Reinforcement-learning-based route generation for heavy-traffic autonomous mobile robot systems

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

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

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