Browsing by Keyword "Robotic ecology"
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Item A cognitive robotic ecology approach to self-configuring and evolving AAL systems(2015-10-01) Dragone, Mauro; Amato, Giuseppe; Bacciu, Davide; Chessa, Stefano; Coleman, Sonya; Di Rocco, Maurizio; Gallicchio, Claudio; Gennaro, Claudio; Lozano, Hector; Maguire, Liam; McGinnity, Martin; Micheli, Alessio; O׳Hare, Gregory M.P.; Renteria, Arantxa; Saffiotti, Alessandro; Vairo, Claudio; Vance, P.; O'Hare, Gregory M.P.; Medical Technologies; Robótica MédicaRobotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user's activities and changing user's habits.Item Robotic Ubiquitous Cognitive Ecology for Smart Homes(2015-12-01) Amato, G.; Bacciu, D.; Broxvall, M.; Chessa, S.; Coleman, S.; Di Rocco, M.; Dragone, M.; Gallicchio, C.; Gennaro, C.; Lozano, H.; McGinnity, T. M.; Micheli, A.; Ray, A. K.; Renteria, A.; Saffiotti, A.; Swords, D.; Vairo, C.; Vance, P.; Medical TechnologiesRobotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.Item Self-sustaining learning for robotic ecologies(2012) Bacciu, D.; Broxvall, M.; Coleman, S.; Dragone, M.; Gallicchio, C.; Gennán, R.; Loparo, C.; Guzmez, R.; Lozano-Peiteado, H.; Ray, A.; Renteria, A.; Saffiotti, A.; Vairo, C.; Medical Technologies; Robótica MédicaThe 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.