6,748 research outputs found
A Vision-based Scheme for Kinematic Model Construction of Re-configurable Modular Robots
Re-configurable modular robotic (RMR) systems are advantageous for their
reconfigurability and versatility. A new modular robot can be built for a
specific task by using modules as building blocks. However, constructing a
kinematic model for a newly conceived robot requires significant work. Due to
the finite size of module-types, models of all module-types can be built
individually and stored in a database beforehand. With this priori knowledge,
the model construction process can be automated by detecting the modules and
their corresponding interconnections. Previous literature proposed theoretical
frameworks for constructing kinematic models of modular robots, assuming that
such information was known a priori. While well-devised mechanisms and built-in
sensors can be employed to detect these parameters automatically, they
significantly complicate the module design and thus are expensive. In this
paper, we propose a vision-based method to identify kinematic chains and
automatically construct robot models for modular robots. Each module is affixed
with augmented reality (AR) tags that are encoded with unique IDs. An image of
a modular robot is taken and the detected modules are recognized by querying a
database that maintains all module information. The poses of detected modules
are used to compute: (i) the connection between modules and (ii) joint angles
of joint-modules. Finally, the robot serial-link chain is identified and the
kinematic model constructed and visualized. Our experimental results validate
the effectiveness of our approach. While implementation with only our RMR is
shown, our method can be applied to other RMRs where self-identification is not
possible
Technology Integration around the Geographic Information: A State of the Art
One of the elements that have popularized and facilitated the use of geographical information on a variety of computational applications has been the use of Web maps; this has opened new research challenges on different subjects, from locating places and people, the study of social behavior or the analyzing of the hidden structures of the terms used in a natural language query used for locating a place. However, the use of geographic information under technological features is not new, instead it has been part of a development and technological integration process. This paper presents a state of the art review about the application of geographic information under different approaches: its use on location based services, the collaborative user participation on it, its contextual-awareness, its use in the Semantic Web and the challenges of its use in natural languge queries. Finally, a prototype that integrates most of these areas is presented
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
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Geovisualization of dynamics, movement and change: key issues and developing approaches in visualization research
Service-oriented Context-aware Framework
Location- and context-aware services are emerging technologies in mobile and
desktop environments, however, most of them are difficult to use and do not
seem to be beneficial enough. Our research focuses on designing and creating a
service-oriented framework that helps location- and context-aware,
client-service type application development and use. Location information is
combined with other contexts such as the users' history, preferences and
disabilities. The framework also handles the spatial model of the environment
(e.g. map of a room or a building) as a context. The framework is built on a
semantic backend where the ontologies are represented using the OWL description
language. The use of ontologies enables the framework to run inference tasks
and to easily adapt to new context types. The framework contains a
compatibility layer for positioning devices, which hides the technical
differences of positioning technologies and enables the combination of location
data of various sources
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