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1

On Semantic Support System for USAR operations with mobile robots

Musialik P.

Challenges of Modern Technology

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2014

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Vol. 5, no. 2

3--8

EN

This paper describes the design and the basic implementation of a Semantic Support System (SSS) for Urban Search and Rescue (USAR) operations. The system is intended for use by rescue teams equipped with mobile robots. The goal of the work is to provide the rescue team with a global operational picture that ensures proper information availability. The core of the system is a semantic model of the environment based on Qualitative Spatio-Temporal Representation and Reasoning (QSTRR)[1] framework. Thus, the ontology of the semantic model is described. The ontology is based on Humanitarian Data Model enhanced by the robot`s information. SSS is designed to work with multiple data sources: Geographic Information System (GIS), 3D point clouds, camera images etc. The article describes,as an example, the results of building the semantic model from 3D point clouds. An example of qualitative reasoning based on the semantic model is shown.

2

Qualitative Spatio-Temporal Representation and Reasoning for robotic applications

Będkowski J.

Pomiary Automatyka Robotyka

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2013

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R. 17, nr 2

300-303

EN

This paper discusses the methodology of Qualitative Spatio-Temporal Representation and Reasoning (QSTRR) for robotic applications. The goal is to develop reasoning mechanism that will allow modelling the environment and performing spatiotemporal decisions. A new approach is related to environment modelling based on robot’s perception, therefore new concepts (spatial entities) are obtained automatically, and then used in reasoning. This paper presents the results of the three experiments. Each experiment focuses on different robotic applications, such as mobile spatial assistive intelligence for spatial design, spatial design used for robotic arm integration with the environment and supervision of a teleoperated robot. Each of the experiments is considered as the proof of concept of the proposed methodology. Thus, it can be efficiently used for developing sophisticated robotic application where human-robot interaction and integration are considered as an important goal.

PL

W artykule przedstawiono metodykę jakościowej przestrzenno-czasowej reprezentacji oraz rozumowania dla aplikacji robotycznych. Celem jest opracowanie mechanizmu podejmowania decyzji, które umożliwi także modelowanie środowiska oraz rozumowanie w sensie jakościowym. Nowym zagadnieniem badawczym jest budowanie modelu środowiska na bazie obserwacji robota mobilnego, przy czym nowe koncepty (podstawowe elementy przestrzenne) są generowane automatycznie. Przedstawiono trzy eksperymenty, każdy skojarzony z inną aplikacją robotyczną, między innymi mobilna przestrzenna inteligencja asystująca dla projektowania przestrzennego, projektowanie przestrzenne dla integracji robota przemysłowego z istniejącym środowiskiem pracy oraz nadzorowanie pracy robota zdalnie sterowanego. Każdy z tych eksperymentów dowodzi słuszności proponowanej metodyki. W związku z tym metodyka może znaleźć zastosowanie w projektowaniu zaawansowanych aplikacji robotycznych, gdzie interakcja oraz integracja człowiek-robot są zasadniczym zagadnieniem funkcjonalnym oraz użytkowym.

3

A qualitative trajectory calculus as a basis for representing moving objects in Geographical Information Systems

Weghe N. van de, Cohn A. G., Tre G. de, Maeyer P. de

Control and Cybernetics

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2006

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Vol. 35, no 1

97-119

EN

Qualitative formalisms, suited to express qualitative temporal or spatial relationships between entities, have gained wide acceptance as a useful way of abstracting from the real world. The question remains how to describe spatio-temporal concepts, such as the interaction between disconnected moving objects, adequately within a qualitative calculus and more specifically how to use this in geographical information systems. In this paper, the Basic Qualitative Trajectory Calculus (QTCB) for representing and reasoning about moving objects is presented. QTCB enables comparisons between positions of objects at different time points ro be made. The calculus is based on few primitives (i.e., distance and speed constraints), making it elegant and theoretically simple. To clarify the way in which trajectories are represented within QTCB: specific cases of movements (e.g. circular movement) are presented. To illustrate the naturalness of QTC, a "predator-prey" example is studied.