25,466 research outputs found
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
A short curriculum of the robotics and technology of computer lab
Our research Lab is directed by Prof. Anton Civit. It is an interdisciplinary group of 23
researchers that carry out their teaching and researching labor at the Escuela
Politécnica Superior (Higher Polytechnic School) and the Escuela de Ingeniería
Informática (Computer Engineering School). The main research fields are: a)
Industrial and mobile Robotics, b) Neuro-inspired processing using electronic spikes,
c) Embedded and real-time systems, d) Parallel and massive processing computer
architecture, d) Information Technologies for rehabilitation, handicapped and elder
people, e) Web accessibility and usability
In this paper, the Lab history is presented and its main publications and research
projects over the last few years are summarized.Nuestro grupo de investigación está liderado por el profesor Civit. Somos un grupo
multidisciplinar de 23 investigadores que realizan su labor docente e investigadora
en la Escuela Politécnica Superior y en Escuela de Ingeniería Informática. Las
principales líneas de investigaciones son: a) Robótica industrial y móvil. b)
Procesamiento neuro-inspirado basado en pulsos electrónicos. c) Sistemas
empotrados y de tiempo real. d) Arquitecturas paralelas y de procesamiento masivo.
e) Tecnología de la información aplicada a la discapacidad, rehabilitación y a las
personas mayores. f) Usabilidad y accesibilidad Web.
En este artículo se reseña la historia del grupo y se resumen las principales
publicaciones y proyectos que ha conseguido en los últimos años
Optimization Beyond the Convolution: Generalizing Spatial Relations with End-to-End Metric Learning
To operate intelligently in domestic environments, robots require the ability
to understand arbitrary spatial relations between objects and to generalize
them to objects of varying sizes and shapes. In this work, we present a novel
end-to-end approach to generalize spatial relations based on distance metric
learning. We train a neural network to transform 3D point clouds of objects to
a metric space that captures the similarity of the depicted spatial relations,
using only geometric models of the objects. Our approach employs gradient-based
optimization to compute object poses in order to imitate an arbitrary target
relation by reducing the distance to it under the learned metric. Our results
based on simulated and real-world experiments show that the proposed method
enables robots to generalize spatial relations to unknown objects over a
continuous spectrum.Comment: Accepted for publication at ICRA2018. Supplementary Video:
http://spatialrelations.cs.uni-freiburg.de
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