Robotics in Underground Mining for Improved Worker Safety and Efficiency

Abstract

Underground mining operations benefit substantially from AI and Machine Learning integration because this technology improves both safety performance and operational efficiency standards. The implementation of autonomous decision-making algorithms enables mines to enhance their operational efficiency by controlling drilling and loading and hauling processes which reduces human mistakes. Real-time data from IoT sensors which embed mining equipment together with environmental monitors and robotic systems is evaluated through AI algorithms to let operators predict challenges before operational downtime and safety incidents occur. Policy decisions made through machine learning predictions help maintain equipment better and prevent unanticipated equipment failure. Machine learning analytics works to maximize resource utilization by checking ore rock types alongside excavation pace and equipment operational strength which enhances mining operations and cuts down material waste outputs. The integration of this method produces both raised productivity alongside substantial hazard environment reduction for workers which results in increased mining operation sustainability. Ultimately, AI and machine learning drive smarter, data-driven decision-making for mining operations.