Plume Source Localization and Boundary Prediction

Plume location and prediction using mobile sensors is the main contribution of this thesis. Plume concentration values measured by chemical sensors at different locations are used to estimate the source of the plume. This is achieved by employing a stochastic approximation technique to localize the source and compare its performance to the nonlinear least squares method. The source location is then used as the initial estimate for the boundary tracking problem. Sensor measurements are used to estimate the parameters and the states of the state space model of the dynamics of the plume boundary. The predicted locations are the reference inputs for the LQR controller. Measurements at the new locations (after the correction of the prediction error) are added to the set of data to refine the next prediction process. Simulations are performed to demonstrate the viability of the methods developed. Finally, interpolation using the sensors locations is used to approximate the boundary shape

Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins

In this work we propose an Uncertainty Quantification methodology for sedimentary basins evolution under mechanical and geochemical compaction processes, which we model as a coupled, time-dependent, non-linear, monodimensional (depth-only) system of PDEs with uncertain parameters. While in previous works (Formaggia et al. 2013, Porta et al., 2014) we assumed a simplified depositional history with only one material, in this work we consider multi-layered basins, in which each layer is characterized by a different material, and hence by different properties. This setting requires several improvements with respect to our earlier works, both concerning the deterministic solver and the stochastic discretization. On the deterministic side, we replace the previous fixed-point iterative solver with a more efficient Newton solver at each step of the time-discretization. On the stochastic side, the multi-layered structure gives rise to discontinuities in the dependence of the state variables on the uncertain parameters, that need an appropriate treatment for surrogate modeling techniques, such as sparse grids, to be effective. We propose an innovative methodology to this end which relies on a change of coordinate system to align the discontinuities of the target function within the random parameter space. The reference coordinate system is built upon exploiting physical features of the problem at hand. We employ the locations of material interfaces, which display a smooth dependence on the random parameters and are therefore amenable to sparse grid polynomial approximations. We showcase the capabilities of our numerical methodologies through two synthetic test cases. In particular, we show that our methodology reproduces with high accuracy multi-modal probability density functions displayed by target state variables (e.g., porosity).Comment: 25 pages, 30 figure

Cooperative localisation in underwater robotic swarms for ocean bottom seismic imaging.

Spatial information must be collected alongside the data modality of interest in wide variety of sub-sea applications, such as deep sea exploration, environmental monitoring, geological and ecological research, and samples collection. Ocean-bottom seismic surveys are vital for oil and gas exploration, and for productivity enhancement of an existing production facility. Ocean-bottom seismic sensors are deployed on the seabed to acquire those surveys. Node deployment methods used in industry today are costly, time-consuming and unusable in deep oceans. This study proposes the autonomous deployment of ocean-bottom seismic nodes, implemented by a swarm of Autonomous Underwater Vehicles (AUVs). In autonomous deployment of ocean-bottom seismic nodes, a swarm of sensor-equipped AUVs are deployed to achieve ocean-bottom seismic imaging through collaboration and communication. However, the severely limited bandwidth of underwater acoustic communications and the high cost of maritime assets limit the number of AUVs that can be deployed for experiments. A holistic fuzzy-based localisation framework for large underwater robotic swarms (i.e. with hundreds of AUVs) to dynamically fuse multiple position estimates of an autonomous underwater vehicle is proposed. Simplicity, exibility and scalability are the main three advantages inherent in the proposed localisation framework, when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation (by 16.53% and 35.17% respectively) at a swarm size of 150 AUVs when compared to the Extended Kalman Filter based localisation with round-robin scheduling. The proposed fuzzy based localisation method requires fuzzy rules and fuzzy set parameters tuning, if the deployment scenario is changed. Therefore a cooperative localisation scheme that relies on a scalar localisation confidence value is proposed. A swarm subset is navigationally aided by ultra-short baseline and a swarm subset (i.e. navigation beacons) is configured to broadcast navigation aids (i.e. range-only), once their confidence values are higher than a predetermined confidence threshold. The confidence value and navigation beacons subset size are two key parameters for the proposed algorithm, so that they are optimised using the evolutionary multi-objective optimisation algorithm NSGA-II to enhance its localisation performance. Confidence value-based localisation is proposed to control the cooperation dynamics among the swarm agents, in terms of aiding acoustic exteroceptive sensors. Given the error characteristics of a commercially available ultra-short baseline system and the covariance matrix of a trilaterated underwater vehicle position, dead reckoning navigation - aided by Extended Kalman Filter-based acoustic exteroceptive sensors - is performed and controlled by the vehicle's confidence value. The proposed confidence-based localisation algorithm has significantly improved the entire swarm mean localisation error when compared to the fuzzy-based and round-robin Extended Kalman Filter-based localisation methods (by 67.10% and 59.28% respectively, at a swarm size of 150 AUVs). The proposed fuzzy-based and confidence-based localisation algorithms for cooperative underwater robotic swarms are validated on a co-simulation platform. A physics-based co-simulation platform that considers an environment's hydrodynamics, industrial grade inertial measurement unit and underwater acoustic communications characteristics is implemented for validation and optimisation purposes

Étude d'un réseau de capteur UWB pour la localisation et la communication dans un environnement minier

Le jour n'est peut-être pas très loin où une mine pourra compter sur un système de communication sans fil pour échanger des données, transmettre des informations ou localiser des travailleurs dans le cas d'une activité normale ou en cas d'urgence. Au point de vue de la sécurité, un système de communications sans fil aurait l'avantage de localiser en temps réel un travailleur ou un engin. Les travailleurs se déplacent sans cesse dans une mine. Avec une technologie sans fil permanente, on pourrait localiser les personnes de manière relativement précise. Même en cas d'éboulement, avec une technologie adaptée, il serait possible de savoir où se trouve la personne en détresse. Notre travail de recherche s'inscrit dans la perspective du développement d'un réseau de capteurs ultra large bande (UWB) pour deux applications : l'aide à la radiolocalisation et l'extension du réseau de capteurs sans fil dans la mine. Cette étude est focalisée sur trois aspects. La première partie de notre étude consiste à étudier tous les problèmes reliés à la radiolocalisation dans la mine. Vue l'importance de cette application, nous avons mis en oeuvre un réseau de capteurs en tenant compte d'un futur déploiement dans la mine. La technologie utilisée repose sur la technologie ultra large bande. Comme il n'existe pas de travaux qui traitent ce genre de problèmes, nous avons commencé notre étude par une caractérisation du canal UWB dans les mines souterraines. Pour atteindre ces objectifs, plusieurs campagnes de mesure sur site (mine expérimentale) ont été menées. Nous sommes parvenus à une modélisation du canal de propagation et à avancer des recommandations pour aider au dimensionnement d'un réseau de capteurs dans ce type d'environnement. Dans la première partie, le but est d'étudier le problème de radiolocalisation avec les réseaux de capteurs. Notre scénario proposé serait de placer des capteurs sur chaque agent (mineur, engin). On suppose que chaque noeud (agent) qui circule à travers un réseau d'ancre maillé (déjà déployé), va extraire des informations de distance (en utilisant le critère de temps d'arrivée), ensuite il va utiliser un algorithme de positionnement distribué afin de déterminer sa propre position. Lors de cette partie nous avons aussi étudié quelques estimateurs cohérents et non-cohérents du temps d'arrivée. La caractérisation de l'erreur de mesure utilisant le temps d'arrivée dans un environnement minier a été aussi évaluée. Enfin, dans la dernière partie, nous avons analysé par simulations un déploiement d'un réseau de capteurs UWB ad hoc dans la mine. Nous avons choisi d'adopter une approche théorique afin d'évaluer les performances de cette configuration. Une conception intercouche pour un routage optimal a été étudiée. Nous avons utilisé la couche physique/réseau afin de minimiser l'énergie consommée lors de l'acheminement du données

Optimizing Image Reconstruction in Electrical Impedance Tomography

Tato disertační práce pojednává o optimalizaci algoritmů pro rekonstrukci obrazu neznámé měrné vodivosti z měřených dat pořízených elektrickou impedanční tomografií. Danou problematiku zde věcně vymezuje několik různých prvků, zejména pak stručný matematický popis dopředné a inverzní úlohy řešené různými přístupy, metodika měření a pořizování dat pro rekonstrukci a přehled dostupných numerických nástrojů. Uvedenou charakteristiku rozšiřuje rozbor optimalizací parametrů modelu ovlivňujících přesnost rekonstrukce, způsoby paralelního zpracování algoritmů a souhrn dostupných zařízení pro měření tomografických dat. Na základě získaných poznatků byla navržena optimalizace parametrů matematického modelu, která umožňuje jeho velmi přesný návrh dle měřených dat. V této souvislosti dochází ke snížení nejistoty rekonstrukce rozložení konduktivity. Pro zefektivnění procesu získávání dat bylo navrženo zařízení k automatizaci tomografie s důrazem na cenovou dostupnost a snížení nejistoty měření. V oblasti tvorby numerického modelu byly dále zkoumány možnosti užití otevřených a uzavřených domén pro různé metody regularizace a hrubost sítě, a to s ohledem na velikost chyby rekonstruované konduktivity a výpočetní náročnost. Součástí práce je také paralelizace subalgoritmů rekonstrukce s využitím vícejádrové grafické karty. Předložené výsledky mají přímý vliv na snížení nejistoty rekonstrukce (optimalizací počáteční hodnoty konduktivity, rozmístění elektrod a tvarové deformace domény, regularizačních metod a typu domén) a urychlení výpočtů paralelizací algoritmů, přičemž výzkum byl podpořen vlastním návrhem jednotky pro automatizaci tomografie.The thesis presents, analyzes, and discusses the optimization of algorithms that reconstruct images of unknown specific conductivity from data acquired via electrical impedance tomography. In this context, the author provides a brief mathematical description of the forward and inverse tasks solved by using diverse approaches, characterizes relevant measurement techniques and data acquisition procedures, and discusses available numerical tools. Procedurally, the initial working stages involved analyzing the methods for optimizing those parameters of the model that influence the reconstruction accuracy; demonstrating approaches to the parallel processing of the algorithms; and outlining a survey of available instruments to acquire the tomographic data. The obtained knowledge then yielded a process for optimizing the parameters of the mathematical model, thus allowing the model to be designed precisely, based on the measured data; such a precondition eventually reduced the uncertainty in reconstructing the specific conductivity distribution. When forming the numerical model, the author investigated the possibilities and overall impact of combining the open and closed domains with various regularization methods and mesh element scales, considering both the character of the conductivity reconstruction error and the computational intensity. A complementary task resolved within the broader scheme outlined above lay in parallelizing the reconstruction subalgorithms by using a multi-core graphics card. The results of the thesis are directly reflected in a reduced reconstruction uncertainty (through an optimization of the initial conductivity value, placement of the electrodes, and shape deformation of the domains) and accelerated computation via parallelized algorithms. The actual research benefited from an in-house designed automated tomography unit.