Article(id=1311351102906401172, tenantId=1146029695717560320, journalId=1146123346816638986, issueId=1311351083973305284, articleNumber=null, orderNo=null, doi=10.13426/j.cnki.yky.2025.02.05, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740499200000, receivedDateStr=2025-02-26, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790580066658, onlineDateStr=2026-09-28, pubDate=1763568000000, pubDateStr=2025-11-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790580066658, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790580066658, creator=13701087609, updateTime=1790580066658, updator=13701087609, issue=Issue{id=1311351083973305284, tenantId=1146029695717560320, journalId=1146123346816638986, year='2025', volume='44', issue='4', pageStart='1', pageEnd='158', issueExtLink='null', onlineDate='null', pubDate='1763568000000', pubDateStr='2025-11-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790580062144, creator='13701087609', updateTime=1790583973598, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311367489863381299, tenantId=1146029695717560320, journalId=1146123346816638986, issueId=1311351083973305284, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311367489863381300, tenantId=1146029695717560320, journalId=1146123346816638986, issueId=1311351083973305284, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=112, endPage=118, ext={EN=ArticleExt(id=1311351103170642325, articleId=1311351102906401172, tenantId=1146029695717560320, journalId=1146123346816638986, language=EN, title=Anti Collision Technology for Open-pit Mine Dump Trucks Based on LiDAR and Improved AI Algorithm, columnId=1175805042197152439, journalTitle=Uranium Mining and Metallurgy, columnName=SAFETY AND ENVIRONMENT PROTECTION, runingTitle=null, highlight=null, articleAbstract=

Due to the sudden change in load capacity of open-pit mine dump trucks, the sliding mode variable structure will cause the hydraulic braking system to produce chattering characteristics, making it difficult to accurately determine whether there is overlap between the dump truck and the collision detection object's enclosure box, resulting in a longer response time for collision detection. To address this, a collision prevention technology for open-pit mine dump trucks based on LiDAR and improved AI algorithm was proposed. Using a single laser point cloud spatial neighborhood joint segmentation method to distinguish obstacles and ground points around dump trucks on a grid map, and calibrating the position of the laser radar and mining truck through a coordinate transformation matrix. Using the bounding box algorithm in AI algorithms to perform collision detection between dump trucks and obstacles in a grid map. Through an improved hybrid hierarchical bounding box algorithm, it is determined whether the bounding boxes of dump trucks and collision detection objects overlap for collision detection, quickly eliminating detection objects that are unlikely to collide. If there is a possibility of collision, the cascading braking parking strategy is used to actively stop the dump truck and achieve collision avoidance operation. The experimental results show that using this technology, dump trucks can effectively avoid surrounding obstacles and reach the target point in a short period of time, and the collision detection response time is reduced to 35.4 ms, effectively reducing the collision risk of open-pit mine dump trucks.

, authors=Shixin ZHANG¹, Jiulin FAN¹, Weidong LI², Chongming LIU², authorsList=Shixin ZHANG, Jiulin FAN, Weidong LI, Chongming LIU, 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=1311351103418106262, articleId=1311351102906401172, tenantId=1146029695717560320, journalId=1146123346816638986, language=CN, title=基于激光雷达与改进AI算法的露天矿自卸卡车防撞技术, columnId=1175805042343953080, journalTitle=铀矿冶, columnName=安全·环保, runingTitle=null, highlight=null, articleAbstract=

露天矿自卸卡车在载重突变时，滑模变结构控制会引发液压制动系统抖振，该现象会导致自卸卡车与障碍物的包围盒重叠判断困难，延长碰撞监测系统的检测响应时间。对此问题，提出基于激光雷达与改进AI算法的露天矿自卸卡车防撞技术。该技术采用单束激光点云空间邻域联合分割法，在栅格地图中区分出自卸卡车周边障碍物和地面点，并通过坐标变换矩阵标定激光雷达与矿卡位置。采用AI算法中的包围盒算法，在栅格地图中进行自卸卡车与障碍物的碰撞检测，通过改进的混合层次包围盒算法对自卸卡车与障碍物的包围盒重叠进行碰撞检测判断，快速排除不可能发生碰撞的检测对象；若可能产生碰撞，则使用级联制动的停车策略主动停车，实现自卸卡车的防撞操作。实验结果表明：利用该技术，自卸卡车能在短时间内有效躲避周边障碍物并到达目标点，且碰撞检测响应时间降低至35.4 ms，有效降低了露天矿自卸卡车的碰撞风险。

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张世新（1999-），男，辽宁朝阳人，学士，助理工程师，主要从事露天矿山安全研究。

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张世新（1999-），男，辽宁朝阳人，学士，助理工程师，主要从事露天矿山安全研究。

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