, ChinaKey Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaWith the increasing demand for marine monitoring, the use of coverage path planning based on unmanned aerial vehicle (UAV) aerial images to assist multiple unmanned surface vehicles (USVs) has shown great potential in marine applications. However, achieving accurate map modeling and optimal path planning are still key challenges that restrict its widespread application. To this end, an innovative coverage path planning algorithm for UAV-assisted multiple USVs is proposed. First, a semantic segmentation algorithm based on the YOLOv5-assisted prompting segment anything model (SAM) is designed to establish an accurate map model. By refining the axial, length, width, and coordinate information of obstacles, the algorithm enables YOLOv5 to generate accurate object bounding box prompts and then assists SAM in automatically and accurately extracting obstacles and coastlines in complex scenes. Based on this accurate map model, a multi-objective stepwise optimization coverage path planning algorithm is further proposed. The algorithm divides the complete path into two parts, the straight paths and the turning paths, and both the path length and the number of turns is designed, respectively, to optimize each type of path step by step, which significantly improves the coverage effect. Experiments prove that in various complex marine coverage scenarios, the proposed algorithm achieves 100% coverage, the redundancy rate is less than 2%, and it is superior to existing advanced algorithms in path length and number of turns. This research provides a feasible technical solution for efficient and accurate marine coverage tasks and lays the foundation for unmanned marine supervision.https://www.mdpi.com/2504-446X/9/1/30coverage path planningunmanned surface vehiclesunmanned aerial vehicleaerial imagessemantic segmentation |
| spellingShingle | Shaohua Pan Xiaosu Xu Yi Cao Liang Zhang Optimal Coverage Path Planning for UAV-Assisted Multiple USVs: Map Modeling and Solutions Drones coverage path planning unmanned surface vehicles unmanned aerial vehicle aerial images semantic segmentation |
| title | Optimal Coverage Path Planning for UAV-Assisted Multiple USVs: Map Modeling and Solutions |
| title_full | Optimal Coverage Path Planning for UAV-Assisted Multiple USVs: Map Modeling and Solutions |
| title_fullStr | Optimal Coverage Path Planning for UAV-Assisted Multiple USVs: Map Modeling and Solutions |
| title_full_unstemmed | Optimal Coverage Path Planning for UAV-Assisted Multiple USVs: Map Modeling and Solutions |
| title_short | Optimal Coverage Path Planning for UAV-Assisted Multiple USVs: Map Modeling and Solutions |
| title_sort | optimal coverage path planning for uav assisted multiple usvs map modeling and solutions |
| topic | coverage path planning unmanned surface vehicles unmanned aerial vehicle aerial images semantic segmentation |
| url | https://www.mdpi.com/2504-446X/9/1/30 |
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