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

Kullback-Leibler divergence-based differential eEvolution Markov chain filter for global localization of mobile robots

One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot's pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area.The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, fase III; S2013/MIT-2748),funded by Programas de Actividades I+Den la Comunidad de Madrid and cofunded by the Structural Funds of the EU

Two improved methods for mobile robot localization

Mobile robot localization is the problem of determining the robot\u27s pose given the map of its environment, based on the sensor reading and its movement. It is a fundamental and very important problem in the research of mobile robotics. Grid localization and Monte Carlo localization (MCL) are two of the most widely used approaches for localization, especially the MCL. However each of these two popular methods has its own problems. How to reduce the computation cost and better the accuracy is our main concern. In order to improve the performance of localization, we propose two improved localization algorithms. The first algorithm is called moving grid cell based MCL, which takes advantages of both grid localization and MCL and overcomes their respective shortcomings. The second algorithm is dynamic MCL based on clustering, which uses a cluster analysis component to reduce the computation cost

Using the Jensen-Shannon, density power, and Itakura-Saito divergences to implement an evolutionary-based global localization filter for mobile robots

One of the most demanding skills for a mobile robot is to be intelligent enough to know its own location. The global localization problem consists of obtaining the robot's pose (position and orientation) in a known map if the initial location is unknown. This task is addressed applying evolutionary computation concepts (Differential Evolution). In the current approach, the distances obtained from the laser sensors are combined with the predicted scan (in the known map) from possible locations to implement a cost function that is optimized by an evolutionary filter. The laser beams (sensor information) are modeled using a combination of probability distributions to implement a non-symmetric fitness function. The main contribution of this paper is to apply the probabilistic approach to design three different cost functions based on known divergences (Jensen-Shannon, Itakura-Saito, and density power). The three metrics have been tested in different experiments and the localization module performance is exceptional in regions with occlusions caused by different obstacles. This fact validates that the non-symmetric probabilistic approach is a suitable technique to be applied to multiple metrics.This work was supported by the Competitive Improvement of Drilling and Blasting Cycle in Mining and Underground-Works through New Techniques of Engineering, Explosives, Prototypes, and Advanced Tools, which is a Research and Development project undertaken by the following companies: Obras Subterr a neas, MaxamCorp Holding, Putzmeister Iberica, Subterra Ingenieria, Expace On Boards Systems, Dacartec Servicios Informaticos, and Cepasa Ensayos Geotecnicos

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

Coevolution Based Adaptive Monte Carlo Localization (CEAMCL)

An adaptive Monte Carlo localization algorithm based on coevolution mechanism of ecological species is proposed. Samples are clustered into species, each of which represents a hypothesis of the robot's pose. Since the coevolution between the species ensures that the multiple distinct hypotheses can be tracked stably, the problem of premature convergence when using MCL in highly symmetric environments can be solved. And the sample size can be adjusted adaptively over time according to the uncertainty of the robot's pose by using the population growth model. In addition, by using the crossover and mutation operators in evolutionary computation, intra-species evolution can drive the samples move towards the regions where the desired posterior density is large. So a small size of samples can represent the desired density well enough to make precise localization. The new algorithm is termed coevolution based adaptive Monte Carlo localization (CEAMCL). Experiments have been carried out to prove the efficiency of the new localization algorithm