Efficient collision-avoidance navigation strategy for autonomous surface vehicles in unstructured and constricted marine environments
Compared to structured ocean environments, unstructured ocean environments are inherently more complex. In such unstructured environments, the presence of narrow waterways poses unique navigational hurdles for autonomous surface vehicles (ASVs) due to their restricted connectivity. Current path plan...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1555262/full |
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| author | Wenlong Meng Rongdian Ku Yanbo Pu Xiaoxiao Shi Ya Gong |
| author_facet | Wenlong Meng Rongdian Ku Yanbo Pu Xiaoxiao Shi Ya Gong |
| author_sort | Wenlong Meng |
| collection | DOAJ |
| description | Compared to structured ocean environments, unstructured ocean environments are inherently more complex. In such unstructured environments, the presence of narrow waterways poses unique navigational hurdles for autonomous surface vehicles (ASVs) due to their restricted connectivity. Current path planning algorithms designed for unstructured environments, particularly those characterized by narrow spaces, often face difficulties in efficiently exploring the target area while producing high-quality paths. In this study, we tackle the aforementioned complexities by incorporating progressive sampling and point cloud clustering, which jointly expedite the detection of constrained waterways in unstructured marine environments. More specifically, we generate multiple random trees from these sampling points, thereby bolstering both navigational accuracy and overall computational efficiency. Building upon these core techniques, we introduce a novel extension of the traditional rapidly-exploring random trees (RRT) connect algorithm—referred to as multiple RRT-connect (multi-RRT-connect)—aimed at swiftly determining a viable path between prescribed start and goal coordinates. As the number of samples expands, the random trees gradually enlarge and interlink, mirroring the functionality of classic RRT-connect and ultimately forming a continuous corridor. Subsequently, the derived path undergoes iterative refinement and optimization, culminating in a significantly reduced trajectory length.We subjected the proposed algorithm to rigorous testing through comprehensive simulations alongside meticulous comparisons with established state-of-the-art solutions. The results highlight the algorithm’s distinct advantages across multiple dimensions such as path construction success, computational efficiency, and trajectory refinement quality, thereby underscoring its potential to advance autonomous navigation in challenging maritime settings. |
| format | Article |
| id | doaj-art-4c475e540c3b4b8983ba06c604d0797e |
| institution | DOAJ |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-4c475e540c3b4b8983ba06c604d0797e2025-08-20T03:09:41ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-05-011210.3389/fmars.2025.15552621555262Efficient collision-avoidance navigation strategy for autonomous surface vehicles in unstructured and constricted marine environmentsWenlong Meng0Rongdian Ku1Yanbo Pu2Xiaoxiao Shi3Ya Gong4School of Computer Science and Technology, Harbin Institute of Technology, Weihai, Shandong, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Weihai, Shandong, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Weihai, Shandong, ChinaMarine College, Shandong University, Weihai, Shandong, ChinaCompared to structured ocean environments, unstructured ocean environments are inherently more complex. In such unstructured environments, the presence of narrow waterways poses unique navigational hurdles for autonomous surface vehicles (ASVs) due to their restricted connectivity. Current path planning algorithms designed for unstructured environments, particularly those characterized by narrow spaces, often face difficulties in efficiently exploring the target area while producing high-quality paths. In this study, we tackle the aforementioned complexities by incorporating progressive sampling and point cloud clustering, which jointly expedite the detection of constrained waterways in unstructured marine environments. More specifically, we generate multiple random trees from these sampling points, thereby bolstering both navigational accuracy and overall computational efficiency. Building upon these core techniques, we introduce a novel extension of the traditional rapidly-exploring random trees (RRT) connect algorithm—referred to as multiple RRT-connect (multi-RRT-connect)—aimed at swiftly determining a viable path between prescribed start and goal coordinates. As the number of samples expands, the random trees gradually enlarge and interlink, mirroring the functionality of classic RRT-connect and ultimately forming a continuous corridor. Subsequently, the derived path undergoes iterative refinement and optimization, culminating in a significantly reduced trajectory length.We subjected the proposed algorithm to rigorous testing through comprehensive simulations alongside meticulous comparisons with established state-of-the-art solutions. The results highlight the algorithm’s distinct advantages across multiple dimensions such as path construction success, computational efficiency, and trajectory refinement quality, thereby underscoring its potential to advance autonomous navigation in challenging maritime settings.https://www.frontiersin.org/articles/10.3389/fmars.2025.1555262/fullautonomous surface vehiclesunstructured marine environmentsconstricted waterwaysrapidly exploring random treepoint cloud clustering |
| spellingShingle | Wenlong Meng Rongdian Ku Yanbo Pu Xiaoxiao Shi Ya Gong Efficient collision-avoidance navigation strategy for autonomous surface vehicles in unstructured and constricted marine environments Frontiers in Marine Science autonomous surface vehicles unstructured marine environments constricted waterways rapidly exploring random tree point cloud clustering |
| title | Efficient collision-avoidance navigation strategy for autonomous surface vehicles in unstructured and constricted marine environments |
| title_full | Efficient collision-avoidance navigation strategy for autonomous surface vehicles in unstructured and constricted marine environments |
| title_fullStr | Efficient collision-avoidance navigation strategy for autonomous surface vehicles in unstructured and constricted marine environments |
| title_full_unstemmed | Efficient collision-avoidance navigation strategy for autonomous surface vehicles in unstructured and constricted marine environments |
| title_short | Efficient collision-avoidance navigation strategy for autonomous surface vehicles in unstructured and constricted marine environments |
| title_sort | efficient collision avoidance navigation strategy for autonomous surface vehicles in unstructured and constricted marine environments |
| topic | autonomous surface vehicles unstructured marine environments constricted waterways rapidly exploring random tree point cloud clustering |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2025.1555262/full |
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