Robotic ubiquitous cognitive ecology for smart homes

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 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 feed- back 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

Digital representation of everyday objects in a robot ecology via proxies

Abstract — Robotic middlewares increasingly allow the seamless integration of multiple heterogeneous robots into one distributed system. Unfortunately, very simple devices like tagged everyday objects and smart objects are left orphan in this otherwise pervasive trend. We claim that the inclusion of simple everyday objects as part of distributed robot systems would have many advantages, and propose a design pattern to allow this inclusion. We make this pattern concrete by describing an implementation of it using a specific multi-robot middleware, called PEIS-Ecology Middleware. We also show an illustrative experiment which integrates everyday objects in a smart home equipped with mobile robots as well as more advanced distributed sensor nodes. I

Dependency calculus: Reasoning in a general point relation algebra

Reasoning about complex dependencies between events is a crucial task. However, qualitative reasoning has so far concentrated on spatial and temporal issues. In contrast, we present a new dependency calculus (DC) that is created for specific questions of reasoning about causal relations and consequences. Applications in the field of spatial representation and reasoning are, for instance, modeling traffic networks, ecological systems, medical diagnostics, and Bayesian Networks. Several extensions of the fundamental linear point algebra have been investigated, for instance on trees or on nonlinear structures. DC is an improved generalization that meets all requirements to describe dependencies on networks. We investigate this structure with respect to satisfiability problems, construction problems, tractable subclassses, and embeddings into other relation algebras. Finally, we analyze the associated interval algebra on network structures

CASL specifications of qualitative calculi

Abstract. In AI a large number of calculi for efficient reasoning about spatial and temporal entities have been developed. The most prominent temporal calculi are the point algebra of linear time and Allen’s interval calculus. Examples of spatial calculi include mereotopological calculi, Frank’s cardinal direction calculus, Freksa’s double cross calculus, Egenhofer and Franzosa’s intersection calculi, and Randell, Cui, and Cohn’s region connection calculi. These calculi are designed for modeling specific aspects of space or time, respectively, to the effect that the class of intended models may vary widely with the calculus at hand. But from a formal point of view these calculi are often closely related to each other. For example, the spatial region connection calculus RCC5 may be considered a coarsening of Allen’s (temporal) interval calculus. And vice versa, intervals can be used to represent spatial objects that feature an internal direction. The central question of this paper is how these calculi as well as their mutual dependencies can be axiomatized by algebraic specifications. This question will be investigated within the framework of the Common Algebraic Specification Language (CASL), a specification language developed by the Common Framework Initiative for algebraic specification and development (COFI). We explain scope and expressiveness of CASL by discussing the specifications of some of the calculi mentioned before.

The PEIS-Ecology Project: Vision and Results

Abstract — The vision of an Ecology of Physically Embedded Intelligent 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. I