Article
Data Management System for a Semiautonomous Shuttle Car
for Underground Room and Pillar Coal Mines
Vasilis Androulakis * , Steven Schafrik , Joseph Sottile and Zach Agioutantis
Citation: Androulakis, V.;
Schafrik, S.; Sottile, J.; Agioutantis, Z.
Data Management System for a
Semiautonomous Shuttle Car for
Underground Room and Pillar Coal
Mines. Automation 2021, 2, 153–172.
https://doi.org/10.3390/
automation2030010
Academic Editor: João Miguel da
Costa Sousa
Received: 25 May 2021
Accepted: 11 August 2021
Published: 13 August 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Mining and Minerals Resources Bldg, University of Kentucky, Lexington, KY 40506, USA;
steven.schafrik@uky.edu (S.S.); joseph.sottile@uky.edu (J.S.); zach.agioutantis@uky.edu (Z.A.)
* Correspondence: vandroulakis@uky.edu
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.
Keywords:
mining industry; autonomous navigation; shuttle car; room and pillar mining;
data management
1. Introduction
In recent years, autonomous solutions in the multidisciplinary field of mining engi-
neering have been an extremely popular applied research topic. The increasing demands
of society on mineral resources, along with the accelerating exploitation of the currently
economically viable resources, have led the mining sector to turn to deeper, more-difficult-
to-mine orebodies. To achieve this, the mining industry needs to continue to modernize and
advance mining technology. One of the trends is the integration of autonomous vehicles
and solutions into the mining cycle [1].
The increasing appeal of integrating autonomous vehicles into the mining cycle lies
primarily on two aspects that need to be optimized in every mine: safety and productivity.
A significant improvement of the health and safety of the miners can be achieved by
relocating equipment operators and other miners to a safer and healthier environment.
Equipment operators are inherently exposed to numerous occupational hazards: noise;
dust; vibration; thermal stress; inclement weather; slips, trips, and falls from climbing
Automation 2021, 2, 153–172. https://doi.org/10.3390/automation2030010 https://www.mdpi.com/journal/automation
