Abstract

In recent years, autonomous solutions in the multidisciplinary field of mining engineering have been an extremely popular applied research topic. This is a result of the increasing demands of society on mineral resources along with the accelerating exploitation of the currently economically viable resources, which lead the mining sector to turn to deeper, more-difficult-to-mine orebodies. An appropriate data management system comprises a crucial aspect of the designing and the engineering of a system that involves autonomous or semiautonomous vehicles. The vast volume of data collected from onboard sensors, as well as from a potential IoT network dispersed around a smart mine, necessitates the development of a reliable data management strategy. Ideally, this strategy will allow for fast and asynchronous access to the data for real-time processing and decision-making purposes as well as for visualization through a corresponding human–machine interface. The proposed system has been developed for autonomous navigation of a coalmine shuttle car and has been implemented on a 1/6th scale shuttle car in a mock mine. It comprises three separate nodes, namely, a data collection node, a data management node, and a data processing and visualization node. This approach was dictated by the large amount of collected data and the need to ensure uninterrupted and fast data management and flow. The implementation of an SQL database server allows for asynchronous, real-time, and reliable data management, including data storage and retrieval. On the other hand, this approach introduces latencies between the data management node and the other two nodes. In general, these latencies include sensor latencies, network latencies, and processing latencies. However, the data processing and visualization module is able to retrieve and process the latest data and make a decision about the next optimal movement of the shuttle car prototype in less than 900 ms. This allows the prototype to navigate efficiently around the pillars without interruptions.

Highlights

  • In recent years, autonomous solutions in the multidisciplinary field of mining engineering have been an extremely popular applied research topic

  • This paper presents the data management system implemented when integrating an autonomous shuttle car into the room and pillar underground coal mining cycle

  • Data management systems play a crucial role in the implementation of an autonomous solution

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Summary

Introduction

Autonomous solutions in the multidisciplinary field of mining engineering have been an extremely popular applied research topic. Since the main aim of this ongoing research is to determine the feasibility of autonomous navigation around pillars in underground mines and provide a simplified real-scale demonstration, an exhaustive consideration of industrial safety protocols and regulations or complex interactions between the various equipment operating in the working environment was deemed to be beyond the scope of this study. Despite these simplifications, the authors believe that useful insights can be conveyed by this study

Autonomous Vehicles for Mining Applications
Sensors
LiDAR Scanners
Ultrasonic Sensors
Data Collection
Data Management
Data Processing and Visualization
Latency Considerations
Findings
Conclusions
Full Text
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