University of Bahrain
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Mobile Robot Intelligence Based SLAM Features Learning and Navigation

Show simple item record Mattar, Ebrahim Al Mutib, Khalid Al Sulaiman, Mansour Ramdane, Hajar 2018-07-05T10:12:54Z 2018-07-05T10:12:54Z 2018-01-01
dc.identifier.issn 2210-142X
dc.description.abstract For efficient, and knowledge based navigation, it is essential to blend mobile robot navigation details with information and details from navigation paths-localities. In this respect, the presented scheme was focused towards building intelligence for mobile robot navigation. Intelligence was achieved by considering the navigation capabilities while the mobile robot was in motion. The adopted learning paradigm was a five layers Neuro-Fuzzy learning architecture, with to ability to create an FIS inference for enhanced navigation. To capture the enormous visual and non-visual sensory data, the mobile robot platform has fully computerinterfaced stereo vision, and reliable 3D perception system onboard the mobile platform. A Neuro-Fuzzy intelligence paradigm was used to learn navigation maps (SLAM) main visual features, distances, nature of localities as it travels within spaces. Blinding intelligence with visual maps and non-visual sensory data, has indeed resulted in improved navigation capabilities. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International *
dc.rights.uri *
dc.subject Mobile Robots en_US
dc.subject Maps Learning en_US
dc.subject Neuro-Fuzzy FIS en_US
dc.subject Intelligent Navigation en_US
dc.subject Visual Perception en_US
dc.subject PAC en_US
dc.title Mobile Robot Intelligence Based SLAM Features Learning and Navigation en_US
dc.type Article en_US
dc.volume 07
dc.issue 01
dc.pagestart 23
dc.pageend 34
dc.source.title International Journal of Computing and Digital Systems
dc.abbreviatedsourcetitle IJCDS

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