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Artificial intelligence based framework for robotic search and rescue operations conducted jointly by international teams

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dc.contributor.author Magid E.
dc.contributor.author Pashkin A.
dc.contributor.author Simakov N.
dc.contributor.author Abbyasov B.
dc.contributor.author Suthakorn J.
dc.contributor.author Svinin M.
dc.contributor.author Matsuno F.
dc.date.accessioned 2021-02-25T06:55:25Z
dc.date.available 2021-02-25T06:55:25Z
dc.date.issued 2020
dc.identifier.issn 2190-3018
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/161543
dc.description.abstract © Springer Nature Singapore Pte Ltd 2020. Many countries suffer from various natural disasters, including heavy rains, that are associated with further flood and landslide disasters. Based on our experiences of different disasters response, we develop a joint international operation framework for a disaster site management with distributed heterogeneous robotic teams that consist of unmanned aerial, ground, surface, and underwater vehicles. The artificial intelligence-based information collection system, which is targeting to become a worldwide standard, contains interaction protocols, thematic mapping approaches, and map fusion processes. The project provides a new working framework and control strategies for heterogeneous robotic teams’ cooperative behavior in sensing, monitoring, and mapping of flood and landslide disaster areas. In this paper, we present an overview of the system and a first stage toward robot interaction protocols development and the system modeling within robot operating system’s Gazebo environment.
dc.relation.ispartofseries Smart Innovation, Systems and Technologies
dc.subject Gazebo
dc.subject Heterogeneous robotic teams
dc.subject Information system
dc.subject Robotics
dc.subject Ros
dc.subject Urban search and rescue
dc.subject Usar
dc.title Artificial intelligence based framework for robotic search and rescue operations conducted jointly by international teams
dc.type Conference Paper
dc.relation.ispartofseries-volume 154
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 15
dc.source.id SCOPUS21903018-2020-154-SID85072901857


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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