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Multi-UAV maritime search and rescue path planning based on multi-objective optimization algorithms
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Wenjun ZHANG1, 2, *, Kai LIAO1, 2, Xiangkun MENG1, 2, Xue YANG1, 2, Xiangyu ZHOU1, 2, Huaiyu ZHENG1, 3
Navigation of China | 2026, 49(1) : 66 - 77
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Navigation of China | 2026, 49(1): 66-77
• Marine Traffic Safety •
Multi-UAV maritime search and rescue path planning based on multi-objective optimization algorithms
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Wenjun ZHANG1, 2, *, Kai LIAO1, 2, Xiangkun MENG1, 2, Xue YANG1, 2, Xiangyu ZHOU1, 2, Huaiyu ZHENG1, 3
Affiliations
  • 1.Navigation College, Dalian Maritime University, Dalian 116026, China
  • 2.Dalian Key Laboratory of Safety & Security Technology for Autonomous Shipping, Dalian 116026, China
  • 3.Global Sustainable Transport Innovation and Knowledge Center, Beijing 100736, China
Published: 2026-02-25 doi: 10.3969/j.issn.1000-4653.2026.01.007
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This study develops a coverage path planning method for multi-UAV maritime search and rescue (MSAR) missions under dynamic ocean conditions and time-critical constraints, aiming to balance search efficiency and resource allocation. Firstly, a grid-based regional decomposition approach is adopted to discretize complex maritime environments into visual planning cells, while a Gaussian Mixture Model (GMM) is employed to construct a prior target-drift distribution and generate a probabilistic map for path guidance. Secondly, for multi-UAV coverage planning, an improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed to jointly optimize task allocation, path safety, coverage of high-priority areas, and energy consumption control. Thirdly, to enhance global search capability and convergence performance, the algorithm incorporates a Sigmoid-based adaptive inertia weight strategy, a two-level elite-guided crossover strategy, and a constraint-penalty mechanism. Finally, three UAVs were deployed to conduct simulation tests over MSAR regions of various shapes. Results show that, compared with classical baseline algorithms, the proposed method achieves up to 30. 27% improvement in cumulative detection probability, 82. 5% improvement in workload balance, and 1. 28% reduction in total path length within the first 50 steps, demonstrating its effectiveness and practicality for improving MSAR efficiency and coordination.

Maritime Search and Rescue (MSAR)  /  unmanned aerial vehicles  /  coverage path planning  /  Particle Swarm Optimization (PSO)  /  multi-objective optimization
Wenjun ZHANG, Kai LIAO, Xiangkun MENG, Xue YANG, Xiangyu ZHOU, Huaiyu ZHENG. Multi-UAV maritime search and rescue path planning based on multi-objective optimization algorithms[J]. Navigation of China, 2026 , 49 (1) : 66 -77 . DOI: 10.3969/j.issn.1000-4653.2026.01.007
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Year 2026 volume 49 Issue 1
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Article Info
doi: 10.3969/j.issn.1000-4653.2026.01.007
  • Receive Date：2025-03-02
  • Online Date：2026-05-19
  • Published：2026-02-25
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  • Received：2025-03-02
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Affiliations
    1.Navigation College, Dalian Maritime University, Dalian 116026, China
    2.Dalian Key Laboratory of Safety & Security Technology for Autonomous Shipping, Dalian 116026, China
    3.Global Sustainable Transport Innovation and Knowledge Center, Beijing 100736, China
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