Article(id=1314907865047990988, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907656704324517, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-6099.2026.01.002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760976000000, receivedDateStr=2025-10-21, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791428064834, onlineDateStr=2026-10-08, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791428064834, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791428064834, creator=13701087609, updateTime=1791428064834, updator=13701087609, issue=Issue{id=1314907656704324517, tenantId=1146029695717560320, journalId=1235980550691926019, year='2026', volume='46', issue='1', pageStart='1', pageEnd='206', issueExtLink='null', onlineDate='null', pubDate='1769875200000', pubDateStr='2026-02-01', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791428015160, creator='13701087609', updateTime=1791430491940, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314918045139690301, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907656704324517, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314918045139690302, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907656704324517, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=7, endPage=16, ext={EN=ArticleExt(id=1314907866369196749, articleId=1314907865047990988, tenantId=1146029695717560320, journalId=1235980550691926019, language=EN, title=Path Planning for Deep-Sea Collector Vehicles Based on Integration of RRT Algorithm and Artificial Potential Field Algorithm, columnId=1236276106018484431, journalTitle=Mining and Metallurgical Engineering, columnName=MINING, runingTitle=null, highlight=null, articleAbstract=

An improved algorithm combining Rapidly-Exploring Random Trees (RRT) and Artificial Potential Field (APF) was proposed for obtaining an overall path planning for deep-sea collector vehicles. Based on the kinematic model of the collector vehicle, the RRT sampling mechanism was optimized by a target-biased strategy, and an adaptive repulsive coefficient was introduced to improve the potential field function, thereby enhancing obstacle avoidance and target reachability. Verification was carried out by simulation under two different working conditions. The results show that in Condition 1 (complex environment), the RRT^*-APF algorithm has its searching time shortened by 73.1%, the number of expanded nodes reduced by 50.7%, and the path length shortened from 1 542.35 m to 1 486.16 m compared to the RRT^* algorithm, being more suitable for complex environments. In Condition 2 (simple environment), the RRT-APF algorithm has its searching time shortened by 67.4%, the number of expanded nodes reduced by 52.6%, and the path length shortened from 973.58 m to 910.27 m compared to the traditional RRT algorithm, being more suitable for simple environments.

, authors=Qiaoyu CUI, Saifeng PENG, Hao ZHENG, Rui WANG, authorsList=Qiaoyu CUI, Saifeng PENG, Hao ZHENG, Rui WANG, 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=1314907867963032270, articleId=1314907865047990988, tenantId=1146029695717560320, journalId=1235980550691926019, language=CN, title=基于融合RRT算法和人工势场法的深海采矿车路径规划研究, columnId=1236276106219811026, journalTitle=矿冶工程杂志, columnName=采矿, runingTitle=null, highlight=null, articleAbstract=

针对深海采矿车全局路径规划问题，提出融合快速扩展随机树（RRT）与人工势场法（APF）的改进算法。基于采矿车运动学模型，通过目标偏向策略优化RRT采样机制，引入自适应斥力系数改进势场函数，提升避障性能与目标可达性；并在两种不同工况下进行仿真验证。结果表明，工况1（复杂环境）中，RRT^*-APF改进算法较RRT^*算法搜索时间缩短73.1%、扩展节点数减少50.7%，路径长度从1 542.35 m缩短至1 486.16 m，更适用于复杂环境；工况2（简单环境）中，RRT-APF改进算法较传统RRT算法搜索时间缩短67.4%、扩展节点数减少52.6%，路径长度从973.58 m缩短至910.27 m，更适用于简单环境。

, authors=崔乔宇, 彭赛锋, 郑皓, 王蕊, authorsList=崔乔宇, 彭赛锋, 郑皓, 王蕊, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=Ocq1f4vcWSKVXJhqBbzmLA==, magXml=92gP9WmsR18aF88kpBjBRA==, pdfUrl=null, pdf=c/tJmStBuLYjPKTKqqdoAg==, pdfFileSize=5049207, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=sn9NhRaWOUkrMgphlOkcIA==, mapNumber=null, fund=null)}, authors=[Author(id=1314907868424405715, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1314907865047990988, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=1297332259@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1314907868541846229, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1314907865047990988, authorId=1314907868424405715, language=EN, stringName=Qiaoyu CUI, firstName=Qiaoyu, middleName=null, lastName=CUI, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=State Key Laboratory of Exploitation and Utilization of Deep-Sea Mineral Resources, Changsha Research Institute of Mining and Metallurgy Co., Ltd., Changsha 410012, Hunan, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1314907868747367126, tenantId=1146029695717560320, journalId=1235980550691926019, articleId=1314907865047990988, authorId=1314907868424405715, language=CN, stringName=崔乔宇, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=长沙矿冶研究院有限责任公司深海矿产资源开发利用技术国家重点实验室，湖南 长沙 410012, bio={"content":"

崔乔宇（2000—），男，湖北安陆人，硕士研究生，主要研究方向为深海采矿车路径规划。E-mail: 1297332259@qq.com

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崔乔宇（2000—），男，湖北安陆人，硕士研究生，主要研究方向为深海采矿车路径规划。E-mail: 1297332259@qq.com

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