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Self-sustaining learning for robotic ecologies

Bacciu, D. (author)
Broxvall, Mathias, 1976- (author)
Örebro universitet,Institutionen för naturvetenskap och teknik,AASS
Coleman, S. (author)
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Dragone, M. (author)
Gallicchio, C. (author)
Gennaro, C. (author)
Guzmán, R. (author)
Lopez, R. (author)
Lozano-Peiteado, H. (author)
Ray, A. (author)
Renteria, A. (author)
Saffiotti, Alessandro (author)
Örebro universitet,Institutionen för naturvetenskap och teknik,AASS
Vairo, C. (author)
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 (creator_code:org_t)
2012
2012
English.
  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • The most common use of wireless sensor networks (WSNs) is to collect environmental data from a specificarea, 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 ofWSN with the concepts and technology of distributed robotics and multi-agent systems can open new waysto design systems able to provide intelligent services in our homes and working places. We also claim thatendowing these systems with learning capabilities can greatly increase their viability and acceptability, bysimplifying design, customization and adaptation to changing user needs. To support these claims, we illus-trate our architecture for an adaptive robotic ecology, named RUBICON, consisting of a network of sensors,effectors and mobile robots.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)

Keyword

Robotic Ecology
Wireless Sensor Network
Learning
Datalogi
Computer Science

Publication and Content Type

ref (subject category)
kon (subject category)

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