added 2 research items
In an ambient assisted living environment, raw data can often be very noisy making is difficulty to interrupt by a decision and reasoning system. To help reduce the effects of noise, we propose a decision and reasoning system which combines an interval fuzzy system and a self-organising fuzzy neural network (SOFNN) is presented in this paper. The method exploits the use of a trained standard SOFNN structure from a fuzzy neural network to initialise the proposed approach. Simulation results show that the proposed structure is more suitable for uncertain situations demonstrating a high level of robustness.
Robotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and e�ectors 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, exible, manageable and dependable Ambient Assisted Living (AAL) solutions. Speci�cally, 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
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.
Current approaches to networked robot systems (or ecology of robots and sensors) in ambient assisted living applications (AAL) rely on pre-programmed models of the environment and do not evolve to address novel states of the environment. Envisaged as part of a robotic ecology in an AAL environment to provide different services based on the events and user activities, a Markov based approach to establishing a user behavioural model through the use of a cognitive memory module is presented in this paper. Upon detecting changes in the normal user behavioural pattern, the ecology tries to adapt its response to these changes in an intelligent manner. The approach is evaluated with physical robots and an experimental evaluation is presented in this paper. A major challenge associated with data storage in a sensor rich environment is the expanding memory requirements. In order to address this, a bio-inspired data retention strategy is also proposed. These contributions can enable a robotic ecology to adapt to evolving environmental states while efficiently managing the memory footprint.