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

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.

Structural topic modeling-based systematic review for service robots’ fault diagnosis: content, popularity and regional differences

“…As one of the biggest academic databases, WoS provides comprehensive coverage of scientific literature worldwide, including those from Asia-Pacific, Europe, and the Americas. Several high-quality reviews also used the WoS as the database for their study [23][24][25]. This review used the settled keyword to search literature on WoS, and the search formula used is as follows:…”

Autonomous detection and loading of ore piles with load–haul–dump machines in Room & Pillar mines

“…The automation of equipment is an important goal in today's mining, as it improves the safety, productivity, and reliability of operations (Ali & Frimpong, 2020; ASI, 2023; ENAEX, 2023; Epiroc, 2023a; HITACHI, 2023; Komatsu, 2023). This is especially important in underground mining, where workers are exposed to risks like rock falls, mud rushes, and continued exposure to dust (Salvador et al, 2020; Xiao et al, 2022).…”

“…A load–haul–dump (LHD) is an articulated vehicle, with a frontal bucket, used for the loading, hauling, and dumping of material inside mines. LHD automation is considered to grow in the near future (Cognitive‐Market‐Research, 2023) Both autonomous navigation of LHDs and autonomous loading have been the subject of research (Androulakis, 2021; Backman et al, 2021; Dobson et al, 2017; Gu et al, 2019; Huang et al, 2021; Li & Zhan, 2018; Marshall, Barfoot, et al, 2008; Mascaró et al, 2021; Tampier et al, 2021; Xiao et al, 2022).…”

“…[Correction added on 8 June 2023, after first online publication: In equation 4, the terms "max" have been changed to "min" in this version.] While autonomous navigation of LHDs has been successfully applied in several underground operations worldwide (Epiroc, 2023b;RCT, 2023;Sandvik, 2023), the autonomous loading of material using LHD is still not a mature commercial technology (Xiao et al, 2022). This fact is explained because its application depends on several factors, like the difficulty of modeling the interaction between the LHD and the ore pile due to the different properties of the material to be loaded, and differences in the layout of the mines (width and height of the tunnels, draw points characteristics, etc.)…”

Trend analysis of traffic management based on literature data mining and graph analysis tools

“…Currently used bibliometric analysis software includes CiteSpace, VOSviewer, Pajeck, Histcite and BibExcel. Among the above‐mentioned software, CiteSpace and VOSviewer mainly draw knowledge graphs based on the principle of literature cocitation and have the advantages of a comprehensive analysis function and good visualization effect [8]. Using CiteSpace and VOSviewer to perform bibliometric analysis has gradually emerged in recent years with the support of big data, which has been widely used in traffic flow prediction [9], heterogeneous traffic flow [10], traffic safety analysis [11], and simulated driving [12].…”