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Self-sustaining Learning for Robotic Ecologies

Bacciu, D, Broxvall, M, Coleman, SA, Dragone, M, Gallicchio, C, Gennaro, C, Guzman, R, Lopez, R, Lozano-Peiteado, H, Ray, Anjan, Renteria, A, Saffiotti, A and Vairo, C (2012) Self-sustaining Learning for Robotic Ecologies. In: 1st International Conference on Sensor Networks, Rome, Italy. SciTePress. 5 pp. [Conference contribution]

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URL: HTTP://www.fp7rubicon.eu


The most common use of wireless sensor networks (WSNs) is to collect environmental data from a specific area, and to channel it to a central processing node for on-line or off-line analysis. The WSN technology, however, can be used for much more ambitious goals. We claim that merging the concepts and technology of WSN with the concepts and technology of distributed robotics and multi-agent systems can open new ways to design systems able to provide intelligent services in our homes and working places. We also claim that endowing these systems with learning capabilities can greatly increase their viability and acceptability, by simplifying design, customization and adaptation to changing user needs. To support these claims, we illustrate our architecture for an adaptive robotic ecology, named RUBICON, consisting of a network of sensors,effectors and mobile robots.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:23180
Deposited By: Dr Sonya Coleman
Deposited On:28 Aug 2012 15:08
Last Modified:28 Aug 2012 15:08

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