Article(id=1263557423491572622, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.01.004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1739030400000, receivedDateStr=2025-02-09, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1779185165522, onlineDateStr=2026-05-19, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1779185165522, onlineIssueDateStr=2026-05-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1779185165522, creator=13701087609, updateTime=1779185165522, 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=38, endPage=45, ext={EN=ArticleExt(id=1263557426427585441, articleId=1263557423491572622, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Ship path planning method based on hexagonal grid and A^* algorithm, columnId=1240655017900823355, journalTitle=Navigation of China, columnName=Marine Traffic Safety, runingTitle=null, highlight=null, articleAbstract=

To address the limitations of the conventional square grid in ship path planning—such as insufficient safety margins, low search efficiency, and poor adaptability to ship maneuvering characteristics—this study proposes a ship path planning method based on a regular hexagonal grid and an improved A^* algorithm. A hexagonal grid neighborhood model is constructed according to the geometric properties of the regular hexagonal grid and the required ship-obstacle safety clearance, together with an encoding scheme suitable for hexagonal cells. A ship motion cost model is developed by incorporating ship inertia and turning constraints. Based on this model, the traditional A^* algorithm is improved by optimizing the heuristic function and introducing a turning-penalty mechanism, thereby forming a hexagonal-grid-based search algorithm for ship path planning. Comparative experiments show that, compared with the square-grid 8-neighborhood method, the proposed method shortens the path by 5.51% and reduces the number of search nodes by 30. 7% ; compared with the 4-neighborhood method, the path length is reduced by 17. 0% and the number of turning points decreases by 38. 6%. The paths generated on the hexagonal grid are smoother and more consistent with ship maneuvering characteristics. Key words:intelligent navigation; ship path planning; hexagonal grid; improved A^* algorithm; ship maneuverability

, correspAuthors=Zhao LIU, 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=Zhao LIU, Ziyue KANG, Fan YANG, Chenhan LUO), CN=ArticleExt(id=1263557428117889967, articleId=1263557423491572622, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于正六边形栅格和A^*算法的船舶路径规划方法, columnId=1240655018068595518, journalTitle=中国航海, columnName=海上交通安全, runingTitle=null, highlight=null, articleAbstract=

针对传统正方形栅格在船舶路径规划中存在的安全裕度不足、搜索效率不高以及难以满足船舶运动特性的缺陷，提出一种基于六边形栅格与改进A^*算法的船舶路径规划方法。根据正六边形栅格的几何特性与船舶避碰安全距离，构建六边形栅格邻域模型，并设计适用于六边形栅格的节点编码方法；结合船舶惯性运动特性与转向约束，建立船舶在六边形栅格中的运动代价模型；在此基础上，对传统A^*算法进行启发函数与转向代价改进，提出面向船舶路径规划的六边形栅格搜索算法。对比试验结果表明：与正方形栅格8邻域方法相比，六边形栅格可在保持安全距离的前提下使路径长度缩短5.51%，搜索次数减少30.7%；与正方形栅格4邻域方法相比，路径长度缩短17.0%，转向点数量减少38.6%。基于六边形栅格地图生成的路径更平滑、转向更符合船舶操纵特性。

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刘钊（1986—），男，副教授，博士，研究方向为群船智慧挖掘与应用、无人艇自主航行。

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刘钊（1986—），男，副教授，博士，研究方向为群船智慧挖掘与应用、无人艇自主航行。

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