Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
Abstract
:1. Introduction
2. Case Study: Applications to Pilbara Iron Ore Mine
2.1. Overview of the Pilbara Iron Ore Mine
2.2. Simulation Setup Using Pilbara Geological Data
3. Swarm Robotics Design and Simulation
3.1. Simulated Hardware Design for Robots
3.1.1. Mechanical Structure
3.1.2. Sensor and Actuators
3.1.3. Power and Communications Systems
3.2. Swarm Control Architecture
3.3. Swarm Model Design
3.3.1. Baseline Model
3.3.2. Ant Model
3.3.3. Firefly Model
3.3.4. Honeybee Model
3.4. Swarm Robotics and Formation Control
3.4.1. Strategic Development Approach
3.4.2. Formation Control Mechanisms
4. Simulation Environment
4.1. Assumption and Simulation Constraints
4.2. Simulation Setup for Testing
5. Results and Discussions
5.1. Mining Efficiency and Operational Performance
5.2. Statistical Analysis
5.3. Mining Scalability and Adaptability
5.4. Mining Reliability and Resilience
5.5. Mining Selectivity
5.6. Overview of Swarm Model Performance
5.7. Performance and Resources Metrics
5.7.1. Computational Load and Communication Efficiency
5.7.2. Energy Consumption
5.7.3. CPU Utilization
5.8. Mining Design Applications
6. Future Direction and Real-World Implementation
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Wellmer, F.W.; Buchholz, P.; Gutzmer, J.; Hagelüken, C.; Herzig, P.; Littke, R.; Thauer, R.K. Raw Materials for Future Energy Supply; Springer International Publishing: Cham, Switzerland, 2019. [Google Scholar]
- Government of Western Australia. Western Australia Iron Ore Profile; Government of Western Australia: Perth, Australia, 2019.
- Salisbury, C. Iron Ore—Delivering Value from Flexibility and Optionality; Rio Tinto: London, UK, 2018. [Google Scholar]
- BHP. Automation Data is Making Work Safer, Smarter and Faster; BHP: Melbourne, Australia, 2019. [Google Scholar]
- Jang, H.; Topal, E. Transformation of the Australian mining industry and future prospects. Min. Technol. 2020, 129, 120–134. [Google Scholar] [CrossRef]
- Brambilla, M.; Ferrante, E.; Birattari, M.; Dorigo, M. Swarm robotics: A review from the swarm engineering perspective. Swarm Intell. 2013, 7, 1–41. [Google Scholar] [CrossRef]
- Trianni, V.; IJsselmuiden, J.; Haken, R. The Saga Concept: Swarm Robotics for Agricultural Applications. Technical Report. 2016. Available online: http://laral.istc.cnr.it/saga/wp-content/uploads/2018/02/ITEMS2017-ID11.pdf (accessed on 23 August 2018).
- Thamrin, N.M.; Arshad, N.H.M.; Adnan, R.; Sam, R. Forward Navigation for Autonomous Unmanned Vehicle in Inter-Row Planted Agriculture Field. In Control Engineering in Robotics and Industrial Automation: Malaysian Society for Automatic Control Engineers (MACE) Technical Series; Springer: Berlin/Heidelberg, Germany, 2022; Volume 2018, pp. 183–198. [Google Scholar]
- Mariappan, M.; Arshad, M.R.; Akmeliawati, R.; Chong, C.S. Control Engineering in Robotics and Industrial Automation; Springer International Publishing: Cham, Switzerland, 2022. [Google Scholar]
- Phys.org. Nature-Inspired Soft Millirobot Makes Its Way through Enclosed Spaces. 2018. Available online: https://phys.org/news/2018-01-nature-inspired-soft-millirobot-enclosed-spaces.html (accessed on 1 June 2021).
- Kayser, M.; Cai, L.; Bader, C.; Falcone, S.; Inglessis, N.; Darweesh, B.; Costa, J.; Oxman, N. September. Fiberbots: Design and digital fabrication of tubular structures using robot swarms. In Robotic Fabrication in Architecture, Art and Design; Springer: Cham, Switzerland, 2018; pp. 285–296. [Google Scholar]
- Petersen, K.H.; Nagpal, R.; Werfel, J.K. Termes: An autonomous robotic system for three-dimensional collective construction. In Robotics: Science and systems VII; The MIT Press: Cambridge, MA, USA, 2011. [Google Scholar]
- Sebbane, Y.B. Intelligent Autonomy of UAVs: Advanced Missions and Future Use; CRC Press: Boca Raton, FL, USA, 2018. [Google Scholar]
- Schranz, M.; Umlauft, M.; Sende, M.; Elmenreich, W. Swarm robotic behaviors and current applications. Front. Robot. AI 2020, 7, 36. [Google Scholar] [CrossRef] [PubMed]
- Tan, J.; Melkoumian, N.; Akmeliawati, R.; Harvey, D. Design and application of swarm robotics system using ABCO method for off-Earth mining. In Proceedings of the Fifth International Future Mining Conference 2021, Perth, Australia, 6 December 2021. AusIMM. [Google Scholar]
- Subhan, M.A.; Bhide, A.S.; COEB SSGB. Study of unmanned vehicle (robot) for coal mines. Int. J. Innov. Res. Adv. Eng. 2014, 1, 116–120. [Google Scholar]
- Duarte, M.; Costa, V.; Gomes, J.; Rodrigues, T.; Silva, F.; Oliveira, S.M.; Christensen, A.L. Evolution of collective behaviors for a real swarm of aquatic surface robots. PLoS ONE 2016, 11, e0151834. [Google Scholar] [CrossRef] [PubMed]
- Tan, J.; Melkoumian, N.; Harvey, D.; Akmeliawati, R. Classifying Nature-Inspired Swarm Algorithms for Sustainable Autonomous Mining. Insights Min. Sci. Technol. 2024, 4, 555636. [Google Scholar] [CrossRef]
- St-Onge, D.; Kaufmann, M.; Panerati, J.; Ramtoula, B.; Cao, Y.; Coffey, E.B.; Beltrame, G. Planetary exploration with robot teams: Implementing higher autonomy with swarm intelligence. IEEE Robot. Autom. Mag. 2019, 27, 159–168. [Google Scholar] [CrossRef]
- Nordic Rock Tech Centre. SMIFU WP1, Final Report; Nordic Rock Tech Centre: Oslo, Norway, 2011. [Google Scholar]
- Björkman, B.; Bäckblom, G.; Greberg, J.; Weihed, P. Strategic Research and Innovation Agenda for Swedish Mining and Metal Producing Industry (STRIM), Swedish Mining Innovation. 2013. Available online: https://www.swedishmininginnovation.se/wp-content/uploads/2016/11/STRIM-agenda_2013.pdf (accessed on 14 September 2024).
- Ellem, B. Resource peripheries and neoliberalism: The Pilbara and the remaking of industrial relations in Australia. Aust. Geogr. 2015, 46, 323–337. [Google Scholar] [CrossRef]
- Tinto, R. Rio Tinto. In MarketLine Company Profile; KBC: Brussels, Belgium, 2015; pp. 1–36. [Google Scholar]
- Trott, S. Iron Ore Western Australia, Rio Tinto. 2023. Available online: https://www.riotinto.com/en/operations/australia/iron-ore-western-australia#:~:text=The%20Pilbara%2C%20Western%20Australia,rapidly%20to%20changes%20in%20demand (accessed on 29 August 2024).
- Bearne, G. Innovation in mining: Rio Tinto’s Mine of the Future (TM) programme. Alum. Int. Today 2014, 26, 15. [Google Scholar]
- Leung, R.; Hill, A.J.; Melkumyan, A. Automation and Artificial Intelligence Technology in Surface Mining: A Brief Introduction to Open-Pit Operations in the Pilbara. IEEE Robot. Autom. Mag. 2023, 2–21. [Google Scholar] [CrossRef]
- Duuring, P.; Teitler, Y.; Hagemann, S.G. Banded iron formation-hosted iron ore deposits of the Pilbara Craton. In Australian Ore Deposits; Australasian Institute of Mining and Metallurgy: Carlton, Australia, 2017; pp. 345–350. [Google Scholar]
- Bus, G.D. Pilbara Iron Ore Agreements Processing Obligations and Outcomes. Ph.D. Thesis, Murdoch University, Perth, Australia, 2015. [Google Scholar]
- Murphy, P. Pilbara Iron Ore Agreements Processing Obligations and Outcomes. Doctoral Dissertation, Murdoch University, Perth, Australia, 2015. [Google Scholar]
- Law, W.B.; Slack, M.J.; Ostendorf, B.; Lewis, M.M. Digital terrain analysis reveals new insights into the topographic context of Australian Aboriginal stone arrangements. Archaeol. Prospect. 2017, 24, 169–179. [Google Scholar] [CrossRef]
- Baynes, F.J.; Fookes, P.G.; Kennedy, J.F. The total engineering geology approach applied to railways in the Pilbara, Western Australia. Bull. Eng. Geol. Environ. 2005, 64, 67–94. [Google Scholar] [CrossRef]
- BHP. Surface Mining Operations at South Flank Mine, Pilbara, Image, The University of Adelaide; Reproduced with permission from BHP; BHP: New Delhi, India, 2024. [Google Scholar]
- Gordon, D.M. From division of labor to the collective behavior of social insects. Behav. Ecol. Sociobiol. 2016, 70, 1101–1108. [Google Scholar] [CrossRef] [PubMed]
- Constantino, P.B.; Valentinuzzi, V.S.; Helene, A.F. Division of labor in work shifts by leaf-cutting ants. Sci. Rep. 2021, 11, 8737. [Google Scholar] [CrossRef] [PubMed]
- Di Pietro, V.; Govoni, P.; Chan, K.H.; Oliveira, R.C.; Wenseleers, T.; van den Berg, P. Evolution of self-organised division of labour driven by stigmergy in leaf-cutter ants. Sci. Rep. 2022, 12, 21971. [Google Scholar] [CrossRef] [PubMed]
- Fister, I.; Fister, I., Jr.; Yang, X.S.; Brest, J. A comprehensive review of firefly algorithms. Swarm Evol. Comput. 2013, 13, 34–46. [Google Scholar] [CrossRef]
- Palmieri, N.; Marano, S. Discrete firefly algorithm for recruiting task in a swarm of robots. In Nature-Inspired Computation in Engineering; Springer: Berlin/Heidelberg, Germany, 2016; pp. 133–150. [Google Scholar]
- Karaboga, D.; Gorkemli, B.; Ozturk, C.; Karaboga, N. A comprehensive survey: Artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 2014, 42, 21–57. [Google Scholar] [CrossRef]
- Kiran, M.S. Improved artificial bee colony algorithm for continuous optimization problems. J. Comput. Commun. 2014, 2, 108. [Google Scholar] [CrossRef]
Source of Variation | Sum of Squares (SS) | Degree of Freedom (df) | Mean Squares (MS) | F-Ratio | p-Value |
---|---|---|---|---|---|
Between groups | 1,740,254 | 3 | 580,085 | 2307 | <0.05 |
Within groups | 14,084 | 56 | |||
Total | 1,754,337 | 59 |
Comparison | Mean Difference | p-Value | 95% CI Lower | 95% CI Upper |
---|---|---|---|---|
Baseline vs. Ant | 135.87 | <0.01 | 119.37 | 152.37 |
Baseline vs. Firefly | 222.13 | <0.01 | 205.63 | 238.63 |
Baseline vs. Honeybee | 387.53 | <0.01 | 371.03 | 404.03 |
Ant vs. Firefly | 86.27 | <0.01 | 69.77 | 102.77 |
Ant vs. Honeybee | 251.67 | <0.01 | 135.17 | 168.17 |
Honeybee vs. Firefly | 165.40 | <0.01 | 148.90 | 181.90 |
Swarm Model | Mining Task | Strength |
---|---|---|
Ant model | Ore transportation | High reliability and task specialization |
Firefly model | Ore detection | Robust communication and adaptability |
Honeybee model | Selective ore extraction | High precision and centralized control |
Mining Methods | Ant Model | Firefly Model | Honeybee Model | |
---|---|---|---|---|
Surface Mining Methods | Open-Pit Mining | |||
Strip Mining | ||||
Placer Mining | ||||
Dredging | ||||
Underground Mining Methods | Room-and-Pillar Mining | |||
Longwall Mining | ||||
Block Caving Mining | ||||
Cut-and-Fill Mining | ||||
Shrinkage Stoping | ||||
Sublevel Stoping | ||||
Specialized Mining Techniques | In Situ Leaching | |||
Solution Mining | ||||
Heap Leaching | ||||
Hydraulic Mining | ||||
Space Mining |
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Tan, J.; Melkoumian, N.; Harvey, D.; Akmeliawati, R. Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application. Appl. Sci. 2024, 14, 8876. https://doi.org/10.3390/app14198876
Tan J, Melkoumian N, Harvey D, Akmeliawati R. Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application. Applied Sciences. 2024; 14(19):8876. https://doi.org/10.3390/app14198876
Chicago/Turabian StyleTan, Joven, Noune Melkoumian, David Harvey, and Rini Akmeliawati. 2024. "Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application" Applied Sciences 14, no. 19: 8876. https://doi.org/10.3390/app14198876
APA StyleTan, J., Melkoumian, N., Harvey, D., & Akmeliawati, R. (2024). Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application. Applied Sciences, 14(19), 8876. https://doi.org/10.3390/app14198876