Cooperative Detection-Oriented Formation Design and Optimization of USV Swarms via an Improved Genetic Algorithm
Efficient and adaptive formation planning is critical for unmanned surface vehicle (USV) swarms equipped with sensor networks and smart sensors to perform cooperative detection tasks in complex marine environments. Existing formation optimization methods often overlook the nonlinear coupling between...
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| Format: | Article |
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/3179 |
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| author | Rui Liang Dingzhao Li Haixin Sun Liangpo Hong |
| author_facet | Rui Liang Dingzhao Li Haixin Sun Liangpo Hong |
| author_sort | Rui Liang |
| collection | DOAJ |
| description | Efficient and adaptive formation planning is critical for unmanned surface vehicle (USV) swarms equipped with sensor networks and smart sensors to perform cooperative detection tasks in complex marine environments. Existing formation optimization methods often overlook the nonlinear coupling between sensor-based detection performance, communication constraints, and obstacle avoidance. We propose a multi-objective formation optimization framework based on an improved genetic algorithm that simultaneously considers the detection coverage area, forward detection width, inter-agent communication, and static obstacle avoidance. We formulate a probabilistic cooperative detection model, introduce normalized detection efficiency indicators, and embed multiple geometric and environmental constraints into the optimization process. Simulation results show that the proposed method significantly improves the spatial efficiency of cooperative sensing, yielding a 32.76% increase in effective coverage area and 20.97% improvement in forward detection width compared to unoptimized formations. This strategy, supported by multi-sensor positioning and navigation, offers a robust and generalizable approach for intelligent maritime USV deployment in dynamic, multi-constraint scenarios. |
| format | Article |
| id | doaj-art-c7b3d513fcb44ac98ceafa701b2aae6c |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c7b3d513fcb44ac98ceafa701b2aae6c2025-08-20T03:47:58ZengMDPI AGSensors1424-82202025-05-012510317910.3390/s25103179Cooperative Detection-Oriented Formation Design and Optimization of USV Swarms via an Improved Genetic AlgorithmRui Liang0Dingzhao Li1Haixin Sun2Liangpo Hong3School of General Education, Xiamen City University, Xiamen 361005, ChinaSchool of Information, Xiamen University, Xiang’an District, Xiamen 361102, ChinaSchool of Information, Xiamen University, Xiang’an District, Xiamen 361102, ChinaSchool of Information, Xiamen University, Xiang’an District, Xiamen 361102, ChinaEfficient and adaptive formation planning is critical for unmanned surface vehicle (USV) swarms equipped with sensor networks and smart sensors to perform cooperative detection tasks in complex marine environments. Existing formation optimization methods often overlook the nonlinear coupling between sensor-based detection performance, communication constraints, and obstacle avoidance. We propose a multi-objective formation optimization framework based on an improved genetic algorithm that simultaneously considers the detection coverage area, forward detection width, inter-agent communication, and static obstacle avoidance. We formulate a probabilistic cooperative detection model, introduce normalized detection efficiency indicators, and embed multiple geometric and environmental constraints into the optimization process. Simulation results show that the proposed method significantly improves the spatial efficiency of cooperative sensing, yielding a 32.76% increase in effective coverage area and 20.97% improvement in forward detection width compared to unoptimized formations. This strategy, supported by multi-sensor positioning and navigation, offers a robust and generalizable approach for intelligent maritime USV deployment in dynamic, multi-constraint scenarios.https://www.mdpi.com/1424-8220/25/10/3179unmanned surface vehicle (USV)formation optimizationgenetic algorithmmulti-objective optimizationmarine environment |
| spellingShingle | Rui Liang Dingzhao Li Haixin Sun Liangpo Hong Cooperative Detection-Oriented Formation Design and Optimization of USV Swarms via an Improved Genetic Algorithm Sensors unmanned surface vehicle (USV) formation optimization genetic algorithm multi-objective optimization marine environment |
| title | Cooperative Detection-Oriented Formation Design and Optimization of USV Swarms via an Improved Genetic Algorithm |
| title_full | Cooperative Detection-Oriented Formation Design and Optimization of USV Swarms via an Improved Genetic Algorithm |
| title_fullStr | Cooperative Detection-Oriented Formation Design and Optimization of USV Swarms via an Improved Genetic Algorithm |
| title_full_unstemmed | Cooperative Detection-Oriented Formation Design and Optimization of USV Swarms via an Improved Genetic Algorithm |
| title_short | Cooperative Detection-Oriented Formation Design and Optimization of USV Swarms via an Improved Genetic Algorithm |
| title_sort | cooperative detection oriented formation design and optimization of usv swarms via an improved genetic algorithm |
| topic | unmanned surface vehicle (USV) formation optimization genetic algorithm multi-objective optimization marine environment |
| url | https://www.mdpi.com/1424-8220/25/10/3179 |
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