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Current path planning algorithms for mine cars generally suffer from low search efficiency and excessively long planned paths when navigating complex mining terrain. Developing highly efficient and precise path planning methods has thus become a critical issue requiring urgent resolution. An innovative two-layer transportation path planning scheme was proposed. The upper layer employed the improved A^* algorithm to initially plan the paths between nodes, while the lower layer utilized the improved sparrow search algorithm (ISSA) for overall path optimization. This ensured the minimization of energy consumption and cost for the overall path, aiming to achieve efficient and precise mining transportation path planning. The results show that the two-layer transportation path planning scheme based on the improved A^* algorithm and ISSA not only realizes the minimization of path energy consumption, but also minimizes the path cost, effectively enhancing the efficiency and safety of path planning. In the path planning process, the proposed scheme by the research institute will not be affected by obstacles, there will be no situation of taking a detour, and it can reach the destination along the shortest path. It has excellent obstacle avoidance and shortest path planning capabilities. Moreover, in the three-dimensional topographic map of the mine with complex terrain, it can quickly solve the target function value with lower energy consumption and lower path cost. The shortest transportation distance obtained by the two-layer transportation path planning scheme based on the improved A^* algorithm and ISSA is only 3 125.36 m. The cargo damage cost, transportation cost, and carbon emission cost are 1 028.63 yuan, 3 968.57 yuan, and 335.18 yuan, respectively, and the transportation time is only 1 836.39 s. All these indicators are lower than those of the mine transportation path planning scheme based on the genetic-ant colony reinforcement learning algorithm.

, authors=Xiaofei WANG¹, Xinhong YU², authorsList=Xiaofei WANG, Xinhong YU, authorCompany=null, correspAuthors=Xinhong YU, 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=1322559934663869238, articleId=1322559934319936308, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于改进A^*算法与ISSA的矿山复杂地形环境两层运输路径规划研究, 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=

当前矿车路径规划算法在面对矿山复杂地形时，普遍存在搜索效率低、规划路径长等问题，研发高效精准的路径规划方法成为亟待解决的关键问题。创新性地提出了两层运输路径规划方案，上层采用改进A^*算法初步规划节点间的路径，下层采用改进麻雀搜索算法（ISSA）进行整体路径优化，确保整体路径的能耗与代价最小化，以期实现高效精准的矿山运输路径规划。结果表明：改进A^*算法与ISSA的两层运输路径规划方案不仅实现了路径能耗最小化，还实现了路径代价最小化，有效提升了路径规划效率与安全性；在路径规划中，研究所提方案不会受到障碍物干扰，不存在绕远路的情况，能够以最短的路径到达目的地，具有良好的避障与最短路径规划能力，且在具有复杂地形的矿山三维地形图中，能够快速求解出能耗更低、路径代价更低的目标函数值；改进A^*算法与ISSA的两层运输路径规划方案求解的最短运输距离仅为3 125.36 m，货损、运输及碳排放成本分别为1 028.63元、3 968.57元、335.18元，运输时间仅为1 836.39 s，均低于基于遗传蚁群强化学习算法的矿山运输路径规划方案。

, authors=王晓菲¹, 于新红², authorsList=王晓菲, 于新红, authorCompany=null, correspAuthors=于新红, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=qO92dAvnmZp2HOjlx1aw9g==, magXml=DTdiQVMejR7kPi+NAlQWFQ==, pdfUrl=null, pdf=Qi9pKH1PqUcj/Je5+dwYig==, pdfFileSize=2704359, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=5WzIJI5kZjQQsi0R/hugDA==, mapNumber=null, fund=null)}, authors=[Author(id=1322559935062328126, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559934319936308, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=zkzywxf@zkvtc.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322559935137825600, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559934319936308, authorId=1322559935062328126, language=EN, stringName=Xiaofei WANG, firstName=Xiaofei, middleName=null, lastName=WANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.School of Information Engineering, Zhoukou Polytechnic, Zhoukou, Henan 466000, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322559936823935809, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559934319936308, authorId=1322559935062328126, language=CN, stringName=王晓菲, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．周口职业技术学院 信息工程学院，河南 周口市　466000, bio={"content":"

王晓菲（1988—），男，河南周口人，硕士，讲师，研究方向为计算机应用。E-mail: zkzywxf@zkvtc.edu.cn

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王晓菲（1988—），男，河南周口人，硕士，讲师，研究方向为计算机应用。E-mail: zkzywxf@zkvtc.edu.cn

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