Article(id=1263533691066790469, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.02.012, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740412800000, receivedDateStr=2025-02-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1779179507270, onlineDateStr=2026-05-19, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1779179507270, onlineIssueDateStr=2026-05-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1779179507270, creator=13701087609, updateTime=1779179507270, updator=13701087609, issue=Issue{id=1263533678655881533, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='2', pageStart='1', pageEnd='163', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1779179504312, creator=13701087609, updateTime=1779179563167, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1263533925629047616, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1263533925629047617, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=104, endPage=111, ext={EN=ArticleExt(id=1263533692627071576, articleId=1263533691066790469, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Global path planning for unmanned surface vehicles based on improved ant colony optimization and waypoint refinement, columnId=1240655021340160960, journalTitle=Navigation of China, columnName=Intelligent Shipping, runingTitle=null, highlight=null, articleAbstract=

To overcome the deficiencies of traditional ant colony optimization (ACO) in pheromone updating, local optima convergence, and path planning safety, this study proposes a global path planning algorithm based on improved ACO and turning-point refinement. The heuristic function is improved using the reciprocal of the Euclidean distance between current path nodes and the destination, along with balancing parameters for iteration number, search quality, and efficiency, thereby enhancing global and local search capabilities while avoiding local optima. An adaptive pheromone evaporation coefficient is designed by utilizing characteristics of cosine function to dynamically adjust the convergence of the proposed ant colony optimization in its early and late stages. Considering the complexity of maritime environments and practical navigation requirements, a grid-based navigation environment is constructed. An obstacle-adjacent node detection method and fixed-point approximation algorithm is proposed for turning point refinement to improve navigation safety and ensure optimized paths better conform to maritime practice. Simulation experiments demonstrate that, compared with traditional ACO and other improved algorithms, the proposed algorithm shortens the average path length by approximately 39% and reduces the average iteration number by 79%, significantly improving solution quality and convergence efficiency. It effectively alleviates issues of insufficient search directionality and susceptibility to local optima. These results verify the reliability of the proposed approach for global path planning of unmanned surface vehicles and its high efficiency in redundant waypoint optimization, thereby providing effective decision support in practical applications.

, correspAuthors=Xiaori Gao, 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=Xiaori Gao, Pengfei Xu, Xinbo Liu, Shujia Yan, Lidong Wang), CN=ArticleExt(id=1263533695651164780, articleId=1263533691066790469, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于改进蚁群算法和转向点优化的无人艇全局路径规划, columnId=1240655021528904650, journalTitle=中国航海, columnName=智能航运, runingTitle=null, highlight=null, articleAbstract=

针对传统蚁群算法在信息素更新、易陷入局部最优以及路径规划安全性方面不足等问题，提出一种基于改进蚁群算法和转向点优化的全局路径规划算法。通过引入迭代次数、搜索质量和效率的平衡参数，利用当前路径节点到终点的欧氏距离倒数来改进启发函数，以强化全局与局部搜索能力并避免局部最优。利用余弦函数的特性来设计自适应信息素挥发系数，以动态调整所提出的改进蚁群算法前后期的收敛性。鉴于航海环境的复杂性和实际航行需求，依托栅格法构建的航行环境，设计一种障碍物相邻节点检测和固定点逼近转向点优化算法，以提升航海安全性，使优化路径更贴合航海实践要求。仿真试验表明：与传统蚁群算法和其他改进算法相比，所提出的算法在平均路径长度上缩短约39%，平均迭代次数减少79%，提升路径寻优质量和收敛效率，有效缓解了搜索方向性不足及易陷入局部最优的问题，验证了其在无人艇全局路径规划中的可靠性和冗余转向点处理的高效性，为实际应用提供决策支持。

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高孝日（1983—），男，副教授，博士，研究方向为船舶运动建模与控制、模糊认知图等。

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高孝日（1983—），男，副教授，博士，研究方向为船舶运动建模与控制、模糊认知图等。

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