Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/30359
Title: | Q-learning based failure detection and self-recovery algorithm for multi-robot domains | Authors: | Kayır, Hatice Hilal Ezercan | Keywords: | Autonomous systems Intelligent robots Multi-robot systems Robot learning |
Publisher: | Kauno Technologijos Universitetas | Abstract: | —Task allocation is the essential part of multirobot coordination researches and it plays a significant role to achieve desired system performance. Uncertainties in multirobot systems’ working environment due to nature of them are the major hurdle for perfect coordination. When learning-based task allocation approaches are used, firstly robots learn about their working environment and then they benefit from their experiences in future task allocation process. These approaches provide useful solutions as long as environmental conditions remain unchanged. If permanent changes in environment characteristics or some failure in multi-robot system occur undesirably e.g. in disaster response which is a good example to represent such cases, the previously-learned information becomes invalid. At this point, the most important mission is to detect the failure and to recover the system initial learning state. For this purpose, Q-learning based failure detection and self-recovery algorithm is proposed in this study. According to this approach, multi-robot system checks whether these variations permanent, then recover the system to learning state if it is required. So, it provides dynamic task allocation procedure having great advantages against unforeseen situations. The experimental results verify that the proposed algorithm offer efficient solutions for multi-robot task allocation problem even in systemic failure cases. © 2019 Kauno Technologijos Universitetas. All rights reserved. | URI: | https://hdl.handle.net/11499/30359 https://doi.org/10.5755/j01.eie.25.2.23197 |
ISSN: | 1392-1215 |
Appears in Collections: | Mühendislik Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
Files in This Item:
File | Size | Format | |
---|---|---|---|
Hatice Hilal Ezercan.pdf | 761.38 kB | Adobe PDF | View/Open |
CORE Recommender
Page view(s)
42
checked on Aug 9, 2024
Download(s)
16
checked on Aug 9, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.