Marine Vessel Inspection as a Novel Field for Service Robotics: A Contribution to Systems, Control Methods and Semantic Perception Algorithms.

This cumulative thesis introduces a novel field for service robotics: the inspection of marine vessels using mobile inspection robots. In this thesis, three scientific contributions are provided and experimentally verified in the field of marine inspection, but are not limited to this type of application. The inspection scenario is merely a golden thread to combine the cumulative scientific results presented in this thesis. The first contribution is an adaptive, proprioceptive control approach for hybrid leg-wheel robots, such as the robot ASGUARD described in this thesis. The robot is able to deal with rough terrain and stairs, due to the control concept introduced in this thesis. The proposed system is a suitable platform to move inside the cargo holds of bulk carriers and to deliver visual data from inside the hold. Additionally, the proposed system also has stair climbing abilities, allowing the system to move between different decks. The robot adapts its gait pattern dynamically based on proprioceptive data received from the joint motors and based on the pitch and tilt angle of the robot's body during locomotion. The second major contribution of the thesis is an independent ship inspection system, consisting of a magnetic wall climbing robot for bulkhead inspection, a particle filter based localization method, and a spatial content management system (SCMS) for spatial inspection data representation and organization. The system described in this work was evaluated in several laboratory experiments and field trials on two different marine vessels in close collaboration with ship surveyors. The third scientific contribution of the thesis is a novel approach to structural classification using semantic perception approaches. By these methods, a structured environment can be semantically annotated, based on the spatial relationships between spatial entities and spatial features. This method was verified in the domain of indoor perception (logistics and household environment), for soil sample classification, and for the classification of the structural parts of a marine vessel. The proposed method allows the description of the structural parts of a cargo hold in order to localize the inspection robot or any detected damage. The algorithms proposed in this thesis are based on unorganized 3D point clouds, generated by a LIDAR within a ship's cargo hold. Two different semantic perception methods are proposed in this thesis. One approach is based on probabilistic constraint networks; the second approach is based on Fuzzy Description Logic and spatial reasoning using a spatial ontology about the environment

Magnetic-Assisted Initialization for Infrastructure-free Mobile Robot Localization

Most of the existing mobile robot localization solutions are either heavily dependent on pre-installed infrastructures or having difficulty working in highly repetitive environments which do not have sufficient unique features. To address this problem, we propose a magnetic-assisted initialization approach that enhances the performance of infrastructure-free mobile robot localization in repetitive featureless environments. The proposed system adopts a coarse-to-fine structure, which mainly consists of two parts: magnetic field-based matching and laser scan matching. Firstly, the interpolated magnetic field map is built and the initial pose of the mobile robot is partly determined by the k-Nearest Neighbors (k-NN) algorithm. Next, with the fusion of prior initial pose information, the robot is localized by laser scan matching more accurately and efficiently. In our experiment, the mobile robot was successfully localized in a featureless rectangular corridor with a success rate of 88% and an average correct localization time of 6.6 seconds

Mapeamento magnético para navegação robótica em ambientes interiores

Localization has always been one of the fundamental problems in the field of robotic navigation. The emergence of GPS came as a solution for localization systems in outdoor environments. However, the accuracy of GPS is not always sufficient and GPS based systems often fail and are not suited for indoor environments. Considering this, today there is a variety of real time localization technologies. It is quite common to see magnetic anomalies in indoor environments, which arise due to the presence of ferromagnetic objects, such as concrete or steel infrastructures. In the conventional ambient magnetic field based robotic navigation, which uses the direction of the Earth’s magnetic field to determine orientation, these anomalies are seen as undesirable. However, if the environment is rich in anomalies with sufficient local variability, they can be mapped and used as features for localization purposes. The work presented in this dissertation aims at demonstrating that it is possible to combine the odometric measurements of a mobile robot with magnetic field measurements, in order to effectively estimate the position of the robot in real time in an indoor environment. For this purpose, it is necessary to map the navigation space and develop a localization algorithm. First, the issues addressed to create a magnetic map are presented, namely data acquisition, employed interpolation methods and validation processes. Subsequently, the developed localization algorithm, based on a particle filter, is depicted, as well as the respective experimental validation tests.A localização sempre fui um dos problemas fundamentais a resolver no âmbito da navegação robótica. O surgimento do GPS veio a servir de solução para bastantes sistemas de localização em ambientes exteriores. No entanto, a exatidão do GPS nem sempre é suficiente e os sistemas baseados em GPS falham frequentemente e não são aplicáveis em ambientes interiores. À vista disso, hoje existe uma variedade de tecnologias de localização em tempo real. É bastante comum verificarem-se anomalias magnéticas em ambientes interiores, que provêm de objetos ferromagnéticos, como infraestruturas de betão ou aço. Na navegação robótica baseada na leitura do campo magnético convencional, que utiliza a direção do campo magnético terrestre para determinar a orientação, estas anomalias são vistas como indesejáveis. No entanto, se o ambiente for rico em anomalias com variabilidade local suficiente, estas podem ser mapeadas e utilizadas como caraterísticas para efeitos de localização. O trabalho apresentado nesta dissertação visa a demonstrar que é possível conjugar as medidas odométricas de um robô móvel com medições do campo magnético, para efetivamente localizar o robô em tempo real num ambiente interior. Para esse efeito, é necessário mapear o espaço de navegação e desenvolver um algoritmo de localização. Primeiramente, são apresentadas as questões abordadas para criar um mapa magnético, nomeadamente as aquisições de dados, os métodos de interpolação e os processos de validação. Posteriormente, é retratado o algoritmo de localização desenvolvido, baseado num filtro de partículas, assim como os respetivos testes experimentais de validação.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Validation of robotic navigation strategies in unstructured environments: from autonomous to reactive

The main topic of this master thesis is the validation of a navigation algorithm designed to perform autonomously in unstructured environments. Computer simulations and experimental tests with a mobile robot have allowed reaching the established objective. The presented approach is effective, consistent, and able to attain safe navigation with static and dynamic configurations. This work contains a survey of the principal navigation strategies and components. Afterwards, a recap of the history of robotics is briefly illustrated, emphasizing the description of mobile robotics and locomotion. Subsequently, it presents the development of an algorithm for autonomous navigation through an unknown environment for mobile robots. The algorithm seeks to compute trajectories that lead to a target unknown position without falling into a recurrent loop. The code has been entirely written and tested in MATLAB, using randomly generated obstacles of different sizes. The developed algorithm is used as a benchmark to analyze different predictive strategies for the navigation of mobile robots in the presence of environments not known a priori and overpopulated with obstacles. Then, an innovative algorithm for navigation, called NAPVIG, is described and analyzed. The algorithm has been built using ROS and tested in Gazebo real-time simulator. In order to achieve high performances, optimal parameters have been found tuning and simulating the algorithm in different environmental configurations. Finally, an experimental campaign in the SPARCS laboratory of the University of Padua enabled the validation of the chosen parameters

Grabbing power line conductors based on the measurements of the magnetic field strength

This paper presents the method for the localization and grabbing of the long straight conductor based only on the magnetic field generated by the alternating current flowing through the conductor. The method uses two magnetometers mounted on the robot arm end-effector for localization. This location is then used to determine needed robot movement in order to grab the conductor. The method was tested in the laboratory conditions using the Schunk LWA 4P 6-axis robot arm.Comment: 2021 Aerial Robotic Systems Physically Interacting with the Environment (AIRPHARO). arXiv admin note: text overlap with arXiv:2206.0916

Magnetic Navigation using Attitude-Invariant Magnetic Field Information for Loop Closure Detection

Indoor magnetic fields are a combination of Earth's magnetic field and disruptions induced by ferromagnetic objects, such as steel structural components in buildings. As a result of these disruptions, pervasive in indoor spaces, magnetic field data is often omitted from navigation algorithms in indoor environments. This paper leverages the spatially-varying disruptions to Earth's magnetic field to extract positional information for use in indoor navigation algorithms. The algorithm uses a rate gyro and an array of four magnetometers to estimate the robot's pose. Additionally, the magnetometer array is used to compute attitude-invariant measurements associated with the magnetic field and its gradient. These measurements are used to detect loop closure points. Experimental results indicate that the proposed approach can estimate the pose of a ground robot in an indoor environment within meter accuracy

Feasibility study: Magnetic-based passenger localization in train stations

Train stations are a key element of any transport network because they concentrate a large amount of passenger traffic on a daily basis. Passenger localization in train stations is though limited nowadays by the lack of satellite reception indoors and underground. A possible solution could be to use magnetometers, since they are embedded in today’s smartphones and are available in all urban environments. One of the most extended algorithms to perform magnetic localization is magnetic fingerprinting, however magnetic fingerprinting has not yet been proved viable in train stations. The aim of this article is to present a feasibility study of the possibility to apply magnetic fingerprinting in train stations to locate passengers. We have measured and analyzed the magnetic maps of different train stations in Munich, Germany. Our results show that, the functioning of the trains and the electric topology of the stations hinder the passenger localization using magnetic fingerprinting

Robot Navigation in Distorted Magnetic Fields

This thesis investigates the utilization of magnetic field distortions for the localization and navigation of robotic systems. The work comprehensively illuminates the various aspects that are relevant in this context. Among other things, the characteristics of magnetic field environments are assessed and examined for their usability for robot navigation in various typical mobile robot deployment scenarios. A strong focus of this work lies in the self-induced static and dynamic magnetic field distortions of complex kinematic robots, which could hinder the use of magnetic fields because of their interference with the ambient magnetic field. In addition to the examination of typical distortions in robots of different classes, solutions for compensation and concrete tools are developed both in hardware (distributed magnetometer sensor systems) and in software. In this context, machine learning approaches for learning static and dynamic system distortions are explored and contrasted with classical methods for calibrating magnetic field sensors. In order to extend probabilistic state estimation methods towards the localization in magnetic fields, a measurement model based on Mises-Fisher distributions is developed in this thesis. Finally, the approaches of this work are evaluated in practice inside and outside the laboratory in different environments and domains (e.g. office, subsea, desert, etc.) with different types of robot systems

Modeling and interpolation of the ambient magnetic field by Gaussian processes

Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior on the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications: we demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic