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Reinforcement-learning-based route generation for heavy-traffic autonomous mobile robot systems
ID Kozjek, Dominik (Avtor), ID Malus, Andreja (Avtor), ID Vrabič, Rok (Avtor)

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Izvleček
Autonomous mobile robots (AMRs) are increasingly used in modern intralogistics systems as complexity and performance requirements become more stringent. One way to increase performance is to improve the operation and cooperation of multiple robots in their shared environment. The paper addresses these problems with a method for off-line route planning and on-line route execution. In the proposed approach, pre-computation of routes for frequent pick-up and drop-off locations limits the movements of AMRs to avoid conflict situations between them. The paper proposes a reinforcement learning approach where an agent builds the routes on a given layout while being rewarded according to different criteria based on the desired characteristics of the system. The results show that the proposed approach performs better in terms of throughput and reliability than the commonly used shortest-path-based approach for a large number of AMRs operating in the system. The use of the proposed approach is recommended when the need for high throughput requires the operation of a relatively large number of AMRs in relation to the size of the space in which the robots operate.

Jezik:Angleški jezik
Ključne besede:intralogistics, autonomous mobile robots, multi-robot cooperation, reinforcement learning, route planning
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FS - Fakulteta za strojništvo
Status publikacije:Objavljeno
Različica publikacije:Objavljena publikacija
Leto izida:2021
Št. strani:19 str.
Številčenje:Vol. 21, iss. 14, art. 4809
PID:20.500.12556/RUL-130805 Povezava se odpre v novem oknu
UDK:007.52(045)
ISSN pri članku:1424-8220
DOI:10.3390/s21144809 Povezava se odpre v novem oknu
COBISS.SI-ID:76747779 Povezava se odpre v novem oknu
Datum objave v RUL:17.09.2021
Število ogledov:930
Število prenosov:191
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Gradivo je del revije

Naslov:Sensors
Skrajšan naslov:Sensors
Založnik:MDPI
ISSN:1424-8220
COBISS.SI-ID:10176278 Povezava se odpre v novem oknu

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:14.07.2021

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:intralogistika, avtonomni mobilni roboti, spodbujevalno učenje

Projekti

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:P2-0270
Naslov:Proizvodni sistemi, laserske tehnologije in spajanje materialov

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