Adaptive Multi-Objective Optimization-Based Coverage Path Planning Method for UUVs
Coverage path planning for unmanned undersea vehicles(UUVs) in unknown aquatic environments is a critical task. However, due to environmental uncertainties, motion constraints, and energy limitations, traditional path planning methods struggle to adapt to complex scenarios. This paper proposed an ad...
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Science Press (China)
2025-06-01
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| Series: | 水下无人系统学报 |
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| Online Access: | https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2025-0031 |
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| author | Shaojing ZHAO Songchen FU Letian BAI Yutong GUO Ta LI |
| author_facet | Shaojing ZHAO Songchen FU Letian BAI Yutong GUO Ta LI |
| author_sort | Shaojing ZHAO |
| collection | DOAJ |
| description | Coverage path planning for unmanned undersea vehicles(UUVs) in unknown aquatic environments is a critical task. However, due to environmental uncertainties, motion constraints, and energy limitations, traditional path planning methods struggle to adapt to complex scenarios. This paper proposed an adaptive multi-objective optimization-based coverage path planning method for UUVs, integrating proximal policy optimization(PPO) with a dynamic weight adjustment mechanism. By analyzing the correlation between reward objectives and employing linear regression estimation, the proposed approach adaptively adjusted the weights of different optimization objectives, enabling UUVs to autonomously plan efficient coverage paths in environments with unknown obstacles and ocean currents. To validate the effectiveness of the proposed method, a UUV motion and sonar detection model based on a two-dimensional simulation environment was constructed. Among them, the UUV motion model was simplified to a planar motion model on the basis of the six-degree-of-freedom rigid-body motion. Comparative experiments were conducted under various obstacle distributions and random ocean currents. Experimental results demonstrate that compared with traditional methods, the proposed approach improves coverage while optimizing mission completion rate, trajectory length, energy consumption, and information latency. Specifically, it increases coverage by 4.03%, enhances mission completion rate by 10%, improves utility by 10.96%, reduces mission completion time by 14.13%, shortens trajectory length by 26.85%, lowers energy consumption by 10.3%, and decreases information latency by 19.34%. These results indicate that the proposed method significantly enhances the adaptability and robustness of UUVs in complex environments, providing a novel optimization strategy for autonomous underwater exploration tasks. |
| format | Article |
| id | doaj-art-f7191ffc400b4fe7a5d5bb22a22637e5 |
| institution | Matheson Library |
| issn | 2096-3920 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Science Press (China) |
| record_format | Article |
| series | 水下无人系统学报 |
| spelling | doaj-art-f7191ffc400b4fe7a5d5bb22a22637e52025-07-07T02:29:08ZzhoScience Press (China)水下无人系统学报2096-39202025-06-0133345947210.11993/j.issn.2096-3920.2025-00312025-0031Adaptive Multi-Objective Optimization-Based Coverage Path Planning Method for UUVsShaojing ZHAO0Songchen FU1Letian BAI2Yutong GUO3Ta LI4Laboratory of Speech and Intelligent Information Processing, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaLaboratory of Speech and Intelligent Information Processing, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaLaboratory of Speech and Intelligent Information Processing, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaLaboratory of Speech and Intelligent Information Processing, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaLaboratory of Speech and Intelligent Information Processing, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaCoverage path planning for unmanned undersea vehicles(UUVs) in unknown aquatic environments is a critical task. However, due to environmental uncertainties, motion constraints, and energy limitations, traditional path planning methods struggle to adapt to complex scenarios. This paper proposed an adaptive multi-objective optimization-based coverage path planning method for UUVs, integrating proximal policy optimization(PPO) with a dynamic weight adjustment mechanism. By analyzing the correlation between reward objectives and employing linear regression estimation, the proposed approach adaptively adjusted the weights of different optimization objectives, enabling UUVs to autonomously plan efficient coverage paths in environments with unknown obstacles and ocean currents. To validate the effectiveness of the proposed method, a UUV motion and sonar detection model based on a two-dimensional simulation environment was constructed. Among them, the UUV motion model was simplified to a planar motion model on the basis of the six-degree-of-freedom rigid-body motion. Comparative experiments were conducted under various obstacle distributions and random ocean currents. Experimental results demonstrate that compared with traditional methods, the proposed approach improves coverage while optimizing mission completion rate, trajectory length, energy consumption, and information latency. Specifically, it increases coverage by 4.03%, enhances mission completion rate by 10%, improves utility by 10.96%, reduces mission completion time by 14.13%, shortens trajectory length by 26.85%, lowers energy consumption by 10.3%, and decreases information latency by 19.34%. These results indicate that the proposed method significantly enhances the adaptability and robustness of UUVs in complex environments, providing a novel optimization strategy for autonomous underwater exploration tasks.https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2025-0031unmanned undersea vehiclecoverage path planningreinforcement learningmulti-objective optimizationadaptive weight adjustment |
| spellingShingle | Shaojing ZHAO Songchen FU Letian BAI Yutong GUO Ta LI Adaptive Multi-Objective Optimization-Based Coverage Path Planning Method for UUVs 水下无人系统学报 unmanned undersea vehicle coverage path planning reinforcement learning multi-objective optimization adaptive weight adjustment |
| title | Adaptive Multi-Objective Optimization-Based Coverage Path Planning Method for UUVs |
| title_full | Adaptive Multi-Objective Optimization-Based Coverage Path Planning Method for UUVs |
| title_fullStr | Adaptive Multi-Objective Optimization-Based Coverage Path Planning Method for UUVs |
| title_full_unstemmed | Adaptive Multi-Objective Optimization-Based Coverage Path Planning Method for UUVs |
| title_short | Adaptive Multi-Objective Optimization-Based Coverage Path Planning Method for UUVs |
| title_sort | adaptive multi objective optimization based coverage path planning method for uuvs |
| topic | unmanned undersea vehicle coverage path planning reinforcement learning multi-objective optimization adaptive weight adjustment |
| url | https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2025-0031 |
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