This study examines the potential of autonomous material handling systems to improve safety and efficiency within underground platinum mining operations, with a specific focus on Mimosa Mine in Zimbabwe. The context is critical as, despite mechanized mining methods such as room-and-pillar systems with load-haul-dump machines (LHDs) and investments in a US75 million tailings storage facility, manual material handling tasks like scraper winch operations persist as significant safety bottlenecks. These operations contribute to incidents involving entanglement, equipment collisions, and rock-falls, leading to fatalities. Existing safety management systems prove insufficient. Globally, integrating autonomous technologies such as robotic loaders and sensor-based geofencing has led to a substantial reduction in mining accidents. For example, each report a 40–50% decrease in accident rates following the adoption of these technologies. This review explores their applicability to Mimosa Mine's operations. By analyzing safety challenges, global trends in autonomous mining, relevant case studies, and the specific constraints of the Zimbabwean mining sector, a comparative analysis of research contributions is provided. Finally, key gaps in research are identified, and potential directions for future studies are highlighted, including the integration of AI, data analytics, and scalability in autonomous mining systems.
Tafadzwa et al. (Wed,) studied this question.