Article(id=1240655518251930334, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655508231746242, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2025.02.011, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1716998400000, receivedDateStr=2024-05-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773724925815, onlineDateStr=2026-03-17, pubDate=1750780800000, pubDateStr=2025-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773724925815, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773724925815, creator=13701087609, updateTime=1773724925815, updator=13701087609, issue=Issue{id=1240655508231746242, tenantId=1146029695717560320, journalId=1240618002186551303, year='2025', volume='48', issue='2', pageStart='1', pageEnd='180', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773724923427, creator=13701087609, updateTime=1773725151164, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240656463488667953, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655508231746242, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240656463492862258, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655508231746242, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=80, endPage=90, ext={EN=ArticleExt(id=1240655518801384170, articleId=1240655518251930334, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=A path planning method for unmanned surface vehicle based on improved dynamic window approach, columnId=1240655021340160960, journalTitle=Navigation of China, columnName=Intelligent Shipping, runingTitle=null, highlight=null, articleAbstract=

To improve the effectiveness of Unmanned Surface Vehicle (USV) path planning in complex marine environments, and to address the issue of low operational efficiency in dynamic environments, a new Dynamic Window Approach (DWA) for USV path planning has been developed. Firstly, the target water area range is determined based on the USV's initial and target positions, and the water area is modelled using a grid system. Secondly, the forces exerted on the USV by wind, waves and currents are calculated and combined with the USV's mass to produce an acceleration value, thereby forming a kinematic model of the USV under the influence of the marine environment. Thirdly, the maximum values of linear and angular acceleration of the USV are determined using the Fossen ship dynamics model, thus obtaining the actual reachable speed set of the USV. Finally, an adaptive weight adjustment algorithm is used to improve DWA and plan the USV path, considering the heading, safe distance and speed factors. Simulation experiments were conducted in the waters near the Zhoushan Islands using the USV "Zhi Kun" to verify the reliability of the model. The results show that, compared with the comparative algorithm, the proposed algorithm performs better in terms of running time, simulation step size, change in bow direction amplitude, change in speed amplitude, and length of the planned path. The improved DWA-planned path ensures the USV can reach its destination safely and quickly, providing a reference for improving the autonomous navigation performance and efficient utilisation of USVs.

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为提升无人艇路径规划在复杂海洋环境中的适用性，克服路径规划算法在动态环境中存在的运行效率低下问题，提出一种基于改进动态窗口法（DWA）的无人艇路径规划方法。首先，根据无人艇的初始位置和目标位置确定目标水域范围，将水域进行栅格化建模；其次，计算风、浪、流对无人艇产生的作用力，并结合无人艇质量将其转化为加速度，进而形成海洋环境影响下的无人艇运动学模型；接着，运用Fossen动力学模型，确定无人艇的线加速度和角加速度的最大值，从而得到无人艇的实际可达速度集合；最后，综合考虑航向、安全距离和速度要素，运用自适应权重调整算法改进DWA，规划无人艇路径。以舟山群岛附近水域为试验环境，以无人艇“智鲲”号为试验船舶，进行仿真试验验证模型的可靠性。结果表明：相较于对比算法，所提出的算法在运行时间、仿真步长、艏向的变化幅度、速度的变化幅度以及规划路径的长度等方面表现更优。改进DWA规划出的路径能更好地保障无人艇安全、快速地到达目的地，可为无人艇的自主航行性能提升和高效利用提供参考。

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^4.Sanya Science and Education Innovation Park of Wuhan University of Technology, Sanya 572000, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1240655520013538100, tenantId=1146029695717560320, journalId=1240618002186551303, articleId=1240655518251930334, authorId=1240655519824794407, language=CN, stringName=刘钊, 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 航运学院，湖北 武汉 430063
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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、无人艇风险计算与自主航行。E-mail: zhaoliu@whut.edu.cn

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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、无人艇风险计算与自主航行。E-mail: zhaoliu@whut.edu.cn

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