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
There are various risk factors in underground mine sites, such as rockfall, tunnel collapse, collision between workers and equipment, toxic gases, and many human accidents. According to statistics from the Centers for Disease Control and Prevention (CDC), between 2010 and 2015, approximately 12,230 safety accidents have occurred at the U.S. underground mine sites, of which 121 include deaths [
Recently, various studies on information communication technology (ICT)-based underground mine safety management systems are being conducted. There have been studies to prevent collisions between workers and equipment using Bluetooth beacons [
Recently, studies have been conducted using autonomous driving robots to explore workplaces, transport roads, and accident sites in underground mines. Autonomous driving robots are used to perform exploration tasks while recognizing their own location in underground mine tunnels [
Various studies have also been conducted to measure environmental factors in underground mines using autonomous robots and environmental sensors. Baker et al. [
However, previous environmental monitoring studies using autonomous robots in underground mines have limitations in that autonomous driving functions can be used only in some areas, and the robots need to be controlled remotely in most areas. In addition, environmental factors of underground mines cannot be identified because no analysis or visualization of the acquired environmental data was performed. In particular, it is difficult to predict the location of the environmental data because the environmental data and location information of robots were not used together [
In this study, we developed an unmanned environmental monitoring system using an autonomous robot and environmental sensors for underground mines and created an environmental map for underground mines using the location information of the autonomous robot, the environmental monitoring data, and the geographic information system (GIS). Location information of the autonomous driving robot was obtained using IMU, LiDAR, and encoder sensors, and the temperature, humidity, and concentration of gas in the atmosphere were measured using environmental sensors. This paper details the development of an environmental monitoring system for an autonomous driving robot and the results of a field experiment conducted using the developed system.
Type | Name | Description | Interface | Further specifications | |
---|---|---|---|---|---|
RobotPlatform | ERP-42 | Size: 650 mm |
RS232CWi-Fi | Steer: Ackerman Geometry typeDrive: All wheel differential gear | |
IMUSensor | EBIMU–9DOFV4 | 3 Axis Gyroscope + Accelerometer + MagnetometerError Roll/Pitch: |
UART | Input voltage: 3.3 V |
|
EncoderSensor | IG-32PGM 01TYPE | Encoder gear ratio: 61Motor gear ratio: 13 | RS232C | Input voltage: 12 V | |
LiDAR Sensor | LMS-111 | Operating Range:0.5 |
TCP/IP | Scanning frequency: 25 Hz/50 Hz |
In addition, the autonomous driving robot’s main controller comprised an Intel core i7-9750H CPU 4.50 GHz, 16 GB RAM, a notebook PC with Windows 10 specifications, and a remote-control device with an Intel CPU N2600 1.60 GHz, 2 GB RAM, and a Windows 7 notebook PC. ATMega128 was used as the lower controller, and the video of the webcam installed on the front of the robot was transmitted to and recorded on a notebook PC.
Specifications | Temperature/humidity sensor | Gas sensor | Particle sensor | |
---|---|---|---|---|
Model | SEN-11301P | Gas alert max XT II | Digital dust monitor model 3443 | |
Measuring element | Temperature, Humidity | H2S, CO, O2, Combustible gas (LEL) | Particle concentration | |
Manufacturer | Seeed studio | BW technologies | KANOMAX | |
Dimensions (mm) | 40 |
13.1 |
162 |
|
Weight (g) | 10 g | 1300 kg | 328 g | |
Operating voltage | 3.3/5 V | 6 V | 100–240 V | |
Operating temperature | −20 |
5 |
−20 |
|
Operatinghumidity | 5% |
10% |
< 95% | |
Detection range | Temperature: −40 |
H2S: 0–200 ppmCO: 0.001 |
0 |
|
Accuracy | Temperature: |
N/A |
The environmental sensors used in this study are connected to the main controller, a notebook PC via USB communication, and store the robot’s location information and environmental data in 1 s using LabVIEW software.
Position | X (Distance difference) | Y (Steering value) |
---|---|---|
BR | ||
SR | ||
N | ||
SL | ||
BL |
IMU, encoder, and LiDAR sensors were used to estimate the location of the autonomous robot in the underground mines.
To perform autonomous driving, data acquisition from sensors, and location estimation, LabVIEW2018 software (National Instruments) was used, which is a graphical programming language that enables intuitive programming; therefore, it is effectively used in robot control and signal instruments.
In this study, ArcGIS, a geographic information system (GIS) software, was used to create an environmental map.
In this study, the buffer function was used instead of the spatial interpolation technique because the autonomous driving robot acquired data at close intervals. Additionally, environmental factors were expressed in different colors according to the range to visualize changes and distributions in environmental factors. A 2D map of the underground mine shaft surveyed with a 2D LiDAR sensor was visualized simultaneously.
In this study, field experiments were conducted on an abandoned amethyst mine located in Korea (35
When the autonomous driving robot starts by receiving a start signal from a remote controller, it performs autonomous driving and location estimation using IMU, LiDAR, and encoder sensors. It also measures the temperature, humidity, and concentration of hydrogen sulfide, carbon monoxide, oxygen, combustible gases, and particles using environmental sensors. The exterior driving image of the robot and the screen of the notebook PC were recorded. The estimated location, pose data, and environmental factor data were saved in units of 1 s. After the experiment was completed, the stored location and environmental data were sorted over time to match the environmental factor values according to the robot’s location. In addition, we measured the actual coordinates and driving paths of real robots by recording and analyzing the appearance of the robot’s driving path and evaluated the accuracy of the location estimation method by comparing them with estimated location coordinates. The actual location and estimated location are calculated using the root mean square error (RMSE) method, as shown in
No. | Section | Estimated X coordinate (m) | Estimated Y coordinate (m) | Actual X coordinate (m) | Actual Y coordinate (m) | Temperature ( |
Humidity (%) | Particles concentration (mg/m3) | O2 concentration (%) |
---|---|---|---|---|---|---|---|---|---|
1 | A | 0.01 | 0.00 | 0.01 | 0.00 | 16 | 43 | 0.008 | 15.7 |
2 | 0.26 | 0.02 | 0.15 | 0.00 | 15 | 42 | 0.016 | 15.7 | |
3 | 0.73 | 0.09 | 0.50 | 0.07 | 16 | 43 | 0.012 | 15.6 | |
4 | 1.29 | 0.15 | 1.10 | 0.14 | 16 | 43 | 0.01 | 15.7 | |
5 | 1.82 | 0.13 | 1.54 | 0.14 | 16 | 43 | 0.008 | 15.7 | |
6 | 2.35 | 0.09 | 1.98 | 0.07 | 16 | 43 | 0.018 | 15.7 | |
7 | 2.82 | 0.03 | 2.50 | 0.05 | 16 | 43 | 0.016 | 15.7 | |
8 | 3.30 | −0.10 | 3.02 | −0.07 | 16 | 43 | 0.012 | 15.7 | |
9 | 3.77 | −0.26 | 3.60 | −0.18 | 16 | 43 | 0.014 | 15.7 | |
10 | 4.29 | −0.43 | 4.02 | −0.33 | 16 | 43 | 0.014 | 15.7 | |
11 | B | 4.77 | −0.59 | 4.55 | −0.47 | 16 | 43 | 0.012 | 15.7 |
12 | 5.29 | −0.79 | 5.09 | −0.70 | 16 | 43 | 0.01 | 15.7 | |
13 | 5.79 | −1.02 | 5.58 | −0.93 | 16 | 43 | 0.132 | 15.7 | |
14 | 6.27 | −1.26 | 6.00 | −1.23 | 16 | 43 | 0.012 | 15.7 | |
15 | 6.74 | −1.55 | 6.45 | −1.50 | 17 | 42 | 0.012 | 15.7 | |
16 | 7.12 | −1.86 | 7.00 | −1.72 | 16 | 43 | 0.012 | 15.7 | |
17 | 7.41 | −2.17 | 7.39 | −2.10 | 17 | 42 | 0.01 | 15.7 | |
18 | 7.74 | −2.50 | 7.60 | −2.40 | 16 | 43 | 0.01 | 15.7 | |
19 | 8.11 | −2.83 | 8.10 | −2.72 | 16 | 43 | 0.008 | 15.7 | |
20 | 8.49 | −3.11 | 8.45 | −3.04 | 17 | 42 | 0.01 | 15.7 | |
21 | C | 8.93 | −3.34 | 8.90 | −3.25 | 16 | 43 | 0.014 | 15.7 |
22 | 9.40 | −3.53 | 9.43 | −3.55 | 16 | 43 | 0.01 | 15.7 | |
23 | 9.95 | −3.71 | 9.88 | −3.75 | 17 | 42 | 0.014 | 15.7 | |
24 | 10.38 | −3.82 | 10.33 | −3.81 | 16 | 43 | 0.014 | 15.7 | |
25 | 10.90 | −3.94 | 10.82 | −3.92 | 16 | 43 | 0.01 | 15.7 | |
26 | 11.49 | −4.03 | 11.45 | −4.01 | 17 | 42 | 0.01 | 15.7 | |
27 | 12.33 | −3.96 | 12.09 | −3.95 | 16 | 43 | 0.01 | 15.7 | |
28 | 12.83 | −3.88 | 12.54 | −3.84 | 16 | 43 | 0.012 | 15.7 | |
29 | 13.34 | −3.76 | 13.05 | −3.75 | 17 | 42 | 0.01 | 15.7 | |
30 | 13.85 | −3.64 | 13.55 | −3.60 | 17 | 42 | 0.014 | 15.7 | |
31 | D | 14.37 | −3.54 | 14.08 | −3.55 | 17 | 42 | 0.012 | 15.7 |
32 | 14.87 | −3.44 | 14.50 | −3.43 | 17 | 42 | 0.034 | 15.7 | |
33 | 15.38 | −3.38 | 15.09 | −3.38 | 17 | 42 | 0.012 | 15.7 | |
34 | 15.89 | −3.36 | 15.62 | −3.33 | 16 | 43 | 0.042 | 15.7 | |
35 | 16.40 | −3.32 | 16.25 | −3.23 | 17 | 42 | 0.014 | 15.7 | |
36 | 16.89 | −3.29 | 16.60 | −3.17 | 17 | 42 | 0.114 | 15.7 | |
37 | 17.40 | −3.29 | 17.22 | −3.15 | 17 | 42 | 0.01 | 15.7 | |
38 | 17.89 | −3.33 | 17.69 | −3.13 | 17 | 42 | 0.206 | 15.7 | |
39 | 18.41 | −3.39 | 18.22 | −3.20 | 17 | 42 | 0.38 | 15.7 | |
40 | 18.87 | −3.46 | 18.60 | −3.29 | 17 | 42 | 0.134 | 15.6 | |
41 | E | 19.37 | −3.59 | 19.19 | −3.45 | 17 | 42 | 0.104 | 15.6 |
42 | 19.87 | −3.74 | 19.71 | −3.66 | 17 | 42 | 0.258 | 15.6 | |
43 | 20.49 | −3.92 | 20.18 | −3.81 | 17 | 42 | 0.178 | 15.6 | |
44 | 20.90 | −4.09 | 20.71 | −4.05 | 17 | 42 | 0.272 | 15.6 | |
45 | 21.35 | −4.34 | 21.23 | −4.36 | 17 | 42 | 0.052 | 15.6 | |
46 | 21.79 | −4.58 | 21.56 | −4.57 | 17 | 42 | 0.156 | 15.6 | |
47 | 22.24 | −4.84 | 22.01 | −4.82 | 17 | 42 | 0.09 | 15.6 | |
48 | 22.66 | −5.14 | 22.38 | −5.08 | 17 | 42 | 0.336 | 15.6 | |
49 | 23.03 | −5.46 | 22.78 | −5.43 | 17 | 42 | 0.11 | 15.6 | |
50 | 23.41 | −5.79 | 23.15 | −5.89 | 17 | 42 | 0.056 | 15.6 | |
51 | F | 23.85 | −6.14 | 23.56 | −6.23 | 17 | 42 | 0.01 | 15.6 |
52 | 24.27 | −6.47 | 23.99 | −6.55 | 17 | 42 | 0.028 | 15.6 | |
53 | 24.67 | −6.80 | 24.43 | −6.90 | 15 | 42 | 0.02 | 15.6 | |
54 | 25.09 | −7.13 | 24.79 | −7.06 | 17 | 42 | 0.02 | 15.6 | |
55 | 25.53 | −7.42 | 25.29 | −7.32 | 17 | 42 | 0.01 | 15.6 | |
56 | 26.01 | −7.66 | 25.77 | −7.43 | 15 | 42 | 0.008 | 15.6 | |
57 | 26.52 | −7.90 | 26.19 | −7.64 | 17 | 42 | 0.01 | 15.6 | |
58 | 27.06 | −8.10 | 26.69 | −7.84 | 17 | 42 | 0.09 | 15.6 | |
59 | 27.58 | −8.20 | 27.25 | −7.94 | 17 | 42 | 0.016 | 15.6 | |
60 | 28.07 | −8.27 | 27.90 | −8.03 | 17 | 42 | 0.014 | 15.6 | |
61 | 28.58 | −8.31 | 28.45 | −8.11 | 17 | 42 | 0.038 | 15.6 |
The underground mine where the field experiments were conducted was measured at temperatures of approximately 15–16
The particle concentration of 0.293 mg/m3 was measured at approximately 38 to 51 s after the robot’s departure, and the relative concentration was 190 when the lowest particle concentration generated in the experimental area was converted to 1. It was expected that smoke or particles from the movement of people or equipment would have occurred at that time. The O2 concentration graph (
In this study, the location of the autonomous driving robot was measured in real time using LiDAR, IMU, and encoder sensors, and the driving path was estimated by storing these data over time. In addition, the actual driving path of the robot was assessed by recording the appearance of the robot driving from the outside and analyzing it.
Value | Section | ||||||
---|---|---|---|---|---|---|---|
A | B | C | D | E | F | ||
Location estimation RMSE | 0.18 | 0.14 | 0.08 | 0.18 | 0.17 | 0.23 | |
Temperature ( |
Average | 15.90 | 16.30 | 16.40 | 16.90 | 17.00 | 16.64 |
Standard deviation | 0.32 | 0.48 | 0.49 | 0.30 | 0 | 0.81 | |
Humidity (%) | Average | 42.90 | 42.70 | 42.60 | 42.10 | 42.00 | 42.00 |
Standard deviation | 0.32 | 0.48 | 0.49 | 0.30 | 0 | 0 | |
Particle concentration (mg/m3) | Average | 0.01 | 0.02 | 0.01 | 0.10 | 0.16 | 0.02 |
Standard deviation | 0 | 0.04 | 0 | 0.11 | 0.09 | 0.02 | |
O2 concentration (%) | Average | 15.69 | 15.70 | 15.70 | 15.69 | 15.60 | 15.60 |
Standard deviation | 0.03 | 0 | 0 | 0.03 | 0 | 0 |
It was confirmed that the particle concentration partially increased in the area of 5 m along the Y-axis of the experimental area, and the particle concentration gradually increased from approximately 18 m to the maximum in the area of 19 m, and remained at a high concentration until the area of 24 m.
In this study, a small autonomous driving robot that can perform unmanned environmental monitoring in underground mines was developed using location estimation sensors and environmental detection sensors. Three types of sensors (IMU, LiDAR, and encoder) were used to estimate the location of the robot, and three types (temperature/humidity, gas, and particle) of environmental sensors were used to measure environmental factors. As a result of conducting field experiments on underground mines using the developed system, the location estimation method showed errors of approximately 0.22 m along the x-axis and 0.11 m along the y-axis. Temperature, humidity, O2, and particle concentration were measured to be almost constant, and the concentration of harmful gases was not measured. In the case of particle concentration, it was measured at a maximum of 0.293 mg/m3; it was confirmed from the created environmental map that a large number of particles were generated in the 18–24 m section of the experimental section.
Because the global positioning system (GPS) cannot be used in underground mine environments, it is difficult to recognize the location, and the communication environment for remotely operating devices is also limited. However, the autonomous driving robot developed in this study could efficiently collect location information from the measurement points of environmental data by using location estimation sensors and also conduct exploration autonomously without intervention by workers. In addition, because the location information and environmental data were used together to create an environmental map, the environmental information of the underground mine could be effectively visualized.
The developed small autonomous driving robot can be used in areas where road conditions are relatively stable. However, in the case of an actual underground mine environment, as there exist areas where the road conditions are not stable, its utilization is limited. Therefore, to expand the utilization of the autonomous driving system developed in this study, it would need to be applied to large-scale equipment such as mining transport trucks and loaders [