Article(id=1263557421776159573, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.01.007, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740844800000, receivedDateStr=2025-03-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1779185165113, onlineDateStr=2026-05-19, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1779185165113, onlineIssueDateStr=2026-05-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1779185165113, creator=13701087609, updateTime=1779185165113, updator=13701087609, issue=Issue{id=1263557421033767765, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='1', pageStart='1', pageEnd='197', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1779185164936, creator=13701087609, updateTime=1779185226134, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1263557677813252309, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1263557677813252310, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=66, endPage=77, ext={EN=ArticleExt(id=1263557422334002007, articleId=1263557421776159573, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Multi-UAV maritime search and rescue path planning based on multi-objective optimization algorithms, columnId=1240655017900823355, journalTitle=Navigation of China, columnName=Marine Traffic Safety, runingTitle=null, highlight=null, articleAbstract=

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.

, correspAuthors=Wenjun ZHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Wenjun ZHANG, Kai LIAO, Xiangkun MENG, Xue YANG, Xiangyu ZHOU, Huaiyu ZHENG), CN=ArticleExt(id=1263557423156085595, articleId=1263557421776159573, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于多目标优化算法的多无人机海上搜救路径规划, columnId=1240655018068595518, journalTitle=中国航海, columnName=海上交通安全, runingTitle=null, highlight=null, articleAbstract=

针对多变海洋环境与紧迫时间需求下的多无人机海上搜救任务，提出一种面向搜索效率与资源均衡的覆盖路径规划方法。首先，通过基于网格的区域分解方法将复杂的海上环境简化为可视化的规划单元，引入高斯混合模型对目标漂移分布进行先验建模，生成概率分布图以引导路径搜索。其次，在多无人机覆盖路径规划中，基于改进的多目标粒子群优化算法，融合任务分配、路径安全、高优先级区域覆盖及能耗控制等多个优化目标。再次，为提升算法全局搜索能力与收敛性能，引入基于Sigmoid函数的自适应动态权重调整机制、双层精英交叉策略以及路径约束惩罚等改进策略。最后，部署三架无人机在多种形状搜救区域中开展仿真试验。结果表明：所提方法在无人机前50步目标累计发现概率、任务均衡度和路径总长度方面，分别较传统经典算法最高提升了30.27%、82.5%和1.28%，验证了所提方法在提升搜救效率和任务均衡性方面的有效性与可行性。

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章文俊（1977—），男，教授，博士，研究方向为海上交通安全保障。

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章文俊（1977—），男，教授，博士，研究方向为海上交通安全保障。

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