A cognitive robotic ecology approach to self-configuring and evolving AAL systems

Mauro Dragone, Giuseppe Amato, Davide Bacciu, Stefano Chessa, SA Coleman, Maurizio Di Rocco, Claudio Gallicchio, Claudio Gennaro, Hector Lozano, LP Maguire, TM McGinnity, Alessio Micheli, Gregory M.P. O׳Hare, Arantxa Renteria, Alessandro Saffiotti, Claudio Vairo, Philip Vance

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Robotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user׳s activities and changing user׳s habits.
Original languageEnglish
Pages (from-to)269-280
JournalEngineering Applications of Artificial Intelligence
Volume45
DOIs
Publication statusPublished - 1 Oct 2015

Keywords

  • Robotic ecology
  • Ambient assisted living
  • Cognitive robotics
  • Machine learning
  • Planning

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