Open Access

ARTICLE

Self-Driving Algorithm and Location Estimation Method for Small Environmental Monitoring Robot in Underground Mines

by Heonmoo Kim, Yosoon Choi^*

Department of Energy Resources Engineering, Pukyong National University, Busan, 48513, Korea

* Corresponding Author: Yosoon Choi. Email:

Computer Modeling in Engineering & Sciences 2021, 127(3), 943-964. https://doi.org/10.32604/cmes.2021.015300

Received 07 December 2020; Accepted 10 March 2021; Issue published 24 May 2021

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/m³ 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.

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