Self-sustaining learning for robotic ecologies.
Bacciu D., Broxvall M., Coleman S., Dragone M., Gallicchio C., Gennaro C., Guzman R., Lopez R., Lozano-Peiteado H., Ray A., Renteria A., Saffiotti A., Vairo C.
Learning; Robotic ecology; Wireless sensor network
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.
Source: 1st International Conference on Sensor Networks, pp. 99–103, Rome, Italy, 24-26 February 2012
Publisher: SciTePress, Lisbona, PRT
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@inproceedings{oai:it.cnr:prodotti:220981,
title = {Self-sustaining learning for robotic ecologies.},
author = {Bacciu D. and Broxvall M. and Coleman S. and Dragone M. and Gallicchio C. and Gennaro C. and Guzman R. and Lopez R. and Lozano-Peiteado H. and Ray A. and Renteria A. and Saffiotti A. and Vairo C.},
publisher = {SciTePress, Lisbona, PRT},
booktitle = {1st International Conference on Sensor Networks, pp. 99–103, Rome, Italy, 24-26 February 2012},
year = {2012}
}
0000-0002-3715-149X