Self-sustaining Learning for Robotic Ecologies

D Bacciu, M Broxvall, SA Coleman, M Dragone, C Gallicchio, C Gennaro, R Guzman, R Lopez, H Lozano-Peiteado, Anjan Ray, A Renteria, A Saffiotti, C Vairo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages99-103
Number of pages5
Publication statusPublished - 22 Feb 2012
Event1st International Conference on Sensor Networks - Rome, Italy
Duration: 22 Feb 2012 → …

Conference

Conference1st International Conference on Sensor Networks
Period22/02/12 → …

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Ecology
Wireless sensor networks
Robotics
Multi agent systems
Merging
Mobile robots
Sensors
Processing

Cite this

Bacciu, D., Broxvall, M., Coleman, SA., Dragone, M., Gallicchio, C., Gennaro, C., ... Vairo, C. (2012). Self-sustaining Learning for Robotic Ecologies. In Unknown Host Publication (pp. 99-103)
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 ; Vairo, C. / Self-sustaining Learning for Robotic Ecologies. Unknown Host Publication. 2012. pp. 99-103
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Bacciu, D, Broxvall, M, Coleman, SA, Dragone, M, Gallicchio, C, Gennaro, C, Guzman, R, Lopez, R, Lozano-Peiteado, H, Ray, A, Renteria, A, Saffiotti, A & Vairo, C 2012, Self-sustaining Learning for Robotic Ecologies. in Unknown Host Publication. pp. 99-103, 1st International Conference on Sensor Networks, 22/02/12.

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; Vairo, C.

Unknown Host Publication. 2012. p. 99-103.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Guzman, R

AU - Lopez, R

AU - Lozano-Peiteado, H

AU - Ray, Anjan

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N2 - 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.

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Bacciu D, Broxvall M, Coleman SA, Dragone M, Gallicchio C, Gennaro C et al. Self-sustaining Learning for Robotic Ecologies. In Unknown Host Publication. 2012. p. 99-103