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Reinforcement-learning-based route generation for heavy-traffic autonomous mobile robot systems
ID
Kozjek, Dominik
(
Author
),
ID
Malus, Andreja
(
Author
),
ID
Vrabič, Rok
(
Author
)
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MD5: 4BD306C07FB28D74C71B2D60FA9C8796
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https://www.mdpi.com/1424-8220/21/14/4809
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Abstract
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.
Language:
English
Keywords:
intralogistics
,
autonomous mobile robots
,
multi-robot cooperation
,
reinforcement learning
,
route planning
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
FS - Faculty of Mechanical Engineering
Publication status:
Published
Publication version:
Version of Record
Year:
2021
Number of pages:
19 str.
Numbering:
Vol. 21, iss. 14, art. 4809
PID:
20.500.12556/RUL-130805
UDC:
007.52(045)
ISSN on article:
1424-8220
DOI:
10.3390/s21144809
COBISS.SI-ID:
76747779
Publication date in RUL:
17.09.2021
Views:
1798
Downloads:
272
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Record is a part of a journal
Title:
Sensors
Shortened title:
Sensors
Publisher:
MDPI
ISSN:
1424-8220
COBISS.SI-ID:
10176278
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
14.07.2021
Secondary language
Language:
Slovenian
Keywords:
intralogistika
,
avtonomni mobilni roboti
,
spodbujevalno učenje
Projects
Funder:
ARRS - Slovenian Research Agency
Project number:
P2-0270
Name:
Proizvodni sistemi, laserske tehnologije in spajanje materialov
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