Convergence of a Particle-based Approximation of the Block Online Expectation Maximization Algorithm

Online variants of the Expectation Maximization (EM) algorithm have recently been proposed to perform parameter inference with large data sets or data streams, in independent latent models and in hidden Markov models. Nevertheless, the convergence properties of these algorithms remain an open problem at least in the hidden Markov case. This contribution deals with a new online EM algorithm which updates the parameter at some deterministic times. Some convergence results have been derived even in general latent models such as hidden Markov models. These properties rely on the assumption that some intermediate quantities are available in closed form or can be approximated by Monte Carlo methods when the Monte Carlo error vanishes rapidly enough. In this paper, we propose an algorithm which approximates these quantities using Sequential Monte Carlo methods. The convergence of this algorithm and of an averaged version is established and their performance is illustrated through Monte Carlo experiments

Localization Based on Parallel Robots Kinematics as an Alternative to Trilateration

In this work a new scheme for range-based localization is proposed. The main goal is to estimate the position of a mobile point based on distance measurements from fixed devices, called anchors, and on inertial measurements. Due to the non-linear nature of the problem, an analytic relation to compute the position starting from these measurements does not exist, and often trilateration methods are used, generally based on least-square algorithms. The proposed scheme is based on the modelling of the localization process as a parallel robot, thereby methodologies and control algorithms used in the robotic area can be exploited. In particular, a closed loop control system is designed for tracking the position of a mobile point based on range measurements from fixed anchors, and it is shown a peculiar structure of the tracking error dynamics, whose allows an intuitive gain tuning and ensures global exponential stability. Moreover, it is also shown a nice connection between tuning parameters and rate of convergence of the estimation error. Experimental results confirm the validity of the proposed localization method

Uncertainty Minimization in Robotic 3D Mapping Systems Operating in Dynamic Large-Scale Environments

This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras. While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation. Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications

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

Path planning algorithms for autonomous navigation of a non-holonomic robot in unstructured environments

openPath planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms.Path planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms

Belief-Space Planning for Resourceful Manipulation and Mobility

Robots are increasingly expected to work in partially observable and unstructured environments. They need to select actions that exploit perceptual and motor resourcefulness to manage uncertainty based on the demands of the task and environment. The research in this dissertation makes two primary contributions. First, it develops a new concept in resourceful robot platforms called the UMass uBot and introduces the sixth and seventh in the uBot series. uBot-6 introduces multiple postural configurations that enable different modes of mobility and manipulation to meet the needs of a wide variety of tasks and environmental constraints. uBot-7 extends this with the use of series elastic actuators (SEAs) to improve manipulation capabilities and support safer operation around humans. The resourcefulness of these robots is complemented with a belief-space planning framework that enables task-driven action selection in the context of the partially observable environment. The framework uses a compact but expressive state representation based on object models. We extend an existing affordance-based object model, called an aspect transition graph (ATG), with geometric information. This enables object-centric modeling of features and actions, making the model much more expressive without increasing the complexity. A novel task representation enables the belief-space planner to perform general object-centric tasks ranging from recognition to manipulation of objects. The approach supports the efficient handling of multi-object scenes. The combination of the physical platform and the planning framework are evaluated in two novel, challenging, partially observable planning domains. The ARcube domain provides a large population of objects that are highly ambiguous. Objects can only be differentiated using multi-modal sensor information and manual interactions. In the dexterous mobility domain, a robot can employ multiple mobility modes to complete navigation tasks under a variety of possible environment constraints. The performance of the proposed approach is evaluated using experiments in simulation and on a real robot

Infrared Sensor System for Mobile-Robot Positioning in Intelligent Spaces

The aim of this work was to position a Mobile Robot in an Intelligent Space, and this paper presents a sensorial system for measuring differential phase-shifts in a sinusoidally modulated infrared signal transmitted from the robot. Differential distances were obtained from these phase-shifts, and the position of the robot was estimated by hyperbolic trilateration. Due to the extremely severe trade-off between SNR, angle (coverage) and real-time response, a very accurate design and device selection was required to achieve good precision with wide coverage and acceptable robot speed. An I/Q demodulator was used to measure phases with one-stage synchronous demodulation to DC. A complete set of results from real measurements, both for distance and position estimations, is provided to demonstrate the validity of the system proposed, comparing it with other similar indoor positioning systems