Article(id=1309639669369893773, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639664567415657, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20240962, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1735315200000, revisedDateStr=2024-12-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172029105, onlineDateStr=2026-09-23, pubDate=1774281600000, pubDateStr=2026-03-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172029105, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172029105, creator=13701087609, updateTime=1790172029105, updator=13701087609, issue=Issue{id=1309639664567415657, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='3', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1774281600000', pubDateStr='2026-03-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172027961, creator='13701087609', updateTime=1790232938103, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309895140290945772, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639664567415657, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309895140290945773, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639664567415657, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=7, ext={EN=ArticleExt(id=1309639669558637454, articleId=1309639669369893773, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Dynamic Path Planning Algorithm Based on Hybrid A* with Variable Resolution and Reeds-Shepp Curve with Variable Curvature, columnId=1309639665217532778, journalTitle=Automobile Technology, columnName=Special Topic on Dynamic Trajectory Prediction, Planning, and Tracking Control Technology of Intelligent Vehicle, runingTitle=null, highlight=null, articleAbstract=

This paper aims to improve the existing hybrid A* algorithm in view of the issues such as excessive reliance on resolution and paths being overly close to obstacles. Firstly, the distance calculated by KD-Tree is employed as a penalty term and incorporated into the cost function of the hybrid A* algorithm. Secondly, the node expansion distance is dynamically modified in accordance with the distance between the vehicle and the obstacle to realize the dynamic expansion of nodes, ensuring search efficiency while enhancing safety. Then, each node undergoes curvature-based cyclic checking to recall nodes that meet the Reeds-Shepp (RS) curve generation conditions but are previously overlooked due to fixed RS curve curvature. Finally, the curvature of the RS curve is dynamically altered based on the distance between the vehicle and obstacles, enabling the generated RS curve to have a moderate curvature and maintain a safe distance from the obstacles. Experiments demonstrate that, compared with traditional algorithms, the proposed algorithm shortens the search time by 7.18%, reduces the maximum curvature by 63.63%, and increases the minimum distance between the path and the obstacle by 143.94%, effectively enhancing the quality of the generated path.

, authors=Bingli Zhang¹, Zhonghan Huang¹, Ren Li², Yunjie Zuo¹, authorsList=Bingli Zhang, Zhonghan Huang, Ren Li, Yunjie Zuo, 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=1309639669785129871, articleId=1309639669369893773, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于可变分辨率混合A*和可变曲率RS曲线融合的动态路径规划方法, columnId=1309639665360139116, journalTitle=汽车技术, columnName=智能车辆动态轨迹预测、规划与跟踪控制技术专题, runingTitle=null, highlight=null, articleAbstract=

针对现有混合A*路径规划算法过于依赖分辨率、路径与障碍物距离过近等问题，首先，以KD树（KD-Tree）计算出的距离作为惩罚项，补充到混合A*算法的代价函数中，其次，根据车辆与障碍物之间的距离动态改变节点扩展距离，在保证搜索效率的同时提高安全性，然后，对每个节点按曲率进行循环检测，召回满足RS曲线生成条件但由于固定RS曲线曲率而被忽视的节点，最后，根据车辆与障碍物之间的距离动态改变RS曲线的曲率，使生成的RS曲线曲率适中且与障碍物保持安全距离。仿真验证结果表明，与传统算法相比，所提出算法的搜索时间缩短了7.18%、最大曲率减小了63.63%、路径与障碍物的最近距离增加了143.94%，有效提高了生成路径的质量。

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