The mining industry has entered a decisive period of technological transformation driven by the convergence of automation, robotics, sensing, and artificial intelligence. Across both surface and underground operations, autonomous and semi-autonomous systems are increasingly deployed to address long-standing challenges related to worker safety, operational efficiency, productivity variability, and environmental constraints. Despite significant technical progress, the implementation of automation in mining remains uneven, constrained not only by engineering limitations but also by systemic safety risks, human–machine interaction challenges, and the complexity of integrating autonomous systems into legacy mining environments. This research article presents a comprehensive, theory-driven examination of autonomous and robotic systems in mining, synthesizing contemporary developments in unmanned aerial vehicles, unmanned ground vehicles, longwall automation, computer-vision-based collision avoidance, and sensing technologies within a unified systems engineering and human-systems integration framework.

Methodologically, the study adopts a structured qualitative synthesis approach grounded in systems engineering principles, functional safety doctrine, and human-systems integration theory. Findings reveal that productivity gains and safety improvements are most pronounced when automation is embedded within a coherent safety lifecycle that incorporates functional safety standards, human-centered design, and continuous risk assessment. Conversely, failures in mining automation frequently stem from inadequate integration of sensing, control, and human oversight rather than from deficiencies in autonomy algorithms alone.

The discussion critically interrogates prevailing assumptions surrounding full autonomy, highlighting limitations related to trust calibration, situational awareness degradation, and regulatory ambiguity. The article concludes by proposing future research and implementation pathways that emphasize adaptive autonomy, resilient sensing architectures, and the formal integration of human-systems integration into mining automation governance. By reframing automation as a socio-technical safety system rather than a discrete technological artifact, this research contributes a foundational perspective for safer, more sustainable mining operations in the coming decades.

Mining automation, autonomous systems, underground mining safety

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