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.
|Title of host publication||Ambient Intelligence-Software and Applications|
|Subtitle of host publication||3rd International Symposium on Ambient Intelligence (ISAmI 2012)|
|Editors||Paulo Novais, Kasper Hallenborg, Dante I. Tapia, Juan M. Corchado Rodríguez|
|Number of pages||5|
|Publication status||Published - 2012|
|Name||Advances in Intelligent and Soft Computing|