# Development of Autonomous Driving Patrol Robot for Improving Underground Mine Safety
## 2 Materials and Methods
### System Configuration of the Autonomous Driving Robot
A LiDAR sensor is mounted on the front of the robot to measure the shape of the tunnel. Further, thermal and RGB-D cameras are placed in the robot's driving direction to record the presence of workers and the driving environment. A built-in IMU sensor and an encoder sensor are used to measure the pose and position of the robot. Gas and dust sensors are mounted on the front and side parts of the robot, respectively, to examine the working environment in the tunnel and detect the presence or absence of harmful gases. Figure 3 shows the overall data-processing configuration of the autonomous driving robot developed in this study. Data processing includes localization, safety data acquisition, and autonomous driving, and each function shares processed data and operates organically. First, for localization, the data input from the gyroscope, accelerometer, and magnetometer are combined with the Kalman filter to obtain relatively accurate robot pose data. When estimating the robot's position by fusing multiple sensors, the accuracy of pose estimation can be improved by using the Kalman filter to probabilistically calculate and correct errors that may occur in the sensor [36]. The general Kalman filter cannot be applied because the robot's movement and location data are non-linear. Therefore, in this study, the extended Kalman filter (EKF) was used to correct the movement of the robot. In addition, the position of the robot is measured by reflecting the wheel rotation data measured by the encoder sensor on the corrected pose data. At this time, the moving distance and heading of the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Equipment** & **Model** & **Specification** \\ \hline \multirow{7}{*}{Robot Platform} & & Size: 612 mm (length) \(\times\) 580 mm (width) \(\times\) 245 mm (height) \\  & & Drive: four-wheel four drive \\  & & Max travel: 10 km \\  & & Max Speed: 20 km/h \\  & & Climbing ability: 30\({}^{\circ}\) \\  & & Payload capacity: 50 kg \\  & & Code wheel: hall encoder \\ Main & Laptop PC & Intel Core i7-9750H CPU 4.50 GHz (Intel, Santa Clara, CA, UAS), 16 GB \\ Controller & Ubuntu 18.04 & RAM, NVIDIA GeForce 1650 4 GB (NVIDIA, Santa Clara, CA, USA) \\ Data acquisition and & Laptop PC & Intel Core i5-10210U, 1.6 GHz, 8 GB RAM, NVIDIA MX250 (NVIDIA, Santa Clara, CA, USA) \\ remote controller & Windows 10 & Santa Clara, CA, USA) \\  & & Field of View: 270\({}^{\circ}\) \\ LiDAR Sensor & LMS-111 & Interface: TCP/IP \\  & & Operating Range: 0.5–20 m \\  & & Scanning frequency: 25 Hz/50 Hz \\  & & Interface: uart2 \\  & & Gyroscope range: \(\pm\)2000 dps \\ IMU Sensor & RB-SDA-v1 & Accelerometer range: \(\pm\)16 g \\  & & Magnetometer range: \(\pm\)4900 μT \\  & & RGB sensor FOV (H \(\times\) V): 69\({}^{\circ}\)\(\times\) 42\({}^{\circ}\) \\ RGB-D Camera & D435i & RGB frame resolution:1920 \(\times\) 1080 \\  & & Depth Field of View (FOV): 87\({}^{\circ}\)\(\times\) 58\({}^{\circ}\) \\  & & Depth output resolution: 1280 \(\times\) 720 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of sensors and robot platform used in this study. robot are measured using the encoder sensor. The robot's location and pose data are combined to improve accuracy. For safety data acquisition, the data acquired by the gas and particle sensors are stored. Further, the data regarding the presence of workers that is detected via thermal imaging are stored. In addition, after aligning the acquired data according to time, they are combined with the position data of the robot to visualize the point at which toxic gases, dust, and workers are detected. Figure 3: System architecture of the data processing procedure for the autonomous driving robot used in this study. Figure 2: Autonomous driving robot and sensors used in this study. For autonomous driving, a destination point is first set on a previously prepared 2D map. Subsequently, a route is planned, and the robot's position and pose data calculated in real time are reflected to realize autonomous driving.