Coordinated Trajectory Planning for Multiple Autonomous Underwater Vehicles: A Parallel Grey Wolf Optimizer
The utilization of unmanned systems has witnessed a steady surge in popularity owing to its tremendous potential for a wide range of applications. In particular, the coordination among multiple vehicle systems has been demonstrated to possess unparalleled efficacy in accomplishing intricate and dive...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/9/1720 |
| _version_ | 1797579344671932416 |
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| author | Fang Wang Liang Zhao |
| author_facet | Fang Wang Liang Zhao |
| author_sort | Fang Wang |
| collection | DOAJ |
| description | The utilization of unmanned systems has witnessed a steady surge in popularity owing to its tremendous potential for a wide range of applications. In particular, the coordination among multiple vehicle systems has been demonstrated to possess unparalleled efficacy in accomplishing intricate and diverse tasks. In light of this, the present paper delves into the coordinated path planning mission that is accomplished by collaborative efforts amongst multiple Autonomous Underwater Vehicles (AUVs). First, considering the potential threats, arrival time windows, space, and physical constraints for the AUVs, a sophisticated coordinated path planning model is formulated in a 3D environment, serving as a systematic and structured blueprint for the underlying mechanism. Subsequently, the optimization problem is addressed through the incorporation of a restricted initialization scheme and a multi-objective clustering strategy in the proposed methodology. The resulting approach leads to the development of the Parallel Grey Wolf Optimizer (P-GWO) which exhibits strong global searching abilities and a rapid convergence rate, rendering it a dependable and effective solution. The results demonstrate a 10–15% improvement in convergence rate and a reduction of over 60% in the average cost value compared to reliable references, thus presenting an effective solution for underwater missions with specific requirements. |
| first_indexed | 2024-03-10T22:34:47Z |
| format | Article |
| id | doaj.art-05ceb727a230403492c6f924bbb7c344 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T22:34:47Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-05ceb727a230403492c6f924bbb7c3442023-11-19T11:26:29ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-09-01119172010.3390/jmse11091720Coordinated Trajectory Planning for Multiple Autonomous Underwater Vehicles: A Parallel Grey Wolf OptimizerFang Wang0Liang Zhao1School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaThe utilization of unmanned systems has witnessed a steady surge in popularity owing to its tremendous potential for a wide range of applications. In particular, the coordination among multiple vehicle systems has been demonstrated to possess unparalleled efficacy in accomplishing intricate and diverse tasks. In light of this, the present paper delves into the coordinated path planning mission that is accomplished by collaborative efforts amongst multiple Autonomous Underwater Vehicles (AUVs). First, considering the potential threats, arrival time windows, space, and physical constraints for the AUVs, a sophisticated coordinated path planning model is formulated in a 3D environment, serving as a systematic and structured blueprint for the underlying mechanism. Subsequently, the optimization problem is addressed through the incorporation of a restricted initialization scheme and a multi-objective clustering strategy in the proposed methodology. The resulting approach leads to the development of the Parallel Grey Wolf Optimizer (P-GWO) which exhibits strong global searching abilities and a rapid convergence rate, rendering it a dependable and effective solution. The results demonstrate a 10–15% improvement in convergence rate and a reduction of over 60% in the average cost value compared to reliable references, thus presenting an effective solution for underwater missions with specific requirements.https://www.mdpi.com/2077-1312/11/9/1720AUVUSVmultiple AUVscoordinated path planninggrey wolf optimizerGWO |
| spellingShingle | Fang Wang Liang Zhao Coordinated Trajectory Planning for Multiple Autonomous Underwater Vehicles: A Parallel Grey Wolf Optimizer Journal of Marine Science and Engineering AUV USV multiple AUVs coordinated path planning grey wolf optimizer GWO |
| title | Coordinated Trajectory Planning for Multiple Autonomous Underwater Vehicles: A Parallel Grey Wolf Optimizer |
| title_full | Coordinated Trajectory Planning for Multiple Autonomous Underwater Vehicles: A Parallel Grey Wolf Optimizer |
| title_fullStr | Coordinated Trajectory Planning for Multiple Autonomous Underwater Vehicles: A Parallel Grey Wolf Optimizer |
| title_full_unstemmed | Coordinated Trajectory Planning for Multiple Autonomous Underwater Vehicles: A Parallel Grey Wolf Optimizer |
| title_short | Coordinated Trajectory Planning for Multiple Autonomous Underwater Vehicles: A Parallel Grey Wolf Optimizer |
| title_sort | coordinated trajectory planning for multiple autonomous underwater vehicles a parallel grey wolf optimizer |
| topic | AUV USV multiple AUVs coordinated path planning grey wolf optimizer GWO |
| url | https://www.mdpi.com/2077-1312/11/9/1720 |
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