# Development of Autonomous Driving Patrol Robot for Improving Underground Mine Safety
## 2 Materials and Methods
### Autonomous Exploration System Configuration  
Figure 6 shows the dust sensor (Figure 6a), gas sensor (Figure 6b), and a thermal camera (Figure 6c) used in this study. The particle sensor can measure the dust particle concentration in the tunnel, and the gas sensor can measure CO, combustible gases (LEL), and H\({}_{2}\)S. In addition, the detection range of the particle sensor and gas sensor used in this study can measure the recommended environmental range prescribed by the Korean government for the underground mine environment [43]. At a mine site, it is necessary to ensure the absence of workers in the mine before carrying out blasting work. At this point, the thermal camera has the advantage of being able to accurately detect human presence even in a dark underground environment. Table 3 lists the detailed specifications of the environmental detection sensors used in this study. Figure 5: User interface of the autonomous driving robot system in ROS. Figure 6: Environmental sensors used in this study: (**a**) particle sensor; (**b**) gas sensor; (**c**) thermal camera. Figure 7 shows the data processing flowchart for creating an environmental map. While the robot travels along the planned path, the safety sensors acquire data. When the robot returns, the position and safety data are aligned over time to match the robot's position with the safety data at that point. Then, the safety data are visualized according to the robot's movement path; if there is a significant change, they are created in the form of a map using the geographic information system (GIS). In this study, an environmental map for the experimental area was created using the IDW (inverse distance weighting) algorithm [44]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Sensor** & **Particle Sensor** & **Gas Sensor** & **Thermal Camera** \\ \hline Model & Digital dust monitor model 3443 & Gas Alert Max XT II & Vue Pro 640 19 mm 9 hz \\ \hline Manufacturer & KANOMAX & BW Technologies & Teledyne FLIR \\ \hline Measuring factor & Particle concentration & H\({}_{2}\)S, CO, O\({}_{2}\), Combustible Gas (LEL) & Thermal image/video \\ \hline Dimensions (mm) & \(162\times 60\times 109\) & \(13.1\times 7.0\times 5.2\) & \(45\times 45\times 68\) \\ \hline Weight (g) & 328 & 1300 & 113 \\ \hline Operating temperature & \(-20\)–\(50\)\({}^{\circ}\)C & 5–\(40\)\({}^{\circ}\)C & \(-20\)–\(55\)\({}^{\circ}\)C \\ \hline \multirow{3}{*}{Detection range} & \multirow{3}{*}{0–1000 ppm} & H\({}_{2}\)S: 0–200 ppm & \multirow{3}{*}{0–40 \({}^{\circ}\)C} \\  & & CO: 0.001–\(10\) mg/m\({}^{3}\) & \\ \cline{1-1} \cline{3-4}  & & O\({}_{2}\): 0–30\% vol & \\ \cline{1-1} \cline{3-4}  & & LEL: 0–100\% LEL & \\ \hline Accuracy & \(\pm 10\%\) & N/A & \(\pm 5\)\({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Specifications of environmental sensors used in this study. Figure 7: Processing diagram of the environmental mapping system to visualize the data. The robot acquires thermal images when it drives through the entire experimental area. It recognizes people using thermal images and machine-vision-based image processing algorithms. In the image processing process, the background is removed, leaving only the people displayed at a relatively high temperature. When two or more people are detected, they are recognized as independent objects, and the number of people is counted. Figure 8 shows the process of human recognition using the acquired thermal image and machine vision system. As shown in Figure 8a, a thermal image is acquired and then converted to a grayscale image to clearly represent it by separating it from the background (Figure 8b). After classifying the converted grayscale image as a binary image, each object is recognized independently, and the number of people is counted (Figure 8c). The morphology and particle analysis algorithms, along with LabVIEW software, were used for this process.