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Title: IoT driven ambient intelligence architecture for indoor intelligent mobility
Authors: De Silva, Varuna
Roche, Jamie
Shi, Xiyu
Kondoz, Ahmet
Keywords: Ambient
Assistive
Autonomous
Intelligent
Mobility
Robot
Navigation
Issue Date: 2018
Publisher: © IEEE
Citation: DE SILVA, V. ... et al, 2018. IoT driven ambient intelligence architecture for indoor intelligent mobility. IN: 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), Athens, Greece, 12-15 August 2018, pp.451-456.
Abstract: Personal robots are set to assist humans in their daily tasks. Assisted living is one of the major applications of personal assistive robots, where the robots will support health and wellbeing of the humans in need, especially elderly and disabled. Indoor environments are extremely challenging from a robot perception and navigation point of view, because of the ever-changing decorations, internal organizations and clutter. Furthermore, human-robot-interaction in personal assistive robots demands intuitive and human-like intelligence and interactions. Above challenges are aggravated by stringent and often tacit requirements surrounding personal privacy that may be invaded by continuous monitoring through sensors. Towards addressing the above problems, in this paper we present an architecture for "Ambient Intelligence" for indoor intelligent mobility by leveraging IoTs within a framework of Scalable Multi-layered Context Mapping Framework. Our objective is to utilize sensors in home settings in the least invasive manner for the robot to learn about its dynamic surroundings and interact in a human-like manner. The paper takes a semi-survey approach to presenting and illustrating preliminary results from our in-house built fully autonomous electric quadbike.
Description: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Version: Accepted for publication
DOI: 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00090
URI: https://dspace.lboro.ac.uk/2134/35356
Publisher Link: https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00090
ISBN: 9781538675182
Appears in Collections:Conference Papers and Presentations (Loughborough University London)

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