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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Broxvall, M., Seo, B.S., Kwon, W.Y.: The PEIS Kernel: A Middleware for Ubiquitous Robotics. In: Proc. of the IROS 2007 Workshop on Ubiquitous Robotic Space Design and Applications, San Diego, California (October 2007)
Amato, G., Chessa, S., Vairo, C.: MaDWiSe: A Distributed Stream Management System for Wireless Sensor Networks. Software Practice & Experience 40(5) (2010)
Muldoon, C., O Hare, G.M.P., O’ Grady, M.J.: AFME: An Agent Platform for Resource Constrained Devices. In: Proceedings of the ESAW 2006 (2006)
Pecora, F., Cirillo, M.: A Constraint-Based Approach for Plan Management in Intelligent Environments. In: Proc. of the Scheduling and Planning Applications Workshop at ICAPS 2009 (2009)
Bacciu, D., Gallicchio, C., Micheli, A., Chessa, S., Barsocchi, P.: Predicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing. In: Proc. of the IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI), Barcellona, Spain, pp. 1–6 (2011)
Lukosevicius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review 3(3), 127–149 (2009)
Prasad, G., Leng, G., McGinnity, T.M.: On-line identification of self-organising fuzzy neural networks for modelling time-varying complex systems. In: Plamen, et al. (eds.) Evolving Intelligent Systems: Methodology and Applications, pp. 302–324. Wiley-IEEE Press (2010)
Mannella, F., Mirolli, M., Baldassarre, G.: Brain Mechanisms underlying Learning of Habits and Goal-Driven Behaviour: A Computational Model of Devaluation Experiments Tested with a Simulated Rat. In: Tosh, C. (ed.) Neural Network Models. Cambridge University Press
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Amato, G. et al. (2012). Robotic UBIquitous COgnitive Network. In: Novais, P., Hallenborg, K., Tapia, D., Rodríguez, J. (eds) Ambient Intelligence - Software and Applications. Advances in Intelligent and Soft Computing, vol 153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28783-1_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-28783-1_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28782-4
Online ISBN: 978-3-642-28783-1
eBook Packages: EngineeringEngineering (R0)