Implementation of explosion safety regulations in design of a mobile robot for coal mines

The article focuses on specific challenges of the design of a reconnaissance mobile robotic system aimed for inspection in underground coal mine areas after a catastrophic event. Systems that are designated for these conditions must meet specific standards and regulations. In this paper is discussed primarily the main conception of meeting explosion safety regulations of European Union 2014/34/EU (also called ATEX-from French "Appareils destines a etre utilises en ATmospheres Explosives") for Group I (equipment intended for use in underground mines) and Category M1 (equipment designed for operation in the presence of an explosive atmosphere). An example of a practical solution is described on main subsystems of the mobile robot TeleRescuera teleoperated robot with autonomy functions, a sensory subsystem with multiple cameras, three-dimensional (3D) mapping and sensors for measurement of gas concentration, airflow, relative humidity, and temperatures. Explosion safety is ensured according to the Technical Report CLC/TR 60079-33 "s" by two main independent protections-mechanical protection (flameproof enclosure) and electrical protection (automatic methane detector that disconnects power when methane breaches the enclosure and gets inside the robot body).Web of Science811art. no. 230

Near-Surface Interface Detection for Coal Mining Applications Using Bispectral Features and GPR

The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coal-clay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range

A review of laser scanning for geological and geotechnical applications in underground mining

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment. Although there are several published articles on laser scanning, there is a need to review them in the context of underground mining applications. To this end, a holistic review of laser scanning is presented including progress in 3D scanning systems, data capture/processing techniques and primary applications in underground mines. Laser scanning technology has advanced significantly in terms of mobility and mapping, but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency, dynamics, and environmental influences such as dust and water. Studies suggest that laser scanning has matured over the years for change detection, clearance measurements and structure mapping applications. However, there is scope for improvements in lithology identification, surface parameter measurements, logistic tracking and autonomous navigation. Laser scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer, geodetic networking and processing capacity remain limiting factors. Nevertheless, laser scanners are becoming an integral part of mine automation thanks to their affordability, accuracy and mobility, which should support their widespread usage in years to come

Outdoor and indoor mapping of a mining site by indoor mobile mapping and geo referenced Ground Control Scans

L’articolo descrive le metodologie di rilevamento messe in atto per effettuare il rilievo di una miniera sotterranea, nelle sue componenti indoor e outdoor. La parte esterna della miniera, localizzata in una vallata del Nord Italia, è soggetta a numerosi crolli che stanno interessando alcune abitazioni costruite nei pressi della miniera. Si è reso quindi necessario un rilievo tridimensionale globale per collegare la parte interna sotterranea della miniera con la parte esterna, e per effettuare uno studio geotecnico e geologico del comportamento globale del sito. La tecnologia basata su SLAM è stata scelta come la tecnologia più appropriata per rilevare la sezione sotterranea del sito minerario. Infatti la tecnologia iMMS garantisce la precisione richiesta di 3-4 cm, con la tempistica richiesta. Purtroppo l'accesso alla miniera dismessa era di difficile accesso, perché i due ingressi principali sono stati chiusi con terreno per evitare ingressi abusivi; le dimensioni degli ingressi hanno reso impossibile realizzare una classica rete topografica con stazione totale, misurare punti di controllo all'interno della miniera e collegare l'ambiente esterno a quello interno. L'unico modo trovato per collegare il modello tridimensionale della parte interna della miniera, misurata con iMMS, con quella esterna, è stato quello di applicare l'approccio innovativo dell'utilizzo di Ground Control Scans (GCS). Sono state effettuate diverse scansioni statiche in modo da assicurare un collegamento esterno/interno e le scansioni statiche acquisite nella parte a cielo aperto dei due ingressi minerari, sono state georeferenziate grazie a punti di controllo misurati con stazione totale collegata a vertici misurati con GNSS in RTK. In questo modo il modello 3D acquisito da iMMS è stato collegato alla parte esterna della miniera. L'utilizzo dei GCS è possibile all'interno del software di post-elaborazione SLAM, prima della generazione del modello finale della nuvola di punti. L'utilizzo dei GCS è utile anche per correggere gli effetti di deriva spesso presenti nell'approccio SLAM. Le derive altimetriche, nella parte della miniera sotterranea più lontana dai suoi ingressi, sono state ridotte grazie al trasporto dell'ambiente esterno nella quota interna della miniera, grazie ad un foro di ispezione realizzato a scopo ispettivo. L'esperienza mostra un'interessante integrazione tra diverse tecnologie di rilevamento

Integrated stability mapping system for mines

The Integrated Stability Mapping System (ISMS) was developed as an engineering tool to quantify the geologic and geo-mechanical information of mines, and to integrate the critical stability influence factors into an overall stability index for use in mine planning and support design. It is generally understood that the inherent underground roof stability is determined by the interaction of both the given geologic characteristics and the local stress influences. Form this perspective, in this dissertation, the need for an integrated stability mapping system is established through investigating the traditional and current hazard mapping practices. In order to fulfill this need, computer aided hazard mapping techniques and popular numerical methods for geo-mechanical analysis are reviewed. Then, an integrated stability mapping system incorporating geology hazard mapping, geologic structural feature impacts, and advanced numerical stress analysis techniques into one solution has been developed.;The stability system is implemented inside the de-facto standard drawing environment, AutoCAD, and in compatible with widely used geology modeling software SurvCADD. This feature allows one to access numerous existing geologic data and mining information from present mine maps easily and directly. The LaModel stress calculation, a boundary element method, integrated within the mapping system can produce realistic and accurate stress and displacement analysis with its distinguished features such as the laminated overburden model, the true topography consideration and actual irregular pillar matching.;After the stability mapping system was developed, two case studies were performed to check for coding errors, calculation accuracy, and for demonstrating the functionalities and usefulness of the system. In the case studies, the composite stability index was compared with field observations. A good correlation has been found although only a few influence factors have been considered.;In the conclusion of this dissertation, it is suggested that the stability mapping system provides mining engineers with the ability to perform comprehensive, rapid and accurate multiple-factor stability mapping analysis. Then the resultant stability map can be a valuable guide to safer support designing and better mine planning, and ultimately increase the safety of mine design and reduce the injuries and fatalities associated with ground fall in underground mines

PORTABLE MULTI-CAMERA SYSTEM: FROM FAST TUNNEL MAPPING TO SEMI-AUTOMATIC SPACE DECOMPOSITION AND CROSS-SECTION EXTRACTION

The paper outlines the first steps of a research project focused on the digitalization of underground tunnels for the mining industry. The aim is to solve the problem of rapidly, semi-automatically, efficiently, and reliably digitizing complex and meandering tunnels. A handheld multi-camera photogrammetric tool is used for the survey phase, which allows for the rapid acquisition of the image dataset needed to produce the 3D data. Moreover, since often, automatic, and fast acquisitions are not supported by easy-to-use tools to access and use the data at an operational level, a second aim of the research is to define a method able to arrange and organise the gathered data so that it would be easily accessible. The proposed approach is to compute the 3D skeleton of the surveyed environment by employing tools developed for the analysis of vascular networks in medical imagery. From the computed skeletonization of the underground tunnels, a method is proposed to automatically extrapolate valuable information such as cross-sections, decomposed portions of the tunnel, and the referenced images from the photogrammetric survey. The long-term research goal is to create an effective workflow, both at the hardware and software level, that can reduce computation times, process large amounts of data, and reduce dependency on high levels of experience

3D modelling of geological and anthropogenic deposits at the World Heritage Site of Bryggen in Bergen, Norway

The landscape of many historic cities and the character of their shallow subsurface environments are defined by a legacy of interaction between anthropogenic and geological processes. Anthropogenic deposits and excavations result from processes ranging from archaeological activities to modern urban development. Hence, in heritage cities, any geological investigation should acknowledge the role of past and ongoing human activities, while any archaeological investigation should be conducted with geological processes in mind. In this paper it is shown that 3D geological and anthropogenic models at different scales can provide a holistic system for the management of the subsurface. It provides a framework for the integration of other spatial and processmodels to help assess the preservationpotential for buried heritage. Such an integrated framework model is thus contributing to a decision support system for sustainable urban (re)development and regeneration in cities, while preserving cultural heritage. A collaborative approach is proposed to enhance research and implementation of combined geological and archaeological modelling for sustainable land use planning and heritage preservation, using York and Bryggen as prime examples. This paper presents the status of 3D framework modelling at Bryggen in Norway as an example

SURVEYING AND MAPPING OF OPEN PIT MINES

Surveys of open pit mines combine characteristics of engineering and topographic surveys. The surveyor provides guidance for miners to develop mining operations according to the earlier-established mine plan, then surveys the progress of mining and develops maps and models representing its current state. The maps and models are used for calculating the volumes and tonnages mined and for reconciling mining progress with the mine plan. Surveying of open pits usually involves the following activities:a. establishment of a mine survey control network;b. detailed topographic surveying of open pit and waste dumps;c. data processing to calculate mined volumes and tonnages;d. stability control surveys of open-pit and waste dump slopes;e. support surveys for earthmoving-machine control systems

Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges