A cognitive robotic ecology approach to self-configuring and evolving AAL systems
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2015-10-01
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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.
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Publisher Copyright: © 2015 Elsevier Ltd. All rights reserved.
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Robotic ecology , Ambient assisted living , Cognitive robotics , Machine learning , Planning , Robotic ecology , Ambient assisted living , Cognitive robotics , Machine learning , Planning , Control and Systems Engineering , Artificial Intelligence , Electrical and Electronic Engineering , Project ID , info:eu-repo/grantAgreement/EC/FP7/269914/EU/Robotics UBIquitous COgnitive Network/RUBICON , info:eu-repo/grantAgreement/EC/FP7/269914/EU/Robotics UBIquitous COgnitive Network/RUBICON , Funding Info , European Commission. EC | FP7 | SP1 | ICT . FP7-ICT-2009-6. , European Commission. EC | FP7 | SP1 | ICT . FP7-ICT-2009-6.
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Dragone , M , Amato , G , Bacciu , D , Chessa , S , Coleman , S , Di Rocco , M , Gallicchio , C , Gennaro , C , Lozano , H , Maguire , L , McGinnity , M , Micheli , A , O׳Hare , G M P , Renteria , A , Saffiotti , A , Vairo , C , Vance , P & O'Hare , G M P 2015 , ' A cognitive robotic ecology approach to self-configuring and evolving AAL systems ' , unknown , vol. unknown , pp. 269-280 . https://doi.org/10.1016/j.engappai.2015.07.004