@Article{cmes.2021.015300,
AUTHOR = {Heonmoo Kim, Yosoon Choi},
TITLE = {Self-Driving Algorithm and Location Estimation Method for Small Environmental Monitoring Robot in Underground Mines},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {127},
YEAR = {2021},
NUMBER = {3},
PAGES = {943--964},
URL = {http://www.techscience.com/CMES/v127n3/42607},
ISSN = {1526-1506},
ABSTRACT = {In underground mine environments where various hazards exist, such as tunnel collapse, toxic gases, the application of autonomous robots can improve the stability of exploration and efficiently perform repetitive exploratory
operations. In this study, we developed a small autonomous driving robot for unmanned environmental monitoring
in underground mines. The developed autonomous driving robot controls the steering according to the distance
to the tunnel wall measured using the light detection and ranging sensor mounted on the robot to estimate its
location by simultaneously considering the measured values of the inertial measurement unit and encoder sensors.
In addition, the robot autonomously drives through the underground mine and performs environmental monitoring using the temperature/humidity, gas, and particle sensors mounted on the robot. As a result of testing the
performance of the developed robot at an amethyst mine in Korea, the robot was found to be able to autonomously
drive through tunnel sections with ∼28 m length, ∼2.5 m height, and∼3 m width successfully. The average error of
location estimation was approximately 0.16 m. Using environmental monitoring sensors, temperature of 15–17◦C,
humidity of 42%–43%, oxygen concentration of 15.6%–15.7%, and particle concentration of 0.008–0.38 mg/m<sup>3</sup>
were measured in the experimental area, and no harmful gases were detected. In addition, an environmental
monitoring map could be created using the measured values of the robot’s location coordinates and environmental
factors recorded during autonomous driving.},
DOI = {10.32604/cmes.2021.015300}
}