3,913 research outputs found
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Towards Odor-Sensitive Mobile Robots
J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012
Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical
ones when operating in real environments. Until now, these sensorial systems mostly relied on range
sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely
been employed, they can provide a complementary sensory information, vital for some applications, as
with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities
and also reviews some of the hurdles that are preventing smell from achieving the importance of other
sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status
on the three main fields within robotics olfaction: the classification of volatile substances, the spatial
estimation of the gas dispersion from sparse measurements, and the localization of the gas source within
a known environment
Scan matching by cross-correlation and differential evolution
Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85
Global Localization based on Evolutionary Optimization Algorithms for Indoor and Underground Environments
Mención Internacional en el título de doctorA fully autonomous robot is defined by its capability to sense, understand and move
within the environment to perform a specific task. These qualities are included within
the concept of navigation. However, among them, a basic transcendent one is localization,
the capacity of the system to know its position regarding its surroundings.
Therefore, the localization issue could be defined as searching the robot’s coordinates
and rotation angles within a known environment. In this thesis, the particular case
of Global Localization is addressed, when no information about the initial position
is known, and the robot relies only on its sensors. This work aims to develop several
tools that allow the system to locate in the two most usual geometric map representations:
occupancy maps and Point Clouds. The former divides the dimensional
space into equally-sized cells coded with a binary value distinguishing between free
and occupied space. Point Clouds define obstacles and environment features as a
sparse set of points in the space, commonly measured through a laser sensor.
In this work, various algorithms are presented to search for that position through
laser measurements only, in contrast with more usual methods that combine external
information with motion information of the robot, odometry. Therefore, the system
is capable of finding its own position in indoor environments, with no necessity of
external positioning and without the influence of the uncertainty that motion sensors
typically induce. Our solution is addressed by implementing various stochastic optimization
algorithms or Meta-heuristics, specifically those bio-inspired or commonly
known as Evolutionary Algorithms. Inspired by natural phenomena, these algorithms
are based on the evolution of a series of particles or population members towards a
solution through the optimization of a cost or fitness function that defines the problem.
The implemented algorithms are Differential Evolution, Particle Swarm Optimization,
and Invasive Weed Optimization, which try to mimic the behavior of evolution
through mutation, the movement of swarms or flocks of animals, and the colonizing
behavior of invasive species of plants respectively. The different implementations
address the necessity to parameterize these algorithms for a wide search space as
a complete three-dimensional map, with exploratory behavior and the convergence
conditions that terminate the search. The process is a recursive optimum estimation search, so the solution is unknown. These implementations address the optimum
localization search procedure by comparing the laser measurements from the real position
with the one obtained from each candidate particle in the known map. The
cost function evaluates this similarity between real and estimated measurements and,
therefore, is the function that defines the problem to optimize.
The common approach in localization or mapping using laser sensors is to establish
the mean square error or the absolute error between laser measurements as an
optimization function. In this work, a different perspective is introduced by benefiting
from statistical distance or divergences, utilized to describe the similarity between
probability distributions. By modeling the laser sensor as a probability distribution
over the measured distance, the algorithm can benefit from the asymmetries provided
by these divergences to favor or penalize different situations. Hence, how the laser
scans differ and not only how much can be evaluated. The results obtained in different
maps, simulated and real, prove that the Global Localization issue is successfully
solved through these methods, both in position and orientation. The implementation
of divergence-based weighted cost functions provides great robustness and accuracy
to the localization filters and optimal response before different sources and noise levels
from sensor measurements, the environment, or the presence of obstacles that are not
registered in the map.Lo que define a un robot completamente autónomo es su capacidad para percibir el entorno,
comprenderlo y poder desplazarse en ´el para realizar las tareas encomendadas.
Estas cualidades se engloban dentro del concepto de la navegación, pero entre todas
ellas la más básica y de la que dependen en buena parte el resto es la localización,
la capacidad del sistema de conocer su posición respecto al entorno que lo rodea. De
esta forma el problema de la localización se podría definir como la búsqueda de las
coordenadas de posición y los ángulos de orientación de un robot móvil dentro de un
entorno conocido. En esta tesis se aborda el caso particular de la localización global,
cuando no existe información inicial alguna y el sistema depende únicamente de sus
sensores. El objetivo de este trabajo es el desarrollo de varias herramientas que permitan
que el sistema encuentre la localización en la que se encuentra respecto a los
dos tipos de mapa más comúnmente utilizados para representar el entorno: los mapas
de ocupación y las nubes de puntos. Los primeros subdividen el espacio en celdas
de igual tamaño cuyo valor se define de forma binaria entre espacio libre y ocupado.
Las nubes de puntos definen los obstáculos como una serie dispersa de puntos en el
espacio comúnmente medidos a través de un láser.
En este trabajo se presentan varios algoritmos para la búsqueda de esa posición utilizando únicamente las medidas de este sensor láser, en contraste con los métodos más
habituales que combinan información externa con información propia del movimiento
del robot, la odometría. De esta forma el sistema es capaz de encontrar su posición
en entornos interiores sin depender de posicionamiento externo y sin verse influenciado
por la deriva típica que inducen los sensores de movimiento. La solución se
afronta mediante la implementación de varios tipos de algoritmos estocásticos de optimización o Meta-heurísticas, en concreto entre los denominados bio-inspirados o
comúnmente conocidos como Algoritmos Evolutivos. Estos algoritmos, inspirados en
varios fenómenos de la naturaleza, se basan en la evolución de una serie de partículas
o población hacia una solución en base a la optimización de una función de coste que
define el problema.
Los algoritmos implementados en este trabajo son Differential Evolution, Particle
Swarm Optimization e Invasive Weed Optimization, que tratan de imitar el comportamiento
de la evolución por mutación, el movimiento de enjambres o bandas de animales y la colonización por parte de especies invasivas de plantas respectivamente.
Las distintas implementaciones abordan la necesidad de parametrizar estos algoritmos
para un espacio de búsqueda muy amplio como es un mapa completo, con la
necesidad de que su comportamiento sea muy exploratorio, así como las condiciones
de convergencia que definen el fin de la búsqueda ya que al ser un proceso recursivo
de estimación la solución no es conocida. Estos algoritmos plantean la forma de
buscar la localización ´optima del robot mediante la comparación de las medidas del
láser en la posición real con lo esperado en la posición de cada una de esas partículas
teniendo en cuenta el mapa conocido. La función de coste evalúa esa semejanza entre
las medidas reales y estimadas y por tanto, es la función que define el problema.
Las funciones típicamente utilizadas tanto en mapeado como localización mediante
el uso de sensores láser de distancia son el error cuadrático medio o el error
absoluto entre distancia estimada y real. En este trabajo se presenta una perspectiva
diferente, aprovechando las distancias estadísticas o divergencias, utilizadas para
establecer la semejanza entre distribuciones probabilísticas. Modelando el sensor
como una distribución de probabilidad entorno a la medida aportada por el láser, se
puede aprovechar la asimetría de esas divergencias para favorecer o penalizar distintas
situaciones. De esta forma se evalúa como difieren las medias y no solo cuanto. Los
resultados obtenidos en distintos mapas tanto simulados como reales demuestran que
el problema de la localización se resuelve con éxito mediante estos métodos tanto respecto
al error de estimación de la posición como de la orientación del robot. El uso de
las divergencias y su implementación en una función de coste ponderada proporciona
gran robustez y precisión al filtro de localización y gran respuesta ante diferentes
fuentes y niveles de ruido, tanto de la propia medida del sensor, del ambiente y de
obstáculos no modelados en el mapa del entorno.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Fabio Bonsignorio.- Secretario: María Dolores Blanco Rojas.- Vocal: Alberto Brunete Gonzále
Differential evolution Markov chain filter for global localization
A key challenge for an autonomous mobile robot is to estimate its location according to the available information. A particular aspect of this task is the global localization problem. In our previous work, we developed an algorithm based on the Differential Evolution method that solves this problem in 2D and 3D environments. The robot’s pose is represented by a set of possible location estimates weighted by a fitness function. The Markov Chain Monte Carlo algorithms have been successfully applied to multiple fields such as econometrics or computing science. It has been demonstrated that they can be combined with the Differential Evolution method to solve efficiently many optimization problems. In this work, we have combined both approaches to develop a global localization filter. The algorithm performance has been tested in simulated and real maps. The population requirements have been reduced when compared to the previous version.The research leading to these results has received funding from the RoboCity2030-III-CM project (Robotica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU.Publicad
Global localization based on a rejection differential evolution filter
Autonomous systems are able to move from one point to another in a given environment because they can solve two basic problems: the localization problem and the navigation problem. The localization purpose is to determine the current pose of the autonomous robot or system and the navigation purpose is to find out a feasible path from the current pose to the goal point that avoids any obstacle present in the environment. Obviously, without a reliable localization system it is not possible to solve the navigation problem. Both problems are among the oldest problems in human travels and have motivated a considerable amount of technological advances in human history. They are also present in robot motion around the environment and have also motivated a considerable research effort to solve them in an efficient way
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