A DECENTRALISED COLLISION AVOIDANCE METHOD BASED ON Q-LEARNING FOR MULTI-AGV SYSTEMS

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dc.authorid0000-0002-2780-3386
dc.contributor.authorCoban, Mustafa
dc.contributor.authorGelen, Gokhan
dc.date.accessioned2026-02-08T15:15:52Z
dc.date.available2026-02-08T15:15:52Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractAutomated guided vehicles are widely used transport systems in factories, warehouses, and distribution centres. The control and coordination of vehicles is of great importance for safe and efficient transport in multi-vehicle systems. In this study, a collision avoidance strategy is proposed for automated guided vehicle systems operating in environments with shared work zones and conflicting routes. In the proposed method, finite state machines are used to model the motion of vehicles in the environment. Q-learning, one of the most common algorithms of reinforcement learning, is used for collision avoidance. In the presented strategy, a decentralised control approach is utilized to reduce the computational complexity. The proposed method is validated through simulations involving multi-vehicle system applications with several collision zones. Simulation results demonstrate that the proposed method can avoid potential collisions and significantly improve overall efficiency.
dc.identifier.doi10.21278/TOF.491063824
dc.identifier.endpage68
dc.identifier.issn1333-1124
dc.identifier.issue1
dc.identifier.scopus2-s2.0-105003430725
dc.identifier.scopusqualityQ3
dc.identifier.startpage51
dc.identifier.urihttps://doi.org/10.21278/TOF.491063824
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5996
dc.identifier.volume49
dc.identifier.wosWOS:001482085600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherUniv Zagreb Fac Mechanical Engineering & Naval Architecture
dc.relation.ispartofTransactions of Famena
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectautomated guided vehicle
dc.subjectcollision avoidance
dc.subjectfinite state machines
dc.subjectq-learning
dc.subjectreinforcement learning
dc.titleA DECENTRALISED COLLISION AVOIDANCE METHOD BASED ON Q-LEARNING FOR MULTI-AGV SYSTEMS
dc.typeArticle

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