Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application

The mining industry is experiencing a transformative shift with the integration of automation, particularly through autonomous haul truck systems, and further advancements are anticipated with the application of swarm robotics. This study evaluates the performance of four swarm robot models, namely baseline, ant, firefly, and honeybee, in optimizing key mining operations such as ore detection, extraction, and transportation. Simulations replicating real-world mining environments were conducted to assess improvements in operational efficiency, scalability, reliability, selectivity, and energy consumption. The results demonstrate that these models can significantly enhance the precision and productivity of mining activities, especially in complex and dynamic settings. A case study of the Pilbara iron ore mine in Australia is presented to illustrate the practical applicability of these models in an actual mining context. The study also highlights specific enhancements in each model, including role specialization in the ant model, advanced communication in the firefly model, and improved localization combined with hybrid control in the honeybee model. While the honeybee model showed superior performance in high-precision tasks, its reliability was limited under high-error conditions, and it faced a computational resources bottleneck in large-scale operations, highlighting the need for further development. By evaluating these models against performance criteria, the study identifies the most suitable swarm models for various mining conditions, offering insights into achieving more sustainable, scalable, and efficient mining operations.