This paper proposes an integrated path following and obstacle avoidance framework for robots in crowded environments. The architecture considers two major requirements: (i) a given performance level for path following must be guaranteed; (ii) human-friendly robots must behave predictably and naturally, in order to avoid dangerous people reactions. In order to generate paths perceived as natural, and inspired by what happens in high traffic highways, we assume a path to consist of a family of curves, which can be switched using sensory information. The resulting behaviour is both predictable (paths are planned and validated beforehand) and natural (the overall qualitative behaviour is maintained when switching curves). The overall error is upper bounded by the curves configuration. The paper presents results in simulation.

This paper describes a system for context awareness in smart environments, which is based on an ontology expressed in description logic and implemented in OWL 2 EL, which is a subset of the Web Ontology Language that allows for reasoning in polynomial time. The approach is different from all other works in the literature since the proposed system requires only the basic reasoning mechanisms of description logic, i.e., subsumption and instance checking, without any additional external reasoning engine. Experiments performed with data collected in three different scenarios are described, i.e., the CASAS Project at Washington State University, the assisted living facility Villa Basilea in Genoa, and the Merry Porter mobile robot at the Polyclinic of Modena.

The article describes a system for describing and recognizing spatial and temporal patterns of events. The system is based on an ontology described through the Description Logics formalism and implemented in OWL DL. The approach is different from all other works in the literature since the system does not require an external reasoning engine, but relies only on the base mechanism for ontology classification. Experiments performed in two different scenarios are described, i.e., a Smart Home and a mobile robot for autonomous transportation operating within a partially automated building.

Humanoid robots operating in the real world must exhibit very complex behaviors, such as object manipulation or interaction with people. Such capabilities pose the problem of being able to reason on a huge number of different objects, places and actions to carry out, each one relevant for achieving robot goals. This article proposes a functional representation of objects, places and actions described in terms of affordances and capabilities. Everyday problems can be efficiently dealt with by decomposing the reasoning process in two phases, namely problem awareness (which is the focus of this article) and action selection.

The focus of the present work is on daily activity planning, i.e., representations and algorithms able to produce a course of action to deal efficiently with problems of daily living. To achieve this, the article proposes a functional representation of everyday objects, places and actions described in terms of affordances. Its contributions are two--fold: (i) it proposes to represent affordances and capabilities as regions in a proper affordance and capability space, and to describe such regions using neural maps; (ii) it proposes to decompose the planning process into different activities, by introducing a phase referred to as Problem Awareness preceding Action Planning, which allows to reduce the planning space in order to tackle large-scale planning problems.

A knowledge representation system is introduced that allows the recognition of temporal patterns of events in context-aware environments. The system is based on standard frameworks, such as an ontology, an inference mechanism, and relational operators acting on numerical quantities. The paper describes how knowledge is managed, then introduces a collection of temporal operators that are inspired by the Allen's interval algebra, and then details a situation recognition algorithm to assess knowledge semantics. An example is reported to describe the approach.

A framework is introduced that is aimed at integrating ontology and logic approaches for context-awareness, suitable for use in Ambient Intelligence (AmI) scenarios. In particular, the context description language CDL is described, which allows to easily specify patterns of events which occurrences must be monitored by actual systems. As long as systems evolve, symbolic data originating from heterogeneous sources are first aggregated and then classified according to formulas described in CDL. Experimental results performed in a Smart Home environment are presented and discussed.