Self-sustaining Learning for Robotic Ecologies

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