50 research outputs found
A multi-hierarchical symbolic model of the environment for improving mobile robot operation
El trabajo desarrollado en esta tesis se centra en el estudio y aplicación de estructuras multijerárquicas, que representan el entorno de un robot móvil, con el objetivo de mejorar su capacidad de realizar tareas complejas en escenarios humanos.
Un robot móvil debe poseer una representación simbólica de su entorno para poder llevar a cabo operaciones deliberativas, por ejemplo planificar tareas. Sin embargo a la hora de representar simbólicamente entornos reales, dado su complejidad, es imprescindible contar con mecanismos capaces de organizar y facilitar el acceso a la ingente cantidad de información que de ellos se deriva.
Aparte del inconveniente de tratar con grandes cantidades de información, existen otros problemas subyacentes de la representación simbólica de entornos reales, los cuales aún no han sido resueltos por completo en la literatura científica. Uno de ellos consiste en el mantenimiento de la representación simbólica optimizada con respecto a las tareas que el robot debe realizar, y coherente con el entorno en el que se desenvuelve. Otro problema, relacionado con el anterior es la creación/modificación de la información simbólica a partir de información meramente sensorial (este problema es conocido como symbol-grounding).
Esta tesis estudia estos problemas y aporta soluciones mediante estructuras multijerárquicas. Estas estructuras simbólicas, basadas en el concepto de abstracción, imitan la forma en la que los humanos organizamos la información espacial y permite a un robot móvil mejorar sus habilidades en entornos complejos. Las principales contribuciones de este trabajo son:
- Se ha formalizado matemáticamente un modelo simbólico basado en múltiples abstracciones (multijerarquías) mediante Teoría de Categorías.
Se ha desarrollado un planificador de tareas eficiente que es capaz de aprovechar la organización jerárquica del modelo simbólico del entorno. Nuestro método ha sido validado matemáticamente y se han implementado y comparado dos variantes del mismo (HPWA-1 y HPWA-2).
- Una instancia particular del modelo multijerárquico ha sido estudiada e implementada para organizar información simbólica con el objetivo de mejorar simultáneamente diferentes tareas a realizar por un robot móvil.
- Se ha desarrollado un procedimiento que (1) construye un modelo jerárquico del entorno de un robot, (2) lo mantiene coherente y actualizado y (3) lo optimiza con el fin de mejorar las tareas realizadas por un robot móvil.
- Finalmente, se ha implementado una arquitectura robótica que engloba todas las cuestiones anteriormente citadas. Se han realizado pruebas reales con una silla de ruedas robotizada que ponen de manifiesto la utilidad del uso de estructuras multijerárquicas en robótica móvil
An assisted navigation method for telepresence robots
Telepresence robots have emerged as a new means of interaction in remote
environments. However, the use of such robots is still limited due to safety
and usability issues when operating in human-like environments. This work addresses
these issues by enhancing the robot navigation through a collaborative
control method that assists the user to negotiate obstacles. The method has been
implemented in a commercial telepresence robot and a user study has been conducted
in order to test the suitability of our approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Enhancing smart environments with mobile robots
Sensor networks are becoming popular nowadays in the development of smart environments. Heavily relying on static sensor and actuators, though, such environments usually lacks of versatility regarding the provided services and interaction capabilities. Here we present a framework for smart environments where a service robot is included within the sensor network acting as a mobile sensor and/or actuator. Our framework integrates on-the-shelf technologies to ensure its adaptability to a variety of sensor technologies and robotic software. Two pilot cases
are presented as evaluation of our proposal.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
OLT: A Toolkit for Object Labeling Applied to Robotic RGB-D Datasets
In this work we present the Object Labeling Toolkit
(OLT), a set of software components publicly available for
helping in the management and labeling of sequential RGB-D
observations collected by a mobile robot. Such a robot can be
equipped with an arbitrary number of RGB-D devices, possibly
integrating other sensors (e.g. odometry, 2D laser scanners,
etc.). OLT first merges the robot observations to generate a
3D reconstruction of the scene from which object segmentation
and labeling is conveniently accomplished. The annotated labels
are automatically propagated by the toolkit to each RGB-D
observation in the collected sequence, providing a dense labeling
of both intensity and depth images. The resulting objects’ labels
can be exploited for many robotic oriented applications, including
high-level decision making, semantic mapping, or contextual
object recognition. Software components within OLT are highly
customizable and expandable, facilitating the integration of
already-developed algorithms. To illustrate the toolkit suitability,
we describe its application to robotic RGB-D sequences taken in
a home environment.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Spanish grant pro-
gram FPU-MICINN 2010 and the Spanish projects TAROTH:
New developments toward a Robot at Home (DPI2011-25483)
and PROMOVE: Advances in mobile robotics for promoting
independent life of elders (DPI2014-55826-R
Probability and Common-Sense: Tandem Towards Robust Robotic Object Recognition in Ambient Assisted Living
The suitable operation of mobile robots when providing Ambient Assisted Living (AAL) services calls for robust object recognition capabilities. Probabilistic Graphical Models (PGMs) have become the de-facto choice in recognition systems aiming to e ciently exploit contextual relations among objects, also dealing with the uncertainty inherent to the robot workspace. However, these models can perform in an inco herent way when operating in a long-term fashion out of the laboratory, e.g. while recognizing objects in peculiar con gurations or belonging to new types. In this work we propose a recognition system that resorts to PGMs and common-sense knowledge, represented in the form of an ontology, to detect those inconsistencies and learn from them. The utilization of the ontology carries additional advantages, e.g. the possibility to verbalize the robot's knowledge. A primary demonstration of the system capabilities has been carried out with very promising results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Experiences on a motivational learning approach for robotics in undergraduate courses
This paper presents an educational experience carried out in robotics undergraduate courses from two
different degrees: Computer Science and Industrial Engineering, having students with diverse
capabilities and motivations. The experience compares two learning strategies for the practical
lessons of such courses: one relies on code snippets in Matlab to cope with typical robotic problems
like robot motion, localization, and mapping, while the second strategy opts for using the ROS
framework for the development of algorithms facing a competitive challenge, e.g. exploration
algorithms. The obtained students’ opinions were instructive, reporting, for example, that although they
consider harder to master ROS when compared to Matlab, it might be more useful in their (robotic
related) professional careers, which enhanced their disposition to study it. They also considered that
the challenge-exercises, in addition to motivate them, helped to develop their skills as engineers to a
greater extent than the skeleton-code based ones. These and other conclusions will be useful in
posterior courses to boost the interest and motivation of the students.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
UPGMpp: a Software Library for Contextual Object Recognition
Object recognition is a cornerstone task towards the scene
understanding problem. Recent works in the field boost their perfor-
mance by incorporating contextual information to the traditional use
of the objects’ geometry and/or appearance. These contextual cues are
usually modeled through Conditional Random Fields (CRFs), a partic-
ular type of undirected Probabilistic Graphical Model (PGM), and are
exploited by means of probabilistic inference methods. In this work we
present the Undirected Probabilistic Graphical Models in C++ library
(UPGMpp), an open source solution for representing, training, and per-
forming inference over undirected PGMs in general, and CRFs in par-
ticular. The UPGMpp library supposes a reliable and comprehensive
workbench for recognition systems exploiting contextual information, in-
cluding a variety of inference methods based on local search, graph cuts,
and message passing approaches. This paper illustrates the virtues of the
library, i.e. it is efficient, comprehensive, versatile, and easy to use, by
presenting a use-case applied to the object recognition problem in home
scenes from the challenging NYU2 dataset.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Spanish grant program FPU-MICINN 2010
and the Spanish projects “TAROTH: New developments toward a robot at
home” (Ref. DPI2011-25483) and “PROMOVE: Advances in mobile robotics
for promoting independent life of elders” (Ref. DPI2014-55826-R
Online Context-based Object Recognition for Mobile Robots
This work proposes a robotic object recognition
system that takes advantage of the contextual information latent
in human-like environments in an online fashion. To fully leverage
context, it is needed perceptual information from (at least) a
portion of the scene containing the objects of interest, which could
not be entirely covered by just an one-shot sensor observation.
Information from a larger portion of the scenario could still
be considered by progressively registering observations, but this
approach experiences difficulties under some circumstances, e.g.
limited and heavily demanded computational resources, dynamic
environments, etc. Instead of this, the proposed recognition
system relies on an anchoring process for the fast registration
and propagation of objects’ features and locations beyond the
current sensor frustum. In this way, the system builds a graphbased
world model containing the objects in the scenario (both
in the current and previously perceived shots), which is exploited
by a Probabilistic Graphical Model (PGM) in order to leverage
contextual information during recognition. We also propose a
novel way to include the outcome of local object recognition
methods in the PGM, which results in a decrease in the usually
high CRF learning complexity. A demonstration of our proposal
has been conducted employing a dataset captured by a mobile
robot from restaurant-like settings, showing promising results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Improvement of the sensory and autonomous capability of robots through olfaction: the IRO Project
Proyecto de Excelencia Junta de Andalucía TEP2012-530Olfaction is a valuable source of information about the environment that has not been su ciently exploited in mobile robotics
yet. Certainly, odor information can contribute to other sensing modalities, e.g. vision, to successfully accomplish high-level robot
activities, such as task planning or execution in human environments. This paper describes the developments carried out in the scope of the IRO project, which aims at making progress in this direction by investigating mechanisms that exploit odor information (usually coming in the form of the type of volatile and its concentration) in problems like object recognition and scene-activity understanding. A distinctive aspect of this research is the special attention paid to the role of semantics within the robot perception and decisionmaking processes. The results of the IRO project have improved the robot capabilities in terms of efciency, autonomy and usefulness.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec
Evaluation of Using Semi-Autonomy Features in Mobile Robotic Telepresence Systems
Mobile robotic telepresence systems used for social interaction scenarios require that users steer robots in a remote environment. As a consequence, a heavy workload can be put on users if they are unfamiliar with using robotic telepresence units. One way to lessen this workload is to automate certain operations performed during a telepresence session in order to assist remote drivers in navigating the robot in new environments. Such operations include autonomous robot localization and navigation to certain points in the home and automatic docking of the robot to the charging station. In this paper we describe the implementation of such autonomous features along with user evaluation study. The evaluation scenario is focused on the first experience on using the system by novice users. Importantly, that the scenario taken in this study assumed that participants have as little as possible prior information about the system. Four different use-cases were identified from the user behaviour analysis.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Plan Nacional de Investigación, proyecto DPI2011-25483