Abstract
Mining, both surface and underground, is a potentially hazardous activity as it is exposed to equipment collision, rock fall and gas leaks besides poor human visibility in the complex environment. The approach to confronting these risks needs fresh ideas that involve reducing the human exposure to risk and maintaining an ongoing productivity. This study looks at the process and interconnectivity of the autonomous safety system in mining, with intent to increase the level of employee safety and minimizing case of accidents. The suggested idea uses the latest sensing mechanisms, artificial intelligence (AI), robotics, and Internet of Things (IoT) connectivity to develop real-time monitoring, hazard alerting mechanisms, and automated response strategies. The results lead to the conclusion that such programs not only increase situational awareness, mitigate operational risks but also streamline performance, producing predictive maintenance and adaptive decision-making. The importance of this study will be its potential to change safety management in the area of real-life mining operations and make it sustainable and resilient in nature all over the world.
Citations
IRE Journals:
Alan Ato Arthur
"Autonomous Safety Systems for Enhancing Surface and Underground Mining Operations" Iconic Research And Engineering Journals Volume 9 Issue 2 2025 Page 1279-1284
IEEE:
Alan Ato Arthur
"Autonomous Safety Systems for Enhancing Surface and Underground Mining Operations" Iconic Research And Engineering Journals, 9(2)
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