4,113 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
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
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
Application of a mobile robot to spatial mapping of radioactive substances in indoor environment
Nuclear medicine requires the use of radioactive substances that can contaminate critical
areas (dangerous or hazardous) where the presence of a human must be reduced or avoided.
The present work uses a mobile robot in real environment and 3D simulation to develop
a method to realize spatial mapping of radioactive substances. The robot should visit all
the waypoints arranged in a grid of connectivity that represents the environment. The
work presents the methodology to perform the path planning, control and estimation
of the robot location. For path planning two methods are approached, one a heuristic
method based on observation of problem and another one was carried out an adaptation
in the operations of the genetic algorithm. The control of the actuators was based on two
methodologies, being the first to follow points and the second to follow trajectories. To
locate the real mobile robot, the extended Kalman filter was used to fuse an ultra-wide
band sensor with odometry, thus estimating the position and orientation of the mobile
agent. The validation of the obtained results occurred using a low cost system with a
laser range finder.A medicina nuclear requer o uso de substâncias radioativas que pode vir a contaminar
áreas críticas, onde a presença de um ser humano deve ser reduzida ou evitada. O presente
trabalho utiliza um robô móvel em ambiente real e em simulação 3D para desenvolver um
método para o mapeamento espacial de substâncias radioativas. O robô deve visitar todos
os waypoinst dispostos em uma grelha de conectividade que representa o ambiente. O trabalho
apresenta a metodologia para realizar o planejamento de rota, controle e estimação
da localização do robô. Para o planejamento de rota são abordados dois métodos, um
baseado na heurística ao observar o problema e ou outro foi realizado uma adaptação nas
operações do algoritmo genético. O controle dos atuadores foi baseado em duas metodologias,
sendo a primeira para seguir de pontos e a segunda seguir trajetórias. Para localizar
o robô móvel real foi utilizado o filtro de Kalman extendido para a fusão entre um sensor
ultra-wide band e odometria, estimando assim a posição e orientação do agente móvel. A
validação dos resultados obtidos ocorreu utilizando um sistema de baixo custo com um
laser range finder
Terrain classification for a quadruped robot
Using data retrieved from the Puppy II robot at the University of Zurich (UZH), we show that machine learning techniques with non-linearities and fading memory are effective for terrain classification, both supervised and unsupervised, even with a limited selection of input sensors. The results indicate that most information for terrain classification is found in the combination of tactile sensors and proprioceptive joint angle sensors. The classification error is small enough to have a robot adapt the gait to the terrain and hence move more robustly
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