Andreja Malus (Avtor), Dominik Kozjek (Avtor), Rok Vrabič (Avtor)

Povzetek

Autonomous mobile robots (AMRs) are increasingly being used to enable efficient material flow in dynamic production environments. Dispatching transport orders in such environments is difficult due to the complexity arising from the rapid changes in the environment as well as due to a tight coupling between dispatching, path planning, and route execution. For order dispatching, an approach is proposed that uses multi-agent reinforcement learning, where AMR agents learn to bid on orders based on their individual observations. The approach is investigated in a robot simulation environment. The results show a more efficient order allocation compared to commonly used dispatching rules.

Ključne besede

logistics;machine learning;distributed control;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FS - Fakulteta za strojništvo
UDK: 681.5(045)
COBISS: 24176643 Povezava se bo odprla v novem oknu
ISSN: 0007-8506
Št. ogledov: 489
Št. prenosov: 82
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: logistika;strojno učenje;porazdeljeno krmiljenje;
Vrsta dela (COBISS): Članek v reviji
Konec prepovedi (OpenAIRE): 2022-05-17
Strani: str. 397-400
Letnik: ǂVol. ǂ69
Zvezek: ǂiss. ǂ1
Čas izdaje: 2020
DOI: 10.1016/j.cirp.2020.04.001
ID: 11928334