Robotic UBIquitous COgnitive Network
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|Title:||Robotic UBIquitous COgnitive Network||Authors:||Amato, G.
McGinnity, T. M.
O'Hare, G. M. P. (Greg M. P.)
|Permanent link:||http://hdl.handle.net/10197/4341||Date:||2012||Online since:||2013-12-01T04:00:10Z||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 self-adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The EU FP7 project RUBICON develops self-sustaining learning solutions yielding cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, agent control systems, wireless sensor networks and machine learning. This paper briefly illustrates how these techniques are being extended, integrated, and applied to AAL applications.||Type of material:||Conference Publication||Publisher:||Springer||Copyright (published version):||2012 Springer-Verlag||Keywords:||Robotic ecologies; Self-adaptive; Smart robotic environments||DOI:||10.1007/978-3-642-28783-1_23||Language:||en||Status of Item:||Peer reviewed||Is part of:||Novais, Paulo et al. (eds.). Ambient Intelligence - Software and Applications : Advances in Intelligent and Soft Computing Volume 153||Conference Details:||3rd International Symposium on Ambient Intelligence, Salamanca, Spain, 2012||ISBN:||978-3-642-28782-4|
|Appears in Collections:||Computer Science Research Collection|
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