Article(id=1281608081289221098, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.04.0245, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762185600000, receivedDateStr=2025-11-04, revisedDate=1768579200000, revisedDateStr=2026-01-17, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488777706, onlineDateStr=2026-07-08, pubDate=1777305600000, pubDateStr=2026-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488777706, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488777705, creator=13701087609, updateTime=1783488777705, updator=13701087609, issue=Issue{id=1281608054340829934, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='4', pageStart='1', pageEnd='286', issueExtLink='null', onlineDate='null', pubDate='1777305600000', pubDateStr='2026-04-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783488771281, creator='13701087609', updateTime=1783588648031, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282026967885124001, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282026967885124002, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=194, endPage=203, ext={EN=ArticleExt(id=1281608081503130603, articleId=1281608081289221098, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=AGV path planning based on Voronoi skeleton for narrow roads, columnId=1277328338121261909, journalTitle=China Safety Science Journal, columnName=Public Safety and Emergency Management, runingTitle=null, highlight=null, articleAbstract=

This paper addressed the issues of insufficient safety distance, unsmooth path, and low planning efficiency in AGV path planning in narrow road scenarios. A new path planning method based on Voronoi skeletons for narrow roads was proposed. Firstly, the key nodes of the Voronoi skeleton were extracted to generate a custom Voronoi graph layer, which was then combined with static, obstacle, and expansion layers to iteratively generate a four-layer network structure cost map, accurately distinguishing the influence range of obstacles. Secondly, the new cost map was used as a constraint in conjunction with an improved A^* algorithm for global path planning, guiding the AGV to drive along the center of the road to ensure its safety. Finally, the global path was optimized using B-spline smoothing to improve the AGV's efficiency and stability in navigating narrow roads and other complex scenarios. Experimental results show that, in narrow road scenarios, the safety of the AGV path planning is improved by 82%, the number of spatial corners is reduced by 55.85%, and the path planning time is shortened by 48.98%. The proposed algorithm effectively enhances the robustness, dynamic obstacle avoidance, and real-time performance of path planning in narrow road scenarios, enabling the AGV to move in the safest manner.

, authors=Ping Wang^{1, 2}, Hao Zhang¹, Youming Tang^{**, 3}, Yi Zhang^{1, 2}, authorsList=Ping Wang, Hao Zhang, Youming Tang, Yi Zhang, authorCompany=null, correspAuthors=Youming Tang, 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, fund=null), CN=ArticleExt(id=1281608082304242670, articleId=1281608081289221098, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于Voronoi骨架的狭窄道路AGV路径规划, columnId=1277328338280645463, journalTitle=中国安全科学学报, columnName=公共安全与应急管理, runingTitle=null, highlight=null, articleAbstract=

针对自动导引车（AGV）在狭窄道路场景下路径规划存在安全距离不足、路径不平滑、规划效率低的问题，提出一种基于Voronoi骨架的狭窄道路路径规划方法。首先，提取Voronoi骨架关键节点生成自定义的Voronoi图层，与静态层、障碍物层、膨胀层融合迭代生成4层网络结构的新代价地图，精确区分障碍物的影响范围；然后，以新代价地图为约束结合改进后的A^*算法规划全局路径，引导AGV沿道路中心行驶，保障AGV的行驶安全；最后，对全局路径进行B样条平滑优化，提高AGV通过狭窄道路等复杂场景的高效性和稳定性。结果表明：狭窄道路场景下AGV路径规划平均距离指标的安全性提升82%、空间拐点数减少55.85%、路径规划时间缩短48.98%。该算法有效提升了狭窄道路场景下路径规划的鲁棒性、算法实时性，使AGV能以最安全的方式移动。

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王平（1982—），男，湖南常德人，博士，讲师，主要从事智能汽车设计、自动驾驶等方面的研究。E-mail:wangping3487@163.com。

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王平（1982—），男，湖南常德人，博士，讲师，主要从事智能汽车设计、自动驾驶等方面的研究。E-mail:wangping3487@163.com。

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