SELF-SUSTAINING LEARNING FOR ROBOTIC ECOLOGIES

D. Bacciu, M. Broxvall, S. Coleman, M. Dragone, C. Gallicchio, C. Gennaro, R. Guzmán, R. Lopez, H. Lozano-Peiteado, A. Ray, A. Renteria, A. Saffiotti, C. Vairo

2012

Abstract

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.

Paper Citation

in Harvard Style

Bacciu D., Broxvall M., Coleman S., Dragone M., Gallicchio C., Gennaro C., Guzmán R., Lopez R., Lozano-Peiteado H., Ray A., Renteria A., Saffiotti A. and Vairo C. (2012). SELF-SUSTAINING LEARNING FOR ROBOTIC ECOLOGIES . In Proceedings of the 1st International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-8565-01-3, pages 99-103. DOI: 10.5220/0003905100990103

in Bibtex Style

@conference{sensornets12,
author={D. Bacciu and M. Broxvall and S. Coleman and M. Dragone and C. Gallicchio and C. Gennaro and R. Guzmán and R. Lopez and H. Lozano-Peiteado and A. Ray and A. Renteria and A. Saffiotti and C. Vairo},
title={SELF-SUSTAINING LEARNING FOR ROBOTIC ECOLOGIES},
booktitle={Proceedings of the 1st International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2012},
pages={99-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003905100990103},
isbn={978-989-8565-01-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - SELF-SUSTAINING LEARNING FOR ROBOTIC ECOLOGIES
SN - 978-989-8565-01-3
AU - Bacciu D.
AU - Broxvall M.
AU - Coleman S.
AU - Dragone M.
AU - Gallicchio C.
AU - Gennaro C.
AU - Guzmán R.
AU - Lopez R.
AU - Lozano-Peiteado H.
AU - Ray A.
AU - Renteria A.
AU - Saffiotti A.
AU - Vairo C.
PY - 2012
SP - 99
EP - 103
DO - 10.5220/0003905100990103