Article(id=1240656101012730001, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240656096998773667, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2025.01.017, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1699027200000, receivedDateStr=2023-11-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773725064757, onlineDateStr=2026-03-17, pubDate=1742832000000, pubDateStr=2025-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773725064757, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773725064757, creator=13701087609, updateTime=1773725064757, updator=13701087609, issue=Issue{id=1240656096998773667, tenantId=1146029695717560320, journalId=1240618002186551303, year='2025', volume='48', issue='1', pageStart='1', pageEnd='198', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773725063800, creator=13701087609, updateTime=1773725118083, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240656324749480238, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240656096998773667, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240656324749480239, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240656096998773667, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=132, endPage=140, ext={EN=ArticleExt(id=1240656101532823698, articleId=1240656101012730001, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Ship path planning in offshore wind farm waters based on improved A* algorithm, columnId=1240655021340160960, journalTitle=Navigation of China, columnName=Intelligent Shipping, runingTitle=null, highlight=null, articleAbstract=

The traditional A* algorithm applied to the path planning of offshore wind farm operation and maintenance ships has not yet taken into account the dynamic obstacles, water currents, and crossing navigation channels, therefore, this paper proposes a path planning method that considers navigational risks, heading angle constraints, and path smoothing. On the basis of constructing the map of offshore wind farm water environment by raster method, weight coefficients are introduced to change the proportion of estimated surrogate value in the total cost function of the A* algorithm to achieve the purpose of balancing the strength of heuristic information and shortening the pathfinding time, and the risk of obstacles containing water currents is taken into account in order to improve the actual cost function of the A* algorithm and enhance the security of the planned paths. Meanwhile, the heading angle constraint is considered in the A* algorithm to reduce the total number of traversal nodes, the eight-neighborhood search is constrained to three neighboring nodes conforming to the path direction, the inflection points are extracted and visibility check is performed to remove the redundant inflection points in the path, and the smooth planning path is obtained using a uniform B-spline curve. Taking the Donghai Bridge No.5 and No.6 wind farm waters as an example, a high tide path planning scenario is established, and the operation and maintenance ship needs to pass through 9 wind turbines in order to complete the operation and maintenance tasks; 4 indexes (path length, total risk value of the path, total number of traversed nodes, and total number of inflection points) are utilized for evaluating the planning path, so as to validate the effectiveness of the improved A* algorithm. The simulation results show that in the high tide scenario, the planning path smoothness of the improved A* algorithm is improved by 77.69%, the total risk value of the planning path is reduced by 52.83%, and the total number of traversal nodes is reduced by 30.58%, but the planning path length of the improved A* algorithm is 252.89 m longer than that of the traditional A* algorithm.

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针对传统A*算法应用于海上风电场运维船舶路径规划时未考虑动态障碍物、水流和穿越航道等因素的问题，提出考虑航行风险、航向角约束和路径平滑的路径规划方法。在使用栅格法构建海上风电场水域环境地图的基础上，引入权重系数改变估计代价值在A*算法总代价函数中的比例，达到平衡启发式信息强度和缩短寻路时间的目的，并通过考虑含水流的障碍物风险改进A*算法的实际代价函数，提升规划路径安全性。同时在A*算法中考虑航向角约束以减少遍历节点总数，将八邻域搜索设定为符合路径走向的3个相邻节点，提取各拐点并进行通视性检查以删除路径中的冗余拐点，使用均匀B样条曲线得到平滑的规划路径。以东海大桥5号、6号风电场水域为例，建立涨潮路径规划场景，运维船舶需通过9个风机以完成运维任务；利用4个指标（路径长度、路径总风险值、遍历节点总数、拐点总数）对规划路径进行评价，以此验证改进A*算法的有效性。仿真结果表明：在涨潮场景中，改进的A*算法规划路径平滑性提升了77.69%，规划路径总风险值降低了52.83%，遍历节点总数降低了30.58%，但改进的A*算法规划路径比传统A*算法规划路径长252.89 m。

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^4.School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1240656102791114931, tenantId=1146029695717560320, journalId=1240618002186551303, articleId=1240656101012730001, authorId=1240656102577205418, language=CN, stringName=李珍尧, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2, 3, 4}, address=^1．武汉理工大学 水路交通控制全国重点实验室，湖北 武汉 430063
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^3． 武汉理工大学 国家水运安全工程技术研究中心，湖北 武汉 430063
^4． 武汉理工大学 交通与物流工程学院，湖北 武汉 430063, bio={"content":"

李珍尧（1998—），女，硕士，研究方向为路径规划。E-mail:zhenyao.li@whut.edu.cn

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李珍尧（1998—），女，硕士，研究方向为路径规划。E-mail:zhenyao.li@whut.edu.cn

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