Article(id=1309640042084135685, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20260077, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1773590400000, revisedDateStr=2026-03-16, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172117968, onlineDateStr=2026-09-23, pubDate=1782230400000, pubDateStr=2026-06-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172117968, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172117968, creator=13701087609, updateTime=1790172117968, updator=13701087609, issue=Issue{id=1309640041656316677, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='6', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1782230400000', pubDateStr='2026-06-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172117866, creator='13701087609', updateTime=1790232797347, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894549917495577, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894549917495578, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=11, endPage=20, ext={EN=ArticleExt(id=1309640042302239495, articleId=1309640042084135685, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Bi-RRT* Path Planning Algorithm Based on Guided Connection and Artificial Potential Field, columnId=1309640042239324934, journalTitle=Automobile Technology, columnName=Special Topic on Intelligent Driving Path Planning Technology for Complex Scenarios, runingTitle=null, highlight=null, articleAbstract=

To address the issues of strong search blindness and poor path feasibility in the traditional Bi-RRT* algorithm, this paper proposes an improved approach named GC-APF-Bi-RRT* (Bi-RRT* based on Guided Connection and Artificial Potential Field). The algorithm first establishes a collision model for vehicles considering four-wheel steering vehicles. It then overcomes the problems of sampling difficulties and reciprocating paths by extracting environmental guide points and employing a search-based connection strategy constrained by direction consistency. Furthermore, an improved APF integrated with a goal-distance factor is introduced to guide the tree growth direction. Finally, greedy pruning and B-spline smoothing techniques are applied to optimize the trajectory. Simulation results demonstrate that in complex environments, compared to traditional RRT, RRT*, RRT-Connect, Bi-RRT*, and Goal-Biased RRT*, the proposed algorithm reduces path length by 19.45%, 9.94%, 13.85%, 4.10%, and 3.61%, respectively, and shortens search time by 62.57%, 65.15%, 0.97%, 66.63%, and 71.28%, respectively.

, authors=Limin Huang¹, Jiaxin Ding¹, Yifeng Guo¹, Hao Yang¹, Kai Deng², authorsList=Limin Huang, Jiaxin Ding, Yifeng Guo, Hao Yang, Kai Deng, authorCompany=null, correspAuthors=null, 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=1309640042562286345, articleId=1309640042084135685, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于引导连接与人工势场的Bi-RRT*路径规划算法, columnId=1309640042377736968, journalTitle=汽车技术, columnName=复杂场景中智能驾驶路径规划技术专题, runingTitle=null, highlight=null, articleAbstract=

针对Bi-RRT*算法存在搜索盲目性强及路径可行性差等问题，提出一种基于引导连接和人工势场（APF）的Bi-RRT*算法。建立四轮转向车辆碰撞模型，通过提取环境引导点及方向一致性约束的搜索式连接，解决采样困难与折返路径问题，引入带目标距离因子的改进APF以引导生长方向，并采用贪婪剪枝与B样条平滑技术优化路径。试验结果表明：在复杂环境工况下，相较于传统RRT、RRT*、RRT-connect算法Bi-RRT*和Goal-Biased RRT*，所提出方案在路径长度上分别缩短了19.45%、9.94%、13.85%、4.10%和3.61%，在搜索时间上分别缩短了62.57%、65.15%、0.97%、66.63%和71.28%。

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