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Machine Learning for Traffic Control of Unmanned Mining Machines: Using the Q-learning and SARSA algorithms
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
2019 (English)Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesisAlternative title
Maskininlärning för Trafikkontroll av Obemannade Gruvmaskiner : Med användning av algoritmerna Q-learning och SARSA (Swedish)
Abstract [en]

Manual configuration of rules for unmanned mining machine traffic control can be time-consuming and therefore expensive. This paper presents a Machine Learning approach for automatic configuration of rules for traffic control in mines with autonomous mining machines by using Q-learning and SARSA. The results show that automation might be able to cut the time taken to configure traffic rules from 1-2 weeks to a maximum of approximately 6 hours which would decrease the cost of deployment. Tests show that in the worst case the developed solution is able to run continuously for 24 hours 82% of the time compared to the 100% accuracy of the manual configuration. The conclusion is that machine learning can plausibly be used for the automatic configuration of traffic rules. Further work in increasing the accuracy to 100% is needed for it to replace manual configuration. It remains to be examined whether the conclusion retains pertinence in more complex environments with larger layouts and more machines.

Abstract [sv]

Manuell konfigurering av trafikkontroll för obemannade gruvmaskiner kan vara en tidskrävande process. Om denna konfigurering skulle kunna automatiseras så skulle det gynnas tidsmässigt och ekonomiskt. Denna rapport presenterar en lösning med maskininlärning med Q-learning och SARSA som tillvägagångssätt. Resultaten visar på att konfigureringstiden möjligtvis kan tas ned från 1–2 veckor till i värsta fallet 6 timmar vilket skulle minska kostnaden för produktionssättning. Tester visade att den slutgiltiga lösningen kunde köra kontinuerligt i 24 timmar med minst 82% träffsäkerhet jämfört med 100% då den manuella konfigurationen används. Slutsatsen är att maskininlärning eventuellt kan användas för automatisk konfiguration av trafikkontroll. Vidare arbete krävs för att höja träffsäkerheten till 100% så att det kan användas istället för manuell konfiguration. Fler studier bör göras för att se om detta även är sant och applicerbart för mer komplexa scenarier med större gruvlayouts och fler maskiner.

Place, publisher, year, edition, pages
2019. , p. 54
Series
TRITA-CBH-GRU ; 2019:123
Keywords [en]
Machine Learning, reinforcement learning, Q-learning, SARSA, autonomous machines, mining
Keywords [sv]
Maskininlärning, reinforcement learning, Q-learning, SARSA, självstyrande maskiner, gruvdrift
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:kth:diva-260285OAI: oai:DiVA.org:kth-260285DiVA, id: diva2:1355105
Subject / course
Computer Technology and Software Engineering
Educational program
Bachelor of Science in Engineering - Computer Engineering
Supervisors
Examiners
Available from: 2019-09-27 Created: 2019-09-26 Last updated: 2019-09-27Bibliographically approved

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