Version 1
: Received: 6 August 2024 / Approved: 6 August 2024 / Online: 6 August 2024 (10:00:00 CEST)
How to cite:
Tan, J.; Melkoumian, N.; Harvey, D.; Akmeliawati, R. Evaluating Swarm Robotics for Diverse Mining Environments: Insights into Model Performance and Application. Preprints2024, 2024080410. https://doi.org/10.20944/preprints202408.0410.v1
Tan, J.; Melkoumian, N.; Harvey, D.; Akmeliawati, R. Evaluating Swarm Robotics for Diverse Mining Environments: Insights into Model Performance and Application. Preprints 2024, 2024080410. https://doi.org/10.20944/preprints202408.0410.v1
Tan, J.; Melkoumian, N.; Harvey, D.; Akmeliawati, R. Evaluating Swarm Robotics for Diverse Mining Environments: Insights into Model Performance and Application. Preprints2024, 2024080410. https://doi.org/10.20944/preprints202408.0410.v1
APA Style
Tan, J., Melkoumian, N., Harvey, D., & Akmeliawati, R. (2024). Evaluating Swarm Robotics for Diverse Mining Environments: Insights into Model Performance and Application. Preprints. https://doi.org/10.20944/preprints202408.0410.v1
Chicago/Turabian Style
Tan, J., David Harvey and Rini Akmeliawati. 2024 "Evaluating Swarm Robotics for Diverse Mining Environments: Insights into Model Performance and Application" Preprints. https://doi.org/10.20944/preprints202408.0410.v1
Abstract
Mining industry is undergoing a revolution with the introduction of automation, especially through autonomous haul truck systems and is expected to have further essential developments due to advancements in swarm robotics and its applications. This study evaluates the performance of four different swarm robot models, i.e. baseline, ant, firefly, and honeybee, focusing on mining efficiency, scalability, reliability, and selectivity. Enhancements such as role specialization in the ant model, advanced communication in the firefly model, improved localization and the hybrid control system combining centralized and decentralized controls in the honeybee model are integrated to optimize their functionality. Mining simulations presented in this study include swarm robots of various designs, each resulting in certain advantages for autonomous operations. Their capabilities have been evaluated against performance criteria on a spider chart, identifying where each model excelled or failed in an actual extraction operation in mining. This study aims to identify the most optimal swarm models in relation to unique mining site conditions and goals. The applications of the suggested swarm models could contribute to achieving further improvement in productivity, scalability, selectivity and reliability, resulting in a more sustainable, automated and effective operations in mining.
Keywords
Intelligent mining; smart mining; new mining technology; sustainable mining practices; Automated mining equipment; Mining technology of complex and difficult mining body
Subject
Engineering, Mining and Mineral Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
