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Implementation of Machine Learning Algorithms on Multi-Robot Coordination

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dc.creator YİĞİT, Tuncay
dc.creator ÇANKAYA, Şadi Fuat
dc.date 2022-06-01T00:00:00Z
dc.date.accessioned 2023-01-09T12:06:09Z
dc.date.available 2023-01-09T12:06:09Z
dc.identifier a36c59e1-2757-436f-87c7-fb79f1727599
dc.identifier 10.3390/electronics11111786
dc.identifier https://avesis.sdu.edu.tr/publication/details/a36c59e1-2757-436f-87c7-fb79f1727599/oai
dc.identifier.uri http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/98193
dc.description Occasionally, professional rescue teams encounter issues while rescuing people during earthquake collapses. One such issue is the localization of wounded people from the earthquake. Machines used by rescue teams may cause crucial issues due to misleading localization. Usually, robot technology is utilized to address this problem. Many research papers addressing rescue operations have been published in the last two decades. In the literature, there are few studies on multi-robot coordination. The systems designed with a single robot should also overcome time constraints. A sophisticated algorithm should be developed for multi-robot coordination to solve that problem. Then, a fast rescuing operation could be performed. The distinctive property of this study is that it proposes a multi-robot system using a novel heuristic bat-inspired algorithm for use in search and rescue operations. Bat-inspired techniques gained importance in soft-computing experiments. However, there are only single-robot systems for robot navigation. Another original aspect of this paper is that this heuristic algorithm is employed to coordinate the robots. The study is devised to encourage extended work related to earthquake collapse rescue operations.
dc.language eng
dc.rights info:eu-repo/semantics/openAccess
dc.title Implementation of Machine Learning Algorithms on Multi-Robot Coordination
dc.type info:eu-repo/semantics/article


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