Abstract-The vision of an Ecology of Physically EmbeddedIntelligent Systems, or PEIS-Ecology, combines insights from the fields of autonomous robotics and ambient intelligence to provide a new approach to building robotic systems in the service of people. In this paper, we present this vision, and we report the results of a four-year collaborative research project between Sweden and Korea aimed at the concrete realization of this vision. We focus in particular on three results: a robotic middleware able to cope with highly heterogeneous systems; a technique for autonomous self-configuration and reconfiguration; and a study of the problem of sharing information of both physical and digital nature.

-

No abstract

For mobile robots operating in real-world environments, reactive navigation is a useful complement (or even replacement) to pure plan-based metric navigation. Reactive navigation is performed with respect to local perceived features, rather than a global metric reference frame, and can provide reduced installation costs, increased flexibility, and robustness to changes in the environment. To be effective, however, reactive navigation requires fast and reliable perception of the relevant features in the environment. Corridor-like structures are one of the most common features that are used for this purpose. In this paper, we propose a new method for corridor detection from laser data, based on the Hough transform, which is fast, reliable, and noise tolerant. We describe the algorithm, report an extensive experimental evaluation of its performance, and motivate the research with a real application involving the autonomous operation of a loader vehicle in an underground mine.

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 agentbased 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 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.