Article(id=1309639998861836330, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250142, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1777046400000, revisedDateStr=2026-04-25, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172107662, onlineDateStr=2026-09-23, pubDate=1787500800000, pubDateStr=2026-08-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172107662, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172107662, creator=13701087609, updateTime=1790172107662, updator=13701087609, issue=Issue{id=1309639998379491369, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='8', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1787500800000', pubDateStr='2026-08-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172107547, creator='13701087609', updateTime=1790232702427, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894151819330406, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894151819330407, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=11, ext={EN=ArticleExt(id=1309639999042191403, articleId=1309639998861836330, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Research on Path Planning for Intelligent Vehicles Based on Improved RRT* Algorithm, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the issues of low efficiency, poor quality of random sampling points, and suboptimal path quality in the traditional RRT* algorithm during path exploration, this paper proposes a method that integrates an improved artificial potential field formulation with an adaptive step-size bidirectional expansion mechanism. By introducing an adaptive attraction coefficient and dual repulsion forces, the proposed method optimizes path guidance and obstacle avoidance. A dynamic step-size adjustment strategy and a bidirectional tree cooperative search mechanism are employed to reduce redundant nodes and accelerate convergence. A greedy algorithm is used to remove redundant nodes, ensuring path smoothness and continuity. Additionally, an adaptive goal-biasing strategy based on environmental density is introduced to enhance goal orientation and exploration efficiency. Experimental results demonstrate that, compared with the traditional RRT* algorithm and other improved algorithms, the proposed ABF-RRT* algorithm reduces the average path length by 17.7%, decreases the average number of nodes by 90.6%, and shortens the average running time by 95.5% in complex environments, verifying its stability and superiority.

, authors=Zhiwei Guan, Xiaoyu Lu, Yu Zhao, authorsList=Zhiwei Guan, Xiaoyu Lu, Yu Zhao, 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=1309639999373541420, articleId=1309639998861836330, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于改进RRT*算法的智能车路径规划研究, columnId=0, journalTitle=汽车技术, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对传统RRT*算法在路径探索过程中存在的效率低、随机采样点质量差和路径质量不佳等问题，提出一种结合改进人工势场与自适应步长双向扩展机制的方法。通过自适应引力系数和双重斥力，优化路径引导与避障；采用动态步长调整策略和双向树协同搜索机制，减少冗余节点并加速收敛；利用贪婪算法去除冗余点，保证路径的平滑性和连续性；引入基于环境密度的自适应目标偏置策略，增强目标导向性和探索效率。试验结果表明：在复杂环境中，相较于传统RRT*及其他改进算法，ABF-RRT*平均路径长度缩短17.7%，平均节点数减少90.6%，运行时长缩短95.5%，具有稳定性与优越性。

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