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

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dc.contributor.authorTan, J.
dc.contributor.authorMelkoumian, N.
dc.contributor.authorHarvey, D.
dc.contributor.authorAkmeliawati, R.
dc.date.issued2024
dc.description.abstractThe 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.
dc.description.statementofresponsibilityJoven Tan, Noune Melkoumian, David Harvey, and Rini Akmeliawati
dc.identifier.citationApplied Sciences, 2024; 14(19):8876-1-8876-28
dc.identifier.doi10.3390/app14198876
dc.identifier.issn2076-3417
dc.identifier.issn2076-3417
dc.identifier.orcidTan, J. [0000-0003-0306-9751]
dc.identifier.orcidHarvey, D. [0000-0003-3703-7336]
dc.identifier.orcidAkmeliawati, R. [0000-0003-0660-2312]
dc.identifier.urihttps://hdl.handle.net/2440/146604
dc.language.isoen
dc.publisherMDPI AG
dc.rights© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.source.urihttp://dx.doi.org/10.3390/app14198876
dc.subjectmining methods; AI in mining; green operation; sustainable mining; mining optimization; mining automation; nature-inspired behavior; swarm robotics
dc.titleEvaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
dc.typeJournal article
pubs.publication-statusPublished online

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