Multi-USV Task Assignment Based on NSGA II-MC
With the development of artificial intelligence technology, the level of autonomy of unmanned platforms is continuously improving. They are capable of completing certain specific tasks based on external information inputs and can achieve battlefield situational awareness. However, when USVs (Unmanne...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10948398/ |
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| _version_ | 1849738059914936320 |
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| author | Yonghao Zhang Xueman Fan Zhuo Cheng Changyou Xue |
| author_facet | Yonghao Zhang Xueman Fan Zhuo Cheng Changyou Xue |
| author_sort | Yonghao Zhang |
| collection | DOAJ |
| description | With the development of artificial intelligence technology, the level of autonomy of unmanned platforms is continuously improving. They are capable of completing certain specific tasks based on external information inputs and can achieve battlefield situational awareness. However, when USVs (Unmanned Surface Vehicles) search for underwater targets, the problem of target motion dispersion is particularly prominent. To address the multi-USV task allocation optimization problem, an allocation optimization model for USV clusters has been constructed, which aims to maximize the probability of target detection and minimize the collaborative time of USV clusters. An improved task allocation optimization algorithm, NSGA II-MC (Non-dominated Sorting Genetic Algorithm II-Monte Carlo), has been proposed. Firstly, a more realistic detection model was selected. Secondly, the encoding strategy and crossover mutation operations of the algorithm were adjusted. An adaptive crossover and mutation probability mechanism was introduced, and the fitness was calculated using the Monte Carlo method. Experimental results show that compared with the traditional NSGA-II algorithm, the proposed algorithm generated two more solutions in the model solving process and exhibited better convergence. The research results enrich the methods of multi-objective optimization algorithms and multi-USV task allocation, providing strong support for more efficient underwater target search. |
| format | Article |
| id | doaj-art-ecaf73fa541a4e79beb9c4c2d1370dba |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ecaf73fa541a4e79beb9c4c2d1370dba2025-08-20T03:06:43ZengIEEEIEEE Access2169-35362025-01-0113625776259010.1109/ACCESS.2025.355758210948398Multi-USV Task Assignment Based on NSGA II-MCYonghao Zhang0https://orcid.org/0009-0000-9861-7903Xueman Fan1Zhuo Cheng2Changyou Xue3Naval Submarine Academy, Qingdao, ChinaNaval Submarine Academy, Qingdao, ChinaNaval Submarine Academy, Qingdao, ChinaNaval Submarine Academy, Qingdao, ChinaWith the development of artificial intelligence technology, the level of autonomy of unmanned platforms is continuously improving. They are capable of completing certain specific tasks based on external information inputs and can achieve battlefield situational awareness. However, when USVs (Unmanned Surface Vehicles) search for underwater targets, the problem of target motion dispersion is particularly prominent. To address the multi-USV task allocation optimization problem, an allocation optimization model for USV clusters has been constructed, which aims to maximize the probability of target detection and minimize the collaborative time of USV clusters. An improved task allocation optimization algorithm, NSGA II-MC (Non-dominated Sorting Genetic Algorithm II-Monte Carlo), has been proposed. Firstly, a more realistic detection model was selected. Secondly, the encoding strategy and crossover mutation operations of the algorithm were adjusted. An adaptive crossover and mutation probability mechanism was introduced, and the fitness was calculated using the Monte Carlo method. Experimental results show that compared with the traditional NSGA-II algorithm, the proposed algorithm generated two more solutions in the model solving process and exhibited better convergence. The research results enrich the methods of multi-objective optimization algorithms and multi-USV task allocation, providing strong support for more efficient underwater target search.https://ieeexplore.ieee.org/document/10948398/Multi-USVtask assignmentNSGA II-MC algorithmtarget dispersion |
| spellingShingle | Yonghao Zhang Xueman Fan Zhuo Cheng Changyou Xue Multi-USV Task Assignment Based on NSGA II-MC IEEE Access Multi-USV task assignment NSGA II-MC algorithm target dispersion |
| title | Multi-USV Task Assignment Based on NSGA II-MC |
| title_full | Multi-USV Task Assignment Based on NSGA II-MC |
| title_fullStr | Multi-USV Task Assignment Based on NSGA II-MC |
| title_full_unstemmed | Multi-USV Task Assignment Based on NSGA II-MC |
| title_short | Multi-USV Task Assignment Based on NSGA II-MC |
| title_sort | multi usv task assignment based on nsga ii mc |
| topic | Multi-USV task assignment NSGA II-MC algorithm target dispersion |
| url | https://ieeexplore.ieee.org/document/10948398/ |
| work_keys_str_mv | AT yonghaozhang multiusvtaskassignmentbasedonnsgaiimc AT xuemanfan multiusvtaskassignmentbasedonnsgaiimc AT zhuocheng multiusvtaskassignmentbasedonnsgaiimc AT changyouxue multiusvtaskassignmentbasedonnsgaiimc |