3,193 research outputs found
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
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A new genetic algorithm approach to smooth path planning for mobile robots
Purpose-The purpose of this paper is to consider the smooth path planning problem for a mobile robot based on the genetic algorithm (GA) and the Bezier curve. Design/methodology/approach-The workspace of a mobile robot is described by a new grid-based representation that facilitates the operations of the adopted GA. The chromosome of the GA is composed of a sequence of binary numbered grids (i.e. control points of the Bezier curve). Ordinary genetic operators including crossover and mutation are used to search the optimum chromosome where the optimization criterion is the length of a piecewise collision-free Bezier curve path determined by the control points. Findings-This paper has proposed a new smooth path planning for a mobile robot by resorting to the GA and the Bezier curve. A new grid-based representation of the workspace has been presented, which makes it convenient to perform operations in the GA. The GA has been used to search the optimum control points that determine the Bezier curve-based smooth path. The effectiveness of the proposed approach has been verified by a numerical experiment, and some performances of the obtained method have also been analyzed. Research limitations/implications-There still remain many interesting topics, for example, how to solve the specific smooth path planning problem by using the GA and how to promote the computational efficiency in the more grids case. These issues deserve further research. Originality/value-The purpose of this paper is to improve the existing results by making the following three distinctive contributions: a rigorous mathematical formulation of the path planning optimization problem is formulated; a general grid-based representation (2n × 2n) is proposed to describe the workspace of the mobile robots to facilitate the implementation of the GA where n is chosen according to the trade-off between the accuracy and the computational burden; and the control points of the Bezier curve are directly linked to the optimization criteria so that the generated paths are guaranteed to be optimal without any need for smoothing afterwards.This work was supported in part by the Research Fund for the Taishan Scholar Project of Shandong Province of China and the Higher Educational Science and Technology Program of Shandong Province of China under Grant J14LN34
Global Localization based on Evolutionary Optimization Algorithms for Indoor and Underground Environments
Mención Internacional en el tÃtulo 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 podrÃa 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 odometrÃa. 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 tÃpica que inducen los sensores de movimiento. La solución se
afronta mediante la implementación de varios tipos de algoritmos estocásticos de optimización o Meta-heurÃsticas, 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 partÃculas
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, asà 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 partÃculas
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 tÃpicamente 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 estadÃsticas o divergencias, utilizadas para
establecer la semejanza entre distribuciones probabilÃsticas. Modelando el sensor
como una distribución de probabilidad entorno a la medida aportada por el láser, se
puede aprovechar la asimetrÃa 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 IngenierÃa Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Fabio Bonsignorio.- Secretario: MarÃa Dolores Blanco Rojas.- Vocal: Alberto Brunete Gonzále
Design and Performance Analysis of Genetic Algorithms for Topology Control Problems
In this dissertation, we present a bio-inspired decentralized topology control mechanism, called force-based genetic algorithm (FGA), where a genetic algorithm (GA) is run by each autonomous mobile node to achieve a uniform spread of mobile nodes and to provide a fully connected network over an unknown area. We present a formal analysis of FGA in terms of convergence speed, uniformity at area coverage, and Lyapunov stability theorem.
This dissertation emphasizes the use of mobile nodes to achieve a uniform distribution over an unknown terrain without a priori information and a central control unit. In contrast, each mobile node running our FGA has to make its own movement direction and speed decisions based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge.
We have implemented simulation software in Java and developed four different testbeds to study the effectiveness of different GA-based topology control frameworks for network performance metrics including node density, speed, and the number of generations that GAs run.
The stochastic behavior of FGA, like all GA-based approaches, makes it difficult to analyze its convergence speed. We built metrically transitive homogeneous and inhomogeneous Markov chain models to analyze the convergence of our FGA with respect to the communication ranges of mobile nodes and the total number of nodes in the system. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for homogeneous and inhomogeneous Markov chain models of our FGA. Furthermore, convergence characteristic analysis helps us to choose the nearoptimal values for communication range, the number of mobile nodes, and the mean node degree before sending autonomous mobile nodes to any mission.
Our analytical and experimental results show that our FGA delivers promising results for uniform mobile node distribution over unknown terrains. Since our FGA adapts to local environment rapidly and does not require global network knowledge, it can be used as a real-time topology controller for commercial and military applications
Cellular Automata Applications in Shortest Path Problem
Cellular Automata (CAs) are computational models that can capture the
essential features of systems in which global behavior emerges from the
collective effect of simple components, which interact locally. During the last
decades, CAs have been extensively used for mimicking several natural processes
and systems to find fine solutions in many complex hard to solve computer
science and engineering problems. Among them, the shortest path problem is one
of the most pronounced and highly studied problems that scientists have been
trying to tackle by using a plethora of methodologies and even unconventional
approaches. The proposed solutions are mainly justified by their ability to
provide a correct solution in a better time complexity than the renowned
Dijkstra's algorithm. Although there is a wide variety regarding the
algorithmic complexity of the algorithms suggested, spanning from simplistic
graph traversal algorithms to complex nature inspired and bio-mimicking
algorithms, in this chapter we focus on the successful application of CAs to
shortest path problem as found in various diverse disciplines like computer
science, swarm robotics, computer networks, decision science and biomimicking
of biological organisms' behaviour. In particular, an introduction on the first
CA-based algorithm tackling the shortest path problem is provided in detail.
After the short presentation of shortest path algorithms arriving from the
relaxization of the CAs principles, the application of the CA-based shortest
path definition on the coordinated motion of swarm robotics is also introduced.
Moreover, the CA based application of shortest path finding in computer
networks is presented in brief. Finally, a CA that models exactly the behavior
of a biological organism, namely the Physarum's behavior, finding the
minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From
software to wetware. Springer, 201
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