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Robotic Ubiquitous Cognitive Ecology for Smart Homes

Amato, G. (author)
Consiglio Nazionale delle Ricerche-Istituto di Scienza e Tecnologie dell'Informazione (CNR-ISTI), Pisa, Italy
Bacciu, D. (author)
Università di Pisa, Pisa, Italy
Broxvall, Mathias, 1976- (author)
Örebro Universitet, Örebro, Sweden
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Chessa, S. (author)
Università di Pisa, Pisa, Italy
Coleman, S. (author)
University of Ulster, Ulster, UK
Di Rocco, Maurizio, 1982- (author)
Örebro Universitet, Örebro, Sweden
Dragone, M. (author)
Trinity College Dublin, Dublin, Ireland
Gallicchio, C. (author)
Università di Pisa, Pisa, Italy
Gennaro, C. (author)
Consiglio Nazionale delle Ricerche-Istituto di Scienza e Tecnologie dell'Informazione (CNR-ISTI), Pisa, Italy
Lozano, H. (author)
Tecnalia, Madrid, Spain
McGinnity, T. M. (author)
University of Ulster, Ulster, UK
Micheli, A. (author)
Università di Pisa, Pisa, Italy
Ray, A. K. (author)
University of Ulster, Ulster, UK
Renteria, A. (author)
Tecnalia, Madrid, Spain
Saffiotti, Alessandro, 1960- (author)
Örebro universitet,Institutionen för naturvetenskap och teknik
Swords, D. (author)
University College Dublin, Dublin, Ireland
Vairo, C. (author)
Consiglio Nazionale delle Ricerche (CNR)-Istituto di Scienza e Tecnologie dell'Informazione (ISTI), Pisa, Italy
Vance, P. (author)
University of Ulster, Ulster, UK
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 (creator_code:org_t)
2015-02-01
2015
English.
In: Journal of Intelligent and Robotic Systems. - : Springer. - 0921-0296 .- 1573-0409. ; 80, s. S57-S81
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Keyword

Robotic ecology
Networked robotics
Ambient assisted living
Cognitive robotics
Wireless sensor and actuator networks
Home automation
Activity recognition
Activity discovery
Computer Science
Datavetenskap

Publication and Content Type

ref (subject category)
art (subject category)

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