Robotic Ubiquitous Cognitive Ecology for Smart Homes

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• 作者：G. Amato ; D. Bacciu ; M. Broxvall ; S. Chessa…
• 关键词：Robotic ecology ; Networked robotics ; Ambient assisted living ; Cognitive robotics ; Wireless sensor and actuator networks ; Home automation ; Activity recognition ; Activity discovery
• 刊名：Journal of Intelligent and Robotic Systems
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：80
• 期：1-supp
• 页码：57-81
• 全文大小：3,400 KB
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• 作者单位：G. Amato (1)
  D. Bacciu (2)
  M. Broxvall (3)
  S. Chessa (2)
  S. Coleman (4)
  M. Di Rocco (3)
  M. Dragone (5)
  C. Gallicchio (2)
  C. Gennaro (1)
  H. Lozano (6)
  T. M. McGinnity (4)
  A. Micheli (2)
  A. K. Ray (4)
  A. Renteria (6)
  A. Saffiotti (3)
  D. Swords (7)
  C. Vairo (1)
  P. Vance (4)
  
  1. ISTI-CNR, Pisa, Italy
  2. Università di Pisa, Pisa, Italy
  3. Örebro Universitet, Stockholm, Sweden
  4. University of Ulster, Ulster, UK
  5. Trinity College Dublin, Dublin, Ireland
  6. Tecnalia, Madrid, Spain
  7. University College Dublin, Dublin, Ireland
  
• 刊物类别：Engineering
• 刊物主题：Automation and Robotics
  Electronic and Computer Engineering
  Artificial Intelligence and Robotics
  Mechanical Engineering
  
• 出版者：Springer Netherlands
• ISSN：1573-0409

文摘

Robotic 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.