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Robotic Ubiquitous Cognitive Ecology for Smart Homes

Authors:

T. M. Mcginnity,

P. VanceAuthors Info & Claims

Journal of Intelligent and Robotic Systems, Volume 80, Issue 1

Pages 57 - 81

https://doi.org/10.1007/s10846-015-0178-2

Published: 01 October 2015 Publication History

Abstract

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.

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Cited By

Gomez CChessa SFleury ARoussos GPreuveneers D(2019)Internet of Things for enabling smart environmentsJournal of Ambient Intelligence and Smart Environments10.3233/AIS-18050911:1(23-43)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/AIS-180509
Muhammad SMohammad NBashar AKhan M(2019)Designing Human Assisted Wireless Sensor and Robot Networks Using Probabilistic Model CheckingJournal of Intelligent and Robotic Systems10.1007/s10846-018-0901-x94:3-4(687-709)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1007/s10846-018-0901-x
Bacciu DChessa SGallicchio CMicheli AHamdan HBoubiche DKlett F(2017)On the need of machine learning as a service for the internet of thingsProceedings of the 1st International Conference on Internet of Things and Machine Learning10.1145/3109761.3109783(1-8)Online publication date: 17-Oct-2017
https://dl.acm.org/doi/10.1145/3109761.3109783
Show More Cited By

Robotic Ubiquitous Cognitive Ecology for Smart Homes
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling

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cover image Journal of Intelligent and Robotic Systems

Journal of Intelligent and Robotic Systems Volume 80, Issue 1

October 2015

528 pages

ISSN:0921-0296

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Copyright © Copyright © 2015 Springer Science+Business Media Dordrecht.

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Kluwer Academic Publishers

United States

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Published: 01 October 2015

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Cited By

Gomez CChessa SFleury ARoussos GPreuveneers D(2019)Internet of Things for enabling smart environmentsJournal of Ambient Intelligence and Smart Environments10.3233/AIS-18050911:1(23-43)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/AIS-180509
Muhammad SMohammad NBashar AKhan M(2019)Designing Human Assisted Wireless Sensor and Robot Networks Using Probabilistic Model CheckingJournal of Intelligent and Robotic Systems10.1007/s10846-018-0901-x94:3-4(687-709)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1007/s10846-018-0901-x
Bacciu DChessa SGallicchio CMicheli AHamdan HBoubiche DKlett F(2017)On the need of machine learning as a service for the internet of thingsProceedings of the 1st International Conference on Internet of Things and Machine Learning10.1145/3109761.3109783(1-8)Online publication date: 17-Oct-2017
https://dl.acm.org/doi/10.1145/3109761.3109783
Georgievski IAiello M(2016)Automated Planning for Ubiquitous ComputingACM Computing Surveys10.1145/300429449:4(1-46)Online publication date: 5-Dec-2016
https://dl.acm.org/doi/10.1145/3004294
Bacciu D(2016)Unsupervised feature selection for sensor time-series in pervasive computing applicationsNeural Computing and Applications10.1007/s00521-015-1924-x27:5(1077-1091)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s00521-015-1924-x

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