Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review

Author:

Xiao Wei,Liu Mingxia,Chen Xubing

Abstract

The underground intelligent load-haul-dump vehicle (LHD) is a product of the deep integration of traditional LHD with information network technology, automatic controlling and artificial intelligence technology. It gathers the functions of environmental perception, autonomous driving and fault diagnosis in one machine and exhibits higher safety and greater efficiency than traditional LHD. Hence, it is a particularly important piece of underground mining equipment for building green, safe and smart mines. Taking the studies about intelligent LHD collected by CNKI and WOS databases from 1980 to 2022 as a sample data source, employing Citespace visual analysis software for key feature extraction from the documents, statistical analysis was conducted to clarify the current research progress and the frontier topics of the intelligent LHD academia in the past 40 years, in relation to the future development trends. The development history and application status of underground intelligent LHD was expounded in this article, summarizing the research status at home and abroad from four aspects: ore heap perception and modeling technology, trajectory planning method of bucket shoveling, autonomous navigation technology, real-time monitoring and intelligent fault diagnosis technology. The demerits and merits of the technologies were reviewed as well, with future developing and researching trends of the underground intelligent LHD concluded.

Funder

National Natural Science Foundation of China

Special Project for the Central-Guided-Local Science and Technology Development in Hubei Province

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference107 articles.

1. New development and prospect of key technology in underground mining of metal mines;Wu;Met. Mine,2021

2. The influence of the operating environment on manual and automated load-haul-dump machines: a fault tree analysis

3. Longitudinal and Lateral Trajectory Planning for the Typical Duty Cycle of Autonomous Load Haul Dump

4. Application of intelligent remote control technology for underground mine LHD;Liu;Mod. Min.,2020

5. Current situation and development trend of autonomous shovel loading technology for underground LHD;Jiang;Gold Sci. Technol.,2021

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Path planning for unmanned load–haul–dump machines based on a VHF_A* algorithm;Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science;2024-01-30

2. Joint torque prediction of industrial robots based on PSO-LSTM deep learning;Industrial Robot: the international journal of robotics research and application;2024-01-12

3. Trend analysis of traffic management based on literature data mining and graph analysis tools;IET Intelligent Transport Systems;2023-08-14

4. Machine Learning-Based Shoveling Trajectory Optimization of Wheel Loader for Fuel Consumption Reduction;Applied Sciences;2023-06-28

5. Autonomous detection and loading of ore piles with load–haul–dump machines in Room & Pillar mines;Journal of Field Robotics;2023-05-04

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3