Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking Chambers
The onboard energy supply of Autonomous Underwater Vehicles (AUVs) is one of the main limiting factors for their development. The existing methods of deploying and retrieving AUVs from mother ships consume a significant amount of energy during submerging and surfacing, resulting in a small percentag...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/1/162 |
| _version_ | 1827371720729690112 |
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| author | Jia Wang Tianyi Tao Daohua Lu Zhibin Wang Rongtao Wang |
| author_facet | Jia Wang Tianyi Tao Daohua Lu Zhibin Wang Rongtao Wang |
| author_sort | Jia Wang |
| collection | DOAJ |
| description | The onboard energy supply of Autonomous Underwater Vehicles (AUVs) is one of the main limiting factors for their development. The existing methods of deploying and retrieving AUVs from mother ships consume a significant amount of energy during submerging and surfacing, resulting in a small percentage of actual working time. Underwater docking chambers provide support to AUVs underwater, saving their precious energy and addressing this issue. When an AUV cluster is assigned multiple tasks, scheduling the cluster becomes essential, and task allocation and path planning are among the core problems in AUV cluster scheduling research. In this paper, based on the underwater docking chamber, an Improved Genetic Local Search Algorithm with Prior Knowledge (IGLSAPK) is proposed to simultaneously solve the task allocation and path planning problems. Under constraints such as onboard energy supply, AUV quantity, and AUV type, the algorithm groups AUVs, assigns tasks, and plans paths to accomplish tasks at different locations, aiming to achieve overall efficiency. The algorithm first generates an initial population using prior knowledge to improve its search efficiency. It then combines an improved local search algorithm to efficiently solve large-scale, complex, and highly coupled problems. The algorithm has been evaluated through simulation experiments and comparative experiments, and the results demonstrate that the proposed algorithm outperforms other algorithms in terms of speed and optimality. The algorithm presented in this paper addresses the grouping, task allocation, and path planning problems in heterogeneous AUV clusters. Its practical significance lies in its ability to handle tasks executed by a heterogeneous AUV group, making it more practical compared to previous algorithms. |
| first_indexed | 2024-03-08T10:45:36Z |
| format | Article |
| id | doaj.art-bb786774d0734baeb60a8cf13f218576 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-08T10:45:36Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-bb786774d0734baeb60a8cf13f2185762024-01-26T17:17:27ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-01-0112116210.3390/jmse12010162Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking ChambersJia Wang0Tianyi Tao1Daohua Lu2Zhibin Wang3Rongtao Wang4School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, ChinaSchool of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, ChinaMarine Equipment and Technology Institute, Jiangsu University of Science and Technology, Zhenjiang 212003, China708th Research Institute of CSSC, Huangpu District, Shanghai 200011, ChinaShanghai Marine Equipment Research Institute, Shanghai 200031, ChinaThe onboard energy supply of Autonomous Underwater Vehicles (AUVs) is one of the main limiting factors for their development. The existing methods of deploying and retrieving AUVs from mother ships consume a significant amount of energy during submerging and surfacing, resulting in a small percentage of actual working time. Underwater docking chambers provide support to AUVs underwater, saving their precious energy and addressing this issue. When an AUV cluster is assigned multiple tasks, scheduling the cluster becomes essential, and task allocation and path planning are among the core problems in AUV cluster scheduling research. In this paper, based on the underwater docking chamber, an Improved Genetic Local Search Algorithm with Prior Knowledge (IGLSAPK) is proposed to simultaneously solve the task allocation and path planning problems. Under constraints such as onboard energy supply, AUV quantity, and AUV type, the algorithm groups AUVs, assigns tasks, and plans paths to accomplish tasks at different locations, aiming to achieve overall efficiency. The algorithm first generates an initial population using prior knowledge to improve its search efficiency. It then combines an improved local search algorithm to efficiently solve large-scale, complex, and highly coupled problems. The algorithm has been evaluated through simulation experiments and comparative experiments, and the results demonstrate that the proposed algorithm outperforms other algorithms in terms of speed and optimality. The algorithm presented in this paper addresses the grouping, task allocation, and path planning problems in heterogeneous AUV clusters. Its practical significance lies in its ability to handle tasks executed by a heterogeneous AUV group, making it more practical compared to previous algorithms.https://www.mdpi.com/2077-1312/12/1/162AUV swarmgenetic algorithmlocal random searchtask allocationpriori knowledge |
| spellingShingle | Jia Wang Tianyi Tao Daohua Lu Zhibin Wang Rongtao Wang Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking Chambers Journal of Marine Science and Engineering AUV swarm genetic algorithm local random search task allocation priori knowledge |
| title | Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking Chambers |
| title_full | Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking Chambers |
| title_fullStr | Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking Chambers |
| title_full_unstemmed | Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking Chambers |
| title_short | Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking Chambers |
| title_sort | research on the heterogeneous autonomous underwater vehicle cluster scheduling problem based on underwater docking chambers |
| topic | AUV swarm genetic algorithm local random search task allocation priori knowledge |
| url | https://www.mdpi.com/2077-1312/12/1/162 |
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