Sensing and Hazard Detection in Autonomous Mine Rescue

The deployment of autonomous systems in underground mining operations is critical for enhancing personnel safety and the effectiveness of rescue missions in high-risk environments. This paper explores the sensing and hazard detection capabilities of the Husky Unmanned Ground Vehicle (UGV) and evaluates several detection methodologies, including deployable "egg-inspired" sensor nodes for environmental monitoring of gas and temperature and a theoretical "Temperature Net" for directional heat prediction. The primary focus is the development and testing of a "Distance and Temperature Point Grid" system, which utilizes a robotic arm equipped with a narrow field-of-view thermal sensor and a single-point LIDAR to sweep and localize thermal threats. Experimental results indicate that the arm-sweep method offers the most practical solution for active hazard avoidance, successfully identifying the distance and scope of heat sources. These advancements aim to improve the Husky UGV's situational awareness and autonomy in disaster-recovery applications.