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Article(id=1262079399810162869, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, articleNumber=null, orderNo=null, doi=10.19651/j.cnki.emt.2519398, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1752768000000, receivedDateStr=2025-07-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1778832777215, onlineDateStr=2026-05-15, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1778832777215, onlineIssueDateStr=2026-05-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1778832777215, creator=13701087609, updateTime=1778832777215, updator=13701087609, issue=Issue{id=1262079396291141802, tenantId=1146029695717560320, journalId=1260987750510510108, year='2026', volume='49', issue='6', pageStart='1', pageEnd='256', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1778832776377, creator=13701087609, updateTime=1778832914473, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1262079975688122904, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1262079975688122905, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=123, endPage=134, ext={EN=ArticleExt(id=1262079400607080634, articleId=1262079399810162869, tenantId=1146029695717560320, journalId=1260987750510510108, language=EN, title=Research on path planning algorithm based on improved Bi-RRT, columnId=1262079399856300214, journalTitle=Electronic Measurement Technology, columnName=Theory and Algorithms, runingTitle=null, highlight=null, articleAbstract=

To address the low search efficiency, slow convergence speed, and limited path expansion diversity of the RRT family of algorithms, an adaptive multi-strategy dynamic step-size algorithm, AMDS-Bi-RRT*, is proposed. Based on the Bi-RRT* framework, the algorithm enhances convergence efficiency through a dynamic goal-directed extension strategy and an adaptive step-size evaluation function. A multi-directional emergency maneuver strategy is designed to improve adaptability in complex environments. Meanwhile, node sampling is optimized using an improved artificial potential field method, and a three-stage path smoothing approach is introduced to ensure path feasibility. Comparative experiments conducted in four simulation environments of varying complexity against five benchmark algorithms—Bi-APF-RRT*, Bi-RRT*, APF-RRT*, RRT*, and goal-biased RRT*—demonstrate that AMDS-Bi-RRT* reduces average planning time by 12.22%~23.45%, shortens average path length by 0.88%~1.89%, and decreases the average number of nodes by 6.69%~22.85%. The results verify that AMDS-Bi-RRT* outperforms the comparison algorithms in planning efficiency, path quality, and convergence speed, confirming its superior performance across diverse environments.

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针对RRT系列算法存在的搜索效率低、收敛速度慢及路径扩展多样性不足问题，提出了一种自适应多策略动态步长算法AMDS-Bi-RRT*，该算法基于Bi-RRT*框架，通过动态目标直连扩展策略和构建自适应步长评价函数提升收敛效率；设计多方向应急机动策略，以增强复杂环境的适用性；结合改进的人工势场法优化节点采样；并引入三阶段路径平滑方法以确保路径的可行性。通过在4种不同复杂度仿真环境中与Bi-APF-RRT*、Bi-RRT*、APF-RRT*、RRT*、目标偏置RRT*5种基准算法进行对比实验，结果表明：AMDS-Bi-RRT*的平均规划时间平均降低了12.22%～23.45%，平均路径长度平均缩短了0.88%～1.89%，平均节点数量平均减少了6.69%～22.85%。该算法在规划效率、路径质量、收敛速度上均优于对比算法，充分验证了其在不同环境下的优越性。

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何春光（通信作者），博士，讲师，主要研究方向为智能交通系统、交通系统建模与优化。E-mail:
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吐尔逊·买买提，博士，副教授，主要研究方向为智能交通、计算机算法。E-mail:

刘相铄，硕士研究生，主要研究方向为智能交通、路径规划。E-mail:

杨秋菊，硕士，高级工程师，主要研究方向为智慧物流、公路工程。E-mail:

段婷，硕士，工程师，主要研究方向为公路工程科技及技术推广工作。E-mail:

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吐尔逊·买买提，博士，副教授，主要研究方向为智能交通、计算机算法。E-mail:

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刘相铄，硕士研究生，主要研究方向为智能交通、路径规划。E-mail:

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刘相铄，硕士研究生，主要研究方向为智能交通、路径规划。E-mail:

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段婷，硕士，工程师，主要研究方向为公路工程科技及技术推广工作。E-mail:

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基于改进Bi-RRT的路径规划算法研究
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吐尔逊·买买提 1 , 刘相铄 1 , 何春光 1 , 杨秋菊 2 , 段婷 2
电子测量技术 | 理论与算法 2026,49(6): 123-134
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电子测量技术 | 理论与算法 2026, 49(6): 123-134
基于改进Bi-RRT的路径规划算法研究
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吐尔逊·买买提1 , 刘相铄1 , 何春光1 , 杨秋菊2 , 段婷2
作者信息
  • 1．新疆农业大学交通与物流工程学院 乌鲁木齐 830052
  • 2．新疆交通建设集团股份有限公司 乌鲁木齐 830016

    • 吐尔逊·买买提，博士，副教授，主要研究方向为智能交通、计算机算法。E-mail:

    刘相铄，硕士研究生，主要研究方向为智能交通、路径规划。E-mail:

    杨秋菊，硕士，高级工程师，主要研究方向为智慧物流、公路工程。E-mail:

    段婷，硕士，工程师，主要研究方向为公路工程科技及技术推广工作。E-mail:

通讯作者:

何春光（通信作者），博士，讲师，主要研究方向为智能交通系统、交通系统建模与优化。E-mail:
Research on path planning algorithm based on improved Bi-RRT
Mamat Tursun1 , Xiangshuo Liu1 , Chunguang He1 , Qiuju Yang2 , Ting Duan2
Affiliations
  • 1.College of Transportation and Logistics Engineering, Xinjiang Agricultural University, Urumqi 830052, China
  • 2.Xinjiang Communications Construction Group Co., Ltd., Urumqi 830016, China
doi: 10.19651/j.cnki.emt.2519398
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摘要
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针对RRT系列算法存在的搜索效率低、收敛速度慢及路径扩展多样性不足问题，提出了一种自适应多策略动态步长算法AMDS-Bi-RRT*，该算法基于Bi-RRT*框架，通过动态目标直连扩展策略和构建自适应步长评价函数提升收敛效率；设计多方向应急机动策略，以增强复杂环境的适用性；结合改进的人工势场法优化节点采样；并引入三阶段路径平滑方法以确保路径的可行性。通过在4种不同复杂度仿真环境中与Bi-APF-RRT*、Bi-RRT*、APF-RRT*、RRT*、目标偏置RRT*5种基准算法进行对比实验，结果表明：AMDS-Bi-RRT*的平均规划时间平均降低了12.22%～23.45%，平均路径长度平均缩短了0.88%～1.89%，平均节点数量平均减少了6.69%～22.85%。该算法在规划效率、路径质量、收敛速度上均优于对比算法，充分验证了其在不同环境下的优越性。

Bi-RRT*  /  AMDS-Bi-RRT*  /  自适应步长  /  动态目标直连  /  多方向应急机动策略  /  三阶段路径平滑

To address the low search efficiency, slow convergence speed, and limited path expansion diversity of the RRT family of algorithms, an adaptive multi-strategy dynamic step-size algorithm, AMDS-Bi-RRT*, is proposed. Based on the Bi-RRT* framework, the algorithm enhances convergence efficiency through a dynamic goal-directed extension strategy and an adaptive step-size evaluation function. A multi-directional emergency maneuver strategy is designed to improve adaptability in complex environments. Meanwhile, node sampling is optimized using an improved artificial potential field method, and a three-stage path smoothing approach is introduced to ensure path feasibility. Comparative experiments conducted in four simulation environments of varying complexity against five benchmark algorithms—Bi-APF-RRT*, Bi-RRT*, APF-RRT*, RRT*, and goal-biased RRT*—demonstrate that AMDS-Bi-RRT* reduces average planning time by 12.22%~23.45%, shortens average path length by 0.88%~1.89%, and decreases the average number of nodes by 6.69%~22.85%. The results verify that AMDS-Bi-RRT* outperforms the comparison algorithms in planning efficiency, path quality, and convergence speed, confirming its superior performance across diverse environments.

Bi-RRT*  /  AMDS-Bi-RRT*  /  adaptive step size  /  dynamic goal-directed extension  /  multi-directional emergency maneuver strategy  /  three-stage path smoothing
吐尔逊·买买提, 刘相铄, 何春光, 杨秋菊, 段婷. 基于改进Bi-RRT的路径规划算法研究. 电子测量技术, 2026 , 49 (6) : 123 -134 . DOI: 10.19651/j.cnki.emt.2519398
Mamat Tursun, Xiangshuo Liu, Chunguang He, Qiuju Yang, Ting Duan. Research on path planning algorithm based on improved Bi-RRT[J]. Electronic Measurement Technology, 2026 , 49 (6) : 123 -134 . DOI: 10.19651/j.cnki.emt.2519398
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  • 新疆维吾尔自治区重点研发计划项目(2022B01013-2)
  • 新疆维吾尔自治区面上项目(2024D01A65)
2026年第49卷第6期
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doi: 10.19651/j.cnki.emt.2519398
  • 接收时间：2025-07-18
  • 首发时间：2026-05-15
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  • 收稿日期：2025-07-18
基金
新疆维吾尔自治区重点研发计划项目(2022B01013-2)
新疆维吾尔自治区面上项目(2024D01A65)
作者信息
    1．新疆农业大学交通与物流工程学院 乌鲁木齐 830052
    2．新疆交通建设集团股份有限公司 乌鲁木齐 830016

通讯作者:

何春光（通信作者），博士，讲师，主要研究方向为智能交通系统、交通系统建模与优化。E-mail:
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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