Article(id=1321895047935672729, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321894989857141221, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760630400000, receivedDateStr=2025-10-17, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093939050, onlineDateStr=2026-10-27, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093939050, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093939050, creator=13701087609, updateTime=1793093939050, updator=13701087609, issue=Issue{id=1321894989857141221, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='8', pageStart='1', pageEnd='324', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093925203, creator='13701087609', updateTime=1793094606751, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897848535372687, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321894989857141221, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897848535372688, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321894989857141221, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=254, endPage=265, ext={EN=ArticleExt(id=1321895048204108186, articleId=1321895047935672729, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Design and Application of Unmanned Driving System for Underground Electric Load-Haul-Dump Machines, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The rapid development of autonomous driving technology has continuously expanded its application scenarios, and unmanned driving in mines has become an important direction in intelligent mine construction. Aiming at the problems of complex underground working environments, high manual labor intensity, and prominent safety risks, an unmanned driving system for underground electric Load-Haul-Dump (LHD) machines was designed and implemented by integrating autonomous driving and remote control technologies. The system adopted an operation mode of "remote-controlled loading, autonomous driving for transportation and unloading", realizing the full-process unmanned operation of LHD machines in underground roadway environments. Field industrial test results show that the average lateral tracking error of the system during operation is controlled within 0.30 m, the positioning loss probability is approximately 0.11%, and the average communication latency is less than 5 ms. The system also possesses good safety assurance capabilities, with the access control system and anti-collision detection module effectively handling emergency situations. The research results verify the stability and practicability of the system in underground mine environments, which can effectively reduce miners'labor intensity and improve underground working conditions.

, authors=Zhao Li¹, Yinghao Chai^{2, 3}, Jianlong Dai^{2, 4}, authorsList=Zhao Li, Yinghao Chai, Jianlong Dai, authorCompany=null, correspAuthors=Jianlong Dai, 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=1321895048489320859, articleId=1321895047935672729, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=井下电动铲运机无人驾驶系统设计与应用, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

自动驾驶技术的快速发展使其应用场景持续拓展，矿山无人驾驶已成为智能矿山建设的重要方向之一。针对井下作业环境复杂、人工劳动强度大、安全风险突出等问题，结合自动驾驶与远程遥控技术，设计并实现了一套井下电动铲运机无人驾驶系统。系统采用“远程遥控铲装、自动驾驶运输与卸载”的作业模式，实现了井下巷道环境下铲运机出矿作业的全流程无人化。现场工业试验结果表明：系统在运行过程中平均横向跟踪误差控制在0.30 m以内，定位丢失概率约为0.11%，通信平均时延低于5 ms，且具备良好的安全保障能力，门禁系统和防碰撞检测模块可有效应对突发情况。研究成果验证了该系统在矿山井下环境中的稳定性与实用性，能够有效降低矿工劳动强度，改善井下作业条件。

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黎钊（1987—），男，湖南岳阳人，硕士，高级工程师，主要从事矿山智能化、矿山装备状态监测与系统管理。E-mail:33033722@qq.com

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黎钊（1987—），男，湖南岳阳人，硕士，高级工程师，主要从事矿山智能化、矿山装备状态监测与系统管理。E-mail:33033722@qq.com

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