Evolutionary Optimization Techniques for 3D Simultaneous Localization and Mapping

Mención Internacional en el título de doctorMobile robots are growing up in applications to move through indoors and outdoors environments, passing from teleoperated applications to autonomous applications like exploring or navigating. For a robot to move through a particular location, it needs to gather information about the scenario using sensors. These sensors allow the robot to observe, depending on the sensor data type. Cameras mostly give information in two dimensions, with colors and pixels representing an image. Range sensors give distances from the robot to obstacles. Depth Cameras mix both technologies to expand their information to three-dimensional information. Light Detection and Ranging (LiDAR) provides information about the distance to the sensor but expands its range to planes and three dimensions alongside precision. So, mobile robots use those sensors to scan the scenario while moving. If the robot already has a map, the sensors measure, and the robot finds features that correspond to features on the map to localize itself. Men have used Maps as a specialized form of representing the environment for more than 5000 years, becoming a piece of important information in today’s daily basics. Maps are used to navigate from one place to another, localize something inside some boundaries, or as a form of documentation of essential features. So naturally, an intuitive way of making an autonomous mobile robot is to implement geometrical information maps to represent the environment. On the other hand, if the robot does not have a previous map, it should build it while moving around. The robot computes the sensor information with the odometer sensor information to achieve this task. However, sensors have their own flaws due to precision, calibration, or accuracy. Furthermore, moving a robot has its physical constraints and faults that may occur randomly, like wheel drifting or mechanical miscalibration that may make the odometers fail in the measurement, causing misalignment during the map building. A novel technique was presented in the mid-90s to solve this problem and overpass the uncertainty of sensors while the robot is building the map, the Simultaneous Localization and Mapping algorithm (SLAM). Its goal is to build a map while the robot’s position is corrected based on the information of two or more consecutive scans matched together or find the rigid registration vector between them. This algorithm has been broadly studied and developed for almost 25 years. Nonetheless, it is highly relevant in innovations, modifications, and adaptations due to the advances in new sensors and the complexity of the scenarios in emerging mobile robotics applications. The scan matching algorithm aims to find a pose vector representing the transformation or movement between two robot observations by finding the best possible value after solving an equation representing a good transformation. It means searching for a solution in an optimum way. Typically this optimization process has been solved using classical optimization algorithms, like Newton’s algorithm or solving gradient and second derivatives formulations, yet this requires an initial guess or initial state that helps the algorithm point in the right direction, most of the time by getting this information from the odometers or inertial sensors. Although, it is not always possible to have or trust this information, as some scenarios are complex and reckon sensors fail. In order to solve this problem, this research presents the uses of evolutionary optimization algorithms, those with a meta-heuristics definition based on iterative evolution that mimics optimization processes that do not need previous information to search a limited range for solutions to solve a fitness function. The main goal of this dissertation is to study, develop and prove the benefits of evolutionary optimization algorithms in simultaneous localization and mapping for mobile robots in six degrees of freedom scenarios using LiDAR sensor information. This work introduces several evolutionary algorithms for scan matching, acknowledge a mixed fitness function for registration, solve simultaneous localization and matching in different scenarios, implements loop closure and error relaxation, and proves its performance at indoors, outdoors and underground mapping applications.Los robots móviles están creciendo en aplicaciones para moverse por entornos interiores y exteriores, pasando de aplicaciones teleoperadas a aplicaciones autónomas como explorar o navegar. Para que un robot se mueva a través de una ubicación en particular, necesita recopilar información sobre el escenario utilizando sensores. Estos sensores permiten que el robot observe, según el tipo de datos del sensor. Las cámaras en su mayoría brindan información en dos dimensiones, con colores y píxeles que representan una imagen. Los sensores de rango dan distancias desde el robot hasta los obstáculos. Las Cámaras de Profundidad mezclan ambas tecnologías para expandir su información a información tridimensional. Light Detection and Ranging (LiDAR) proporciona información sobre la distancia al sensor, pero amplía su rango a planos y tres dimensiones así como mejora la precisión. Por lo tanto, los robots móviles usan esos sensores para escanear el escenario mientras se mueven. Si el robot ya tiene un mapa, los sensores miden y el robot encuentra características que corresponden a características en dicho mapa para localizarse. La humanidad ha utilizado los mapas como una forma especializada de representar el medio ambiente durante más de 5000 años, convirtiéndose en una pieza de información importante en los usos básicos diarios de hoy en día. Los mapas se utilizan para navegar de un lugar a otro, localizar algo dentro de algunos límites o como una forma de documentación de características esenciales. Entonces, naturalmente, una forma intuitiva de hacer un robot móvil autónomo es implementar mapas de información geométrica para representar el entorno. Por otro lado, si el robot no tiene un mapa previo, deberá construirlo mientras se desplaza. El robot junta la información del sensor de distancias con la información del sensor del odómetro para lograr esta tarea de crear un mapa. Sin embargo, los sensores tienen sus propios defectos debido a la precisión, la calibración o la exactitud. Además, mover un robot tiene sus limitaciones físicas y fallas que pueden ocurrir aleatoriamente, como el desvío de las ruedas o una mala calibración mecánica que puede hacer que los contadores de desplazamiento fallen en la medición, lo que provoca una desalineación durante la construcción del mapa. A mediados de los años 90 se presentó una técnica novedosa para resolver este problema y superar la incertidumbre de los sensores mientras el robot construye el mapa, el algoritmo de localización y mapeo simultáneos (SLAM). Su objetivo es construir un mapa mientras se corrige la posición del robot en base a la información de dos o más escaneos consecutivos emparejados o encontrar el vector de correspondencia entre ellos. Este algoritmo ha sido ampliamente estudiado y desarrollado durante casi 25 años. No obstante, es muy relevante en innovaciones, modificaciones y adaptaciones debido a los avances en sensores y la complejidad de los escenarios en las aplicaciones emergentes de robótica móvil. El algoritmo de correspondencia de escaneo tiene como objetivo encontrar un vector de pose que represente la transformación o el movimiento entre dos observaciones del robot al encontrar el mejor valor posible después de resolver una ecuación que represente una buena transformación. Significa buscar una solución de forma óptima. Por lo general, este proceso de optimización se ha resuelto utilizando algoritmos de optimización clásicos, como el algoritmo de Newton o la resolución de formulaciones de gradientes y segundas derivadas, pero esto requiere una conjetura inicial o un estado inicial que ayude al algoritmo a apuntar en la dirección correcta, la mayoría de las veces obteniendo esta información de los sensores odometricos o sensores de inercia, aunque no siempre es posible tener o confiar en esta información, ya que algunos escenarios son complejos y los sensores fallan. Para resolver este problema, esta investigación presenta los usos de los algoritmos de optimización evolutiva, aquellos con una definición meta-heurística basada en la evolución iterativa que imita los procesos de optimización que no necesitan información previa para buscar dentro de un rango limitado el grupo de soluciones que resuelve una función de calidad. El objetivo principal de esta tesis es estudiar, desarrollar y probar los usos de algoritmos de optimización evolutiva en localización y mapeado simultáneos para robots móviles en escenarios de seis grados de libertad utilizando información de sensores LiDAR. Este trabajo introduce varios algoritmos evolutivos que resuelven la correspondencia entre medidas, soluciona el problema de SLAM, implementa una fusion de funciones objetivos y demuestra sus ventajas con pruebas en escenarios reales tanto en interiores, exteriores como mapeado de escenarios subterraneos.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Gerardo Fernández López.- Secretario: María Dolores Blanco Rojas.- Vocal: David Álvarez Sánche

Monte Carlo Localization in Hand-Drawn Maps

Robot localization is a one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we are able to localize the robot in the correct room with a robustness up to 80

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

Localization, Mapping and SLAM in Marine and Underwater Environments

The use of robots in marine and underwater applications is growing rapidly. These applications share the common requirement of modeling the environment and estimating the robots’ pose. Although there are several mapping, SLAM, target detection and localization methods, marine and underwater environments have several challenging characteristics, such as poor visibility, water currents, communication issues, sonar inaccuracies or unstructured environments, that have to be considered. The purpose of this Special Issue is to present the current research trends in the topics of underwater localization, mapping, SLAM, and target detection and localization. To this end, we have collected seven articles from leading researchers in the field, and present the different approaches and methods currently being investigated to improve the performance of underwater robots

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

The Application of Ant Colony Optimization

The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance

Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation