# Development of Autonomous Driving Patrol Robot for Improving Underground Mine Safety
## 6 Conclusions  
This study developed a multi-sensor-based patrol robot for autonomous exploration and conducted a field experiment in an actual underground mine. The robot's location was estimated using sensors, such as an encoder, IMU, and LiDAR, along with a sensor fusion algorithm. In addition, the location was calibrated by comparing the map prepared in advance with real-time LiDAR data. Multiple waypoints, including the intersection and slope section, were sequentially driven and returned to the starting point. The robot was designed to be equipped with gas sensors, dust sensors, and a thermal camera to recognize hazard factors and workers in the mine. A safety map was created using the GIS system after combining the data acquired by these sensors with the robot location data. The results of the experiment showed that the autonomous driving robot safely traveled to the experimental area and returned to its origin. No toxic gas was detected in the tunnel, and the O\({}_{2}\) concentration was almost constant. The maximum dust concentration measured was about 0.01 mg/m\({}^{3}\), and it was confirmed that the highest dust concentration was measured in the section between 25 m and 35 m.  
As the autonomous driving system sets waypoints on a 2D map while driving, position-estimation errors may occur when driving on an inclined surface. In addition, because only 2D position coordinates are estimated, the acquisition point is not accurate as the robot goes deeper. Therefore, in future research, a more efficient autonomous driving system should be developed by estimating the 3D position of the robot in addition to the 2D coordinates. To this end, a 3D point cloud acquisition sensor, such as 3D LiDAR, should be additionally used, and the location estimation accuracy should be improved by combining it with existing sensors. In this study, the experiment was performed only for short and simple sections among entire underground mines, and there is a limitation that the experiment was conducted only for a single mine. However, it is expected that the same method can be used for other mines, including slopes and intersections. Therefore, additional experiments related to this need to be performed in the future. In addition, the accuracy evaluation of the localization method should be performed by comparing the estimated driving path with the actual driving path. In this study, a gas sensor was installed in the robot, but in order to measure gases lighter than air, a gas sensor should be installed at the top of the tunnel wall, and data should be acquired through wireless communication when the robot reaches the sensor. In this study, there were no dynamically moving obstacles when the robot drove in the underground mine, but in actual underground mining environments, there are many dynamic obstacles, such as workers and trucks. Therefore, to improve robot utilization, experiments on obstacle avoidance and driving in the presence of dynamic obstacles should be conducted in the future. Furthermore, camera sensors and vision technologies should be applied to efficiently detect areas or dynamic obstacles that are not reflected in the pre-prepared map. Additionally, in actual underground mines, the floor surface is often uneven, causing reduced location estimation accuracy and decreased autonomous driving performance due to wheel slippage. Therefore, in the future, more precise location correction technology should be developed to address wheel slippage. Various risk factors, such as toxic gas, falling rocks, and ground collapse, exist at underground mine sites, and there are many areas that are difficult for people to access. Therefore, at a mine work site, periodic exploration must be performed to ensure the safety of workers, and safe rescue work must be carried out when an accident occurs.