20.500.12556/RUL-130805 Reinforcement-learning-based route generation for heavy-traffic autonomous mobile robot systems 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. intralogistics autonomous mobile robots multi-robot cooperation reinforcement learning route planning intralogistika avtonomni mobilni roboti spodbujevalno učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2021-09-17 09:59:06 2021-09-17 09:59:09 2022-09-09 04:18:07 0000-00-00 00:00:00 2021 0 0 19 str. iss. 14, art. 4809 Vol. 21 Jul. 2021 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 007.52(045) 1424-8220 10.3390/s21144809 76747779 RAZ_Kozjek_Dominik_2021.pdf RAZ_Kozjek_Dominik_2021.pdf 1 4BD306C07FB28D74C71B2D60FA9C8796 e3a062b7d62fff7667b9af23ba54fa866baf2e3f858352af3081c82fcbf859e2 f6ff24ad-b0d5-11ec-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=155162 https://www.mdpi.com/1424-8220/21/14/4809 1 e0b10712-178c-11ec-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=147964 Fakulteta za strojništvo 0 0 0