Article(id=1254362832145936747, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, articleNumber=null, orderNo=null, doi=10.13788/j.cnki.cbgc.2026.03.02, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1754064000000, receivedDateStr=2025-08-02, revisedDate=1761926400000, revisedDateStr=2025-11-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1776993004117, onlineDateStr=2026-04-24, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1776993004117, onlineIssueDateStr=2026-04-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1776993004117, creator=13701087609, updateTime=1776993004117, updator=13701087609, issue=Issue{id=1254362823425974931, tenantId=1146029695717560320, journalId=1254119036117037056, year='2026', volume='48', issue='3', pageStart='1', pageEnd='190', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1776993002036, creator=13701087609, updateTime=1776993258606, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1254363899764077151, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1254363899764077152, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=11, endPage=22, ext={EN=ArticleExt(id=1254362833014157689, articleId=1254362832145936747, tenantId=1146029695717560320, journalId=1254119036117037056, language=EN, title=Intelligent Tracking Navigation Planning Algorithm for Ships with Close-Quarters Posture Constraints, columnId=1254362824956895894, journalTitle=Ship Engineering, columnName=Special Topic: Intelligent Ship, runingTitle=null, highlight=null, articleAbstract=
[Purpose] To address the challenge of coordinated optimization between collision avoidance planning and motion constraints in the scenario where ships navigate close to dynamic surface targets, a hierarchical path planning method integrating the improved A* algorithm, rapid reverse search iterative planning (RRSIP), and Hybrid A* fine-grained planning, aiming to achieve efficient and kinematically compliant dynamic target tracking planning is proposed.
[Method] An improved A* algorithm based on dynamic programming and oriented bounding box obstacle detection is used to plan a global reference path, reducing path length and redundant waypoints. For the dynamic target point, the RRSIP method is proposed, which reuses node information from prior searches via reverse search to quickly iterate and predict the approach point, avoiding global replanning and improving efficiency. Hybrid A* algorithm is introduced near turning points for local refined planning, quickly generating a feasible path that satisfies ship kinematics and approach heading constraints.
[Result] Compared with other typical algorithms, the path length of the improved A* algorithm proposed is reduced by an average of 4.17% and 1.79% respectively. The RRSIP method reduces the iterative planning time by at least 33.9% compared with the FR method. While ensuring path feasibility, the local Hybrid A* planning reduces the time consumption by at least 72.1% compared with the global application.
[Conclusion] The proposed method can effectively solve the problems of real-time performance and kinematic feasibility in dynamic target tracking, and significantly improve the autonomous tracking capability of ships in scenarios such as tugboat escort and maritime police law enforcement.
, correspAuthors=Hongdong WANG, 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=Jingtian HUANG, Yuexin LOU, Yong ZHANG, Jiankun LOU, Hongdong WANG), CN=ArticleExt(id=1254362835149058443, articleId=1254362832145936747, tenantId=1146029695717560320, journalId=1254119036117037056, language=CN, title=抵近姿态约束下的船舶智能跟踪航行规划算法, columnId=1254362825296634520, journalTitle=船舶工程, columnName=专题:智能船舶, runingTitle=null, highlight=null, articleAbstract=
[目的] 针对船舶抵近跟踪水面动态目标航行场景中的避碰路径规划与运动约束协同优化难题,
[方法] 提出一种融合改进A*算法、快速反向搜索迭代规划(RRSIP)方法和Hybrid A*精细规划方法的分层路径规划方法,实现高效且符合运动学规律的动态目标跟踪规划。采用基于动态规划和有向包围盒障碍检测的改进A*算法规划路径,减少路径长度和冗余路径点;针对动态路径终点提出基于RRSIP的路径迭代方法,通过反向搜索复用节点信息快速迭代预测抵近点,避免重复搜索,提升搜索效率;在路径转向点附近引入Hybrid A*算法进行局部精细规划,快速生成满足船舶操纵运动学要求和抵近艏向约束的可行路径。通过多组仿真试验验证各方法及整体算法的性能,
[结果] 结果表明:改进A*算法相比其他典型算法路径长度分别平均减少4.17%和1.79%;RRSIP方法相比FR方法能使迭代规划耗时至少缩短33.9%;局部Hybrid A*规划相比全局应用,能在保证路径可行性的同时,使耗时至少缩短72.1%。
[结论] 所提方法能有效解决动态目标跟踪中的实时性和运动可行性问题,显著提升船舶在拖船伴航和海警执法等场景下的自主跟踪能力。
, correspAuthors=王鸿东, authorNote=null, correspAuthorsNote=
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本刊已许可中国学术期刊(光盘版)电子杂志社在中国知网及其系列数据库产品中以数字化方式复制、汇编、发行、信息网络传播本刊全文,本刊著作权使用费与本刊稿酬一并支付;本刊所刊登文章均无知识产权争议,不涉及泄露国家秘密、技术秘密或商业秘密。作者向本刊提交文章发表的行为即视为同意我编辑部上述声明,文责自负。, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=HUKSl2eUlo3cydMEZcB/WA==, magXml=cMCFUPk4/QOz7ds4XLsOMg==, pdfUrl=null, pdf=OIUx6N83DK6+7jrRerKQWg==, pdfFileSize=3515069, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=Bc+nkOvxkBvTemp5pADpDw==, mapNumber=null, authorCompany=null, fund=null, authors=
黄靖添(2001—),男,硕士研究生。研究方向:智能船舶路径规划。
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