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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 25204

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Special Issue Editors

Dr. Binghao Li

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Guest Editor

School of Minerals and Energy Resources, Faculty of Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Interests: indoor positioning; pedestrian navigation; satellite positioning; navigation and mine Internet of Things
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Serkan Saydam

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Guest Editor

School of Minerals and Energy Resources, Faculty of Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Interests: block cave mining; off earch mining; deep underground mining; fully mechanized mining

Prof. Dr. Jian Wang

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Guest Editor

School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing, China
Interests: GNSS/INS integrated positioning and application; emergency positioning for underground
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mines are vital to the world economy; however, the industry faces challenges due to high costs, a volatile economic environment and more difficult mining conditions. In order to meet these challenges, new technologies have been playing an important role in the mining industry. Modern mining is inseparable from Industry 4.0, which means that Internet of Things and artificial intelligence, etc., will be increasingly applied in all aspects of mining. Communication networks will cover everywhere in the mine sites and processing plants; sensors will therefore be deployed anywhere of interest, and robotics will be widely used to replace workers. I would like to invite all researchers working in the area related to minerals and energy resources sector to submit research papers and critical and insightful review articles for this Special Issue on “Sensors, Robotics and Networks in Mining”. I look forward to reading your contributions.

Dr. Binghao Li
Prof. Dr. Serkan Saydam
Prof. Dr. Jian Wang
Guest Editors

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Keywords

mine Internet of Things
digital twin
automation
navigation and positioning
mine robotics
autonomous vehicles
surface and underground mining networks
automated analytical systems
collision avoidance

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Published Papers (10 papers)

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Research

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15 pages, 1784 KiB

Open AccessArticle

Learning Representative Features by Deep Attention Network for 3D Point Cloud Registration

by Xiaokai Xia, Zhiqiang Fan, Gang Xiao, Fangyue Chen, Yu Liu and Yiheng Hu

Sensors 2023, 23(8), 4123; https://doi.org/10.3390/s23084123 - 20 Apr 2023

Cited by 2 | Viewed by 1566

Abstract

Three-dimensional point cloud registration, which aims to find the transformation that best aligns two point clouds, is a widely studied problem in computer vision with a wide spectrum of applications, such as underground mining. Many learning-based approaches have been developed and have demonstrated their effectiveness for point cloud registration. Particularly, attention-based models have achieved outstanding performance due to the extra contextual information captured by attention mechanisms. To avoid the high computation cost brought by attention mechanisms, an encoder–decoder framework is often employed to hierarchically extract the features where the attention module is only applied in the middle. This leads to the compromised effectiveness of the attention module. To tackle this issue, we propose a novel model with the attention layers embedded in both the encoder and decoder stages. In our model, the self-attentional layers are applied in the encoder to consider the relationship between points inside each point cloud, while the decoder utilizes cross-attentional layers to enrich features with contextual information. Extensive experiments conducted on public datasets prove that our model is able to achieve quality results on a registration task. Full article

(This article belongs to the Special Issue Sensors, Robotics and Networks in Mining)

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33 pages, 3029 KiB

Open AccessArticle

Use-Case-Oriented Evaluation of Wireless Communication Technologies for Advanced Underground Mining Operations

by Marius Theissen, Leonhard Kern, Tobias Hartmann and Elisabeth Clausen

Sensors 2023, 23(7), 3537; https://doi.org/10.3390/s23073537 - 28 Mar 2023

Cited by 9 | Viewed by 3449

Abstract

This work aims to give an overview of wireless communication technologies (WCT) for underground applications. Difficulties regarding the harsh mining environment and operational constraints for WCT implementation and use are discussed. Selected technologies are then classified regarding underground mining-specific use cases in advanced mining operations. Use-case-based application categories such as ‘automation and teleoperation’, ‘tracking and tracing’ and ‘Long-Range Underground Monitoring (LUM)’ are defined. The use cases determine requirements for the operational suitability and also quantify evaluation criteria for the evaluation of WCT. The result is a comparison by category of the wireless technologies, which underlines potentials of different technologies for defined use cases, but it can be concluded that the technology always has to be evaluated within the use case and operational constraints. Full article

(This article belongs to the Special Issue Sensors, Robotics and Networks in Mining)

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15 pages, 22055 KiB

Open AccessArticle

Relief Modeling in the Restoration of Extractive Activities Using Drone Imagery

by Erick Russell, Joan-Cristian Padró, Pau Montero, Cristina Domingo-Marimon and Vicenç Carabassa

Sensors 2023, 23(4), 2097; https://doi.org/10.3390/s23042097 - 13 Feb 2023

Cited by 6 | Viewed by 2222

Abstract

In the field of mine engineering, a cross-section topographic survey is usually carried out to perform volumetric calculations of earth movement in order to restore areas affected by extractive activities. Nowadays, Remote Sensing and Geographical Information System (GIS) technologies make it possible to perform the same work by using indirect methods such as images obtained by photogrammetric flights. In this context, Unmanned Aerial Systems (UAS) are considered a very convenient option to develop mapping projects in short periods of time and to provide quality geospatial information such as Digital Elevation Models (DEM) and orthophotos of centimetric spatial resolution. In the present study, this approach has been applied in a gravel extraction area to obtain data for estimating the filling volume of material required for the restoration of the relief (DEM(r)). The estimation of the DEM(r) is later used to calculate a difference of height values (DEM(r)-DEM) that will serve as a variable in the basic operation of volume calculation. The novelty of the presented method is the simulation of a relief adapted to the surrounding morphology, including the derived channel network and the visibility impact, improving what would be a simple clogging. Likewise, the generation of 3D models allows visualizing a new morphological structure of the relief. The proposed approach, based on GIS tools, allows analyzing water flow connectivity integration of the DEM(r) with the environment and estimating potential landscape impacts from the main focuses of a visual basin, both of which are key aspects of restoration modeling that are not always properly addressed. Full article

(This article belongs to the Special Issue Sensors, Robotics and Networks in Mining)

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22 pages, 14605 KiB

Open AccessArticle

Metaheuristic Approach to Synthesis of Suspension System of Mobile Robot for Mining Infrastructure Inspection

by Mateusz Malarczyk, Marcin Kaminski and Jaroslaw Szrek

Sensors 2022, 22(22), 8839; https://doi.org/10.3390/s22228839 - 15 Nov 2022

Cited by 3 | Viewed by 1827

Abstract

The article describes the problem of geometric synthesis of the inspection robot suspension system, designed for operation in difficult conditions with the presence of scattered obstacles. The exemplary application of a mine infrastructure inspection robot is developed and supported by the ideas. The brief introduction presents current trends, requirements and known design approaches of platforms enabled to cross the obstacles. The idea of a nature-inspired wheel-legged robot is given, and the general outline of its characteristics is provided. Then the general idea of kinematic system elements selection is discussed. The main subject of geometrical synthesis of the chosen four-bar mechanism is described in detail. The mathematical model of the suspension and connections between the parts of the structure is clarified. The well-known analytical approach of brute force search is analyzed and validated. Then the method inspired by the branch and bound algorithm is developed. Finally, a novel application of the nature-inspired algorithm (the Chameleon Swarm Algorithm) to synthesis is proposed. The obtained results are analyzed, and a brief comparison of methods is given. The successful implementation of the algorithm is presented. The obtained results are effectively tested with simulations and experimental tests. The designed structure developed with the CSA is assembled and attached to the prototype of a 14-DOF wheel-legged robot. Furthermore, the principles of walking and the elements forming the control structure were also discussed. The paper is summarized with the description of the developed wheel-legged robot LegVan 1v2. Full article

(This article belongs to the Special Issue Sensors, Robotics and Networks in Mining)

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19 pages, 910 KiB

Open AccessArticle

Measurements and Models of 915 MHz LoRa Radio Propagation in an Underground Gold Mine

by Philip Branch

Sensors 2022, 22(22), 8653; https://doi.org/10.3390/s22228653 - 9 Nov 2022

Cited by 8 | Viewed by 2410

Abstract

Underground mining increasingly relies on wireless communications for its operations. The move to automating many underground mining processes makes an understanding of the propagation characteristics of key wireless technologies underground a topic of considerable importance. LoRa has great potential for communications in underground mines, but data on its propagation are quite scarce. In this paper, we describe our measurements of LoRa radio propagation in an underground gold mine. We took measurements in an extraction tunnel with line of sight and in extraction and access tunnels without line of sight. We observed excellent propagation, both with and without line of sight. Our observations support claims by others that the steel-lined tunnels act as a waveguide. As well as reporting measurements, we also developed models of propagation. For line of sight, we show that pathloss is well modelled by a power law with pathloss index of 1.25 and that variability of signal strength is well modelled by a lognormal distribution. We also successfully modelled propagation without line of sight over short distances using a Fresnel Diffraction and Free Space model. Full article

(This article belongs to the Special Issue Sensors, Robotics and Networks in Mining)

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17 pages, 1977 KiB

Open AccessArticle

Application of Wearable Computer and ASR Technology in an Underground Mine to Support Mine Supervision of the Heavy Machinery Chamber

by Paweł Stefaniak, Maria Stachowiak, Wioletta Koperska, Artur Skoczylas and Paweł Śliwiński

Sensors 2022, 22(19), 7628; https://doi.org/10.3390/s22197628 - 8 Oct 2022

Cited by 4 | Viewed by 1408

Abstract

Systems that use automatic speech recognition in industry are becoming more and more popular. They bring benefits especially in cases when the user’s hands are often busy or the environment does not allow the use of a keyboard. However, the accuracy of algorithms is still a big challenge. The article describes the attempt to use ASR in the underground mining industry as an improvement in the records of work in the heavy machinery chamber by a foreman. Particular attention was paid to the factors that in this case will have a negative impact on speech recognition: the influence of the environment, specialized mining vocabulary, and the learning curve. First, the foreman’s workflow and documentation were recognized. This allowed for the selection of functionalities that should be included in the application. A dictionary of specialized mining vocabulary and a source database were developed which, in combination with the string matching algorithms, aim to improve correct speech recognition. Text mining analysis, machine learning methods were used to create functionalities that provide assistance in registering information. Finally, the prototype of the application was tested in the mining environment and the accuracy of the results were presented. Full article

(This article belongs to the Special Issue Sensors, Robotics and Networks in Mining)

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15 pages, 2579 KiB

Open AccessArticle

Application of Spectral Entropy in Haul Truck Joint Damage Detection

by Paweł Stefaniak, Wioletta Koperska, Artur Skoczylas and Maria Stachowiak

Sensors 2022, 22(19), 7358; https://doi.org/10.3390/s22197358 - 28 Sep 2022

Cited by 5 | Viewed by 1701

Abstract

Early detection of machine failures is often beneficial, both financially and in terms of worker safety. The article presents the problem of frequently damaged joints in haul trucks, which are a real threat to the health and life of drivers. It was decided to investigate the problem in terms of dynamic overloads using two NGIMU inertial sensors and placing them in two places on the machine in close proximity to a joint. The data were captured during the standard operation of various machines in several mining departments, which allowed for the detection of a variety of factors influencing vibration. A hypothesis was developed that any changes in the joint would cause a change in the characteristics of vibrations, which were measured using the spectral entropy of vertical vibrations. Analyses have shown that there is a relationship between the change in spectral entropy difference (between the front and back of the vehicle) and joint events: nut tightening, nut replacement, and even joint fracture and replacement. The presented results offer the potential to create a tool for joint diagnostics and the early detection of damage or backlash. Full article

(This article belongs to the Special Issue Sensors, Robotics and Networks in Mining)

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21 pages, 13545 KiB

Open AccessArticle

Extraction of Step-Feature Lines in Open-Pit Mines Based on UAV Point-Cloud Data

by Yachun Mao, Hui Wang, Wang Cao, Yuwen Fu, Yanhua Fu, Liming He and Nisha Bao

Sensors 2022, 22(15), 5706; https://doi.org/10.3390/s22155706 - 30 Jul 2022

Cited by 3 | Viewed by 1954

Abstract

Step-feature lines are one of the important geometrical elements for drawing the status quo maps of open-pit mines, and the efficient and accurate automatic extraction and updating of step-feature lines is of great significance for open-pit-mine stripping planning and analysis. In this study, an automatic extraction method of step-feature lines in an open-pit mine based on unmanned-aerial-vehicle (UAV) point-cloud data is proposed. The method is mainly used to solve the key problems, such as low accuracy, local-feature-line loss, and the discontinuity of the step-feature-line extraction method. The method first performs the regular raster resampling of the open-pit-mine cloud based on the MLS algorithm, then extracts the step-feature point set by detecting the elevation-gradient change in the resampled point cloud, further traces the step-feature control nodes by the seed-growth tracking algorithm, and finally generates smooth step-feature lines by fitting the space curve to the step-feature control nodes. The results show that the method effectively improves the accuracy of step-feature-line extraction and solves the problems of local-feature-line loss and discontinuity. Full article

(This article belongs to the Special Issue Sensors, Robotics and Networks in Mining)

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Review

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44 pages, 1331 KiB

Open AccessReview

Survey on Exact kNN Queries over High-Dimensional Data Space

by Nimish Ukey, Zhengyi Yang, Binghao Li, Guangjian Zhang, Yiheng Hu and Wenjie Zhang

Sensors 2023, 23(2), 629; https://doi.org/10.3390/s23020629 - 5 Jan 2023

Cited by 30 | Viewed by 5235

Abstract

k nearest neighbours (kNN) queries are fundamental in many applications, ranging from data mining, recommendation system and Internet of Things, to Industry 4.0 framework applications. In mining, specifically, it can be used for the classification of human activities, iterative closest point registration and pattern recognition and has also been helpful for intrusion detection systems and fault detection. Due to the importance of kNN queries, many algorithms have been proposed in the literature, for both static and dynamic data. In this paper, we focus on exact kNN queries and present a comprehensive survey of exact kNN queries. In particular, we study two fundamental types of exact kNN queries: the kNN Search queries and the kNN Join queries. Our survey focuses on exact approaches over high-dimensional data space, which covers 20 kNN Search methods and 9 kNN Join methods. To the best of our knowledge, this is the first work of a comprehensive survey of exact kNN queries over high-dimensional datasets. We specifically categorise the algorithms based on indexing strategies, data and space partitioning strategies, clustering techniques and the computing paradigm. We provide useful insights for the evolution of approaches based on the various categorisation factors, as well as the possibility of further expansion. Lastly, we discuss some open challenges and future research directions. Full article

(This article belongs to the Special Issue Sensors, Robotics and Networks in Mining)

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Other

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6 pages, 1801 KiB

Open AccessPerspective

Application of Ultra-Wide Band Sensors in Mining

by Katja Wisiak, Michel Jakić and Philipp Hartlieb

Sensors 2023, 23(1), 300; https://doi.org/10.3390/s23010300 - 28 Dec 2022

Cited by 5 | Viewed by 2313

Abstract

Ultra-wideband (UWB) sensors are a radio frequency technology that use wireless communication between devices to precisely determine the position. The most recent applications focus on locating and sensor data collecting on mobile phones, car keys and other similar devices. However, this technology is still not being utilized in the mining sector. To overcome this gap, this perspective offers implementation options and solutions. Additionally, it evaluated the benefits and drawbacks of using ultra-wideband for mining. The measurements provided were made using QORVO two-way ranging sensors, and these were compared to theoretical and existing technological solutions. To ensure the optimal use of UWB sensors, a special emphasis was placed on certain influencing factors, such as ways of locating via UWB and factors affecting measurement accuracies, such as the line of sight, multipath propagation, the effect of shielding and the ideal measurement setup. A conducted experiment showed that the most accurate results are obtained when there is no multipath propagation and the arriving signal travels directly from the transmitter to the receiver. Full article

(This article belongs to the Special Issue Sensors, Robotics and Networks in Mining)

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