Ce travail porte, de façon théorétique et pratique, sur les sujets plus pertinents autour des drones en navigation autonome et semi-autonome. Conformément à la nature multidisciplinaire des problèmes étudies, une grande diversité des techniques et théories ont été couverts dans les domaines de la robotique, l’automatique, l’informatique, la vision par ordinateur et les systèmes embarques, parmi outres.Dans le cadre de cette thèse, deux plates-formes expérimentales ont été développées afin de valider la théorie proposée pour la navigation autonome d’un drone. Le premier prototype, développé au laboratoire, est un quadrirotor spécialement conçu pour les applications extérieures. La deuxième plate-forme est composée d’un quadrirotor à bas coût du type AR.Drone fabrique par Parrot. Le véhicule est connecté sans fil à une station au sol équipé d’un système d’exploitation pour robots (ROS) et dédié à tester, d’une façon facile, rapide et sécurisé, les algorithmes de vision et les stratégies de commande proposés. Les premiers travaux développés ont été basés sur la fusion de donnés pour estimer la position du drone en utilisant des capteurs inertiels et le GPS. Deux stratégies ont été étudiées et appliquées, le Filtre de Kalman Etendu (EKF) et le filtre à Particules (PF). Les deux approches prennent en compte les mesures bruitées de la position de l’UAV, de sa vitesse et de son orientation. On a réalisé une validation numérique pour tester la performance des algorithmes. Une tâche dans le cahier de cette thèse a été de concevoir d’algorithmes de commande pour le suivi de trajectoires ou bien pour la télé-opération. Pour ce faire, on a proposé une loi de commande basée sur l’approche de Mode Glissants à deuxième ordre. Cette technique de commande permet de suivre au quadrirotor de trajectoires désirées et de réaliser l’évitement des collisions frontales si nécessaire. Etant donné que la plate-forme A.R.Drone est équipée d’un auto-pilote d’attitude, nous avons utilisé les angles désirés de roulis et de tangage comme entrées de commande. L’algorithme de commande proposé donne de la robustesse au système en boucle fermée. De plus, une nouvelle technique de vision monoculaire par ordinateur a été utilisée pour la localisation d’un drone. Les informations visuelles sont fusionnées avec les mesures inertielles du drone pour avoir une bonne estimation de sa position. Cette technique utilise l’algorithme PTAM (localisation parallèle et mapping), qui s’agit d’obtenir un nuage de points caractéristiques dans l’image par rapport à une scène qui servira comme repère. Cet algorithme n’utilise pas de cibles, de marqueurs ou de scènes bien définies. La contribution dans cette méthodologie a été de pouvoir utiliser le nuage de points disperse pour détecter possibles obstacles en face du véhicule. Avec cette information nous avons proposé un algorithme de commande pour réaliser l’évitement d’obstacles. Cette loi de commande utilise les champs de potentiel pour calculer une force de répulsion qui sera appliquée au drone. Des expériences en temps réel ont montré la bonne performance du système proposé. Les résultats antérieurs ont motivé la conception et développement d’un drone capable de réaliser en sécurité l’interaction avec les hommes et les suivre de façon autonome. Un classificateur en cascade du type Haar a été utilisé pour détecter le visage d’une personne. Une fois le visage est détecté, on utilise un filtre de Kalman (KF) pour améliorer la détection et un algorithme pour estimer la position relative du visage. Pour réguler la position du drone et la maintenir à une distance désirée du visage, on a utilisé une loi de commande linéaire. / The present document addresses, theoretically and experimentally, the most relevant topics for Unmanned Aerial Vehicles (UAVs) in autonomous and semi-autonomous navigation. According with the multidisciplinary nature of the studied problems, a wide range of techniques and theories are covered in the fields of robotics, automatic control, computer science, computer vision and embedded systems, among others. As part of this thesis, two different experimental platforms were developed in order to explore and evaluate various theories and techniques of interest for autonomous navigation. The first prototype is a quadrotor specially designed for outdoor applications and was fully developed in our lab. The second testbed is composed by a non expensive commercial quadrotor kind AR. Drone, wireless connected to a ground station equipped with the Robot Operating System (ROS), and specially intended to test computer vision algorithms and automatic control strategies in an easy, fast and safe way. In addition, this work provides a study of data fusion techniques looking to enhance the UAVs pose estimation provided by commonly used sensors. Two strategies are evaluated in particular, an Extended Kalman Filter (EKF) and a Particle Filter (PF). Both estimators are adapted for the system under consideration, taking into account noisy measurements of the UAV position, velocity and orientation. Simulations show the performance of the developed algorithms while adding noise from real GPS (Global Positioning System) measurements. Safe and accurate navigation for either autonomous trajectory tracking or haptic teleoperation of quadrotors is presented as well. A second order Sliding Mode (2-SM) control algorithm is used to track trajectories while avoiding frontal collisions in autonomous flight. The time-scale separation of the translational and rotational dynamics allows us to design position controllers by giving desired references in the roll and pitch angles, which is suitable for quadrotors equipped with an internal attitude controller. The 2-SM control allows adding robustness to the closed-loop system. A Lyapunov based analysis probes the system stability. Vision algorithms are employed to estimate the pose of the vehicle using only a monocular SLAM (Simultaneous Localization and Mapping) fused with inertial measurements. Distance to potential obstacles is detected and computed using the sparse depth map from the vision algorithm. For teleoperation tests, a haptic device is employed to feedback information to the pilot about possible collisions, by exerting opposite forces. The proposed strategies are successfully tested in real-time experiments, using a low-cost commercial quadrotor. Also, conception and development of a Micro Aerial Vehicle (MAV) able to safely interact with human users by following them autonomously, is achieved in the present work. Once a face is detected by means of a Haar cascade classifier, it is tracked applying a Kalman Filter (KF), and an estimation of the relative position with respect to the face is obtained at a high rate. A linear Proportional Derivative (PD) controller regulates the UAV’s position in order to keep a constant distance to the face, employing as well the extra available information from the embedded UAV’s sensors. Several experiments were carried out through different conditions, showing good performance even under disadvantageous scenarios like outdoor flight, being robust against illumination changes, wind perturbations, image noise and the presence of several faces on the same image. Finally, this thesis deals with the problem of implementing a safe and fast transportation system using an UAV kind quadrotor with a cable suspended load. The objective consists in transporting the load from one place to another, in a fast way and with minimum swing in the cable.

Nous présentons un système de tracking optique-inertiel qui consiste en deux caméras stationnaires et une Sensor Unit avec des marqueurs optiques et une centrale inertielle. La Sensor Unit est fixée sur l'objet suivi et sa position et orientation sont déterminées par un algorithme de fusion de données. Le système de tracking est destiné à asservir un outil à main dans un système de chirurgie naviguée ou assistée par ordinateur. L'algorithme de fusion de données intègre les données des différents capteurs, c'est-à-dire les données optiques des caméras et les données inertielles des accéléromètres et gyroscopes. Nous présentons différents algorithmes qui rendent possible un tracking à grande bande passante avec au moins 200Hz avec des temps de latence bas grâce à une approche directe et des filtres dits invariants qui prennent en compte les symmétries du système. Grâce à ces propriétés, le système de tracking satisfait les conditions pour l'application désirée. Le système a été implementé et testé avec succès avec un dispositif expérimental. / We present an optical-inertial tracking system which consists of two stationary cameras and a Sensor Unit with optical markers and an inertial measurement unit (IMU). This Sensor Unit is attached to the object being tracked and its position and orientation are determined by a data fusion algorithm.The tracking system is to be used for servo-controlling a handheld tool in a navigated or computer-assisted surgery system.The data fusion algorithm integrates data from the different sensors, that is optical data from the cameras and inertial data from accelerometers and gyroscopes. We present different algorithms which ensure high-bandwidth tracking with at least 200Hz with low latencies by using a direct approach and so-called invariant filters which take into account system symmetries. Through these features, the tracking system meets the requirements for being used in the desired application.The system was successfully implemented and tested with an experimental setup.

In marine environments, acoustic wave propagation is determined by sound-speed variations in the water column (related to salinity, temperature and pressure) ,and seafloor properties in shallow environments. The refraction index variations and the boundary conditions guide the wave propagation so that an important amount of acoustic energy can propagate over long distances. Measurements of acoustic transmissions coupled with propagation models can be inverted to infer the water column properties (tomography) and the seafloor and subseafloor properties (geoacoustics).<p><p>In this thesis a new method for shallow water inversion based on the sequential assimilation of acoustic measurements in Kalman filters is developed. Filtering algorithms for nonlinear systems, as the ensemble Kalman filter (EnKF), enable the integration of complex acoustic propagation models in the measurement model. The inverse problem is here reformulated into a state-space model to track sequentially the parameters (temperature, receiver positions, etc.) and their uncertainty by filtering regularly new acoustic data.<p><p>Different applications are proposed to demonstrate the sequential acoustic filtering approach. First, the problem of characterizing horizontal inhomogeneities in the sound-speed field between an acoustic source and a vertical array of receivers is addressed. Starting from a range-averaged sound-speed profile, the filtering of complex multifrequency data enables the estimate and tracking of the range-dependence of the sound-speed field.<p>The second application deals with the geoacoustic inversion problem based on a mobile source-receiver setup. The filtering approach is shown to provide more stable results than conventional inversion methods with a reduced computational burden. The last application is dedicated to the tracking of specific oceanic structures affecting the sound-speed field, here thermal fronts. An original parameterization scheme which is specific to the tracked feature is developed and enables to monitor the principal characteristics of the sound-speed field by filtering multifrequency acoustic data.<p><p>This work shows that the sequential filtering approach of transmitted acoustic data can lead to environmental estimates on spatial and temporal scale of interest for regional or coastal oceanographic models and can supplement the dataset assimilated nowadays for forecasting purposes./Dans les environnements marins, la propagation des ondes acoustiques est directement conditionnée par les variations de vitesse de propagation dans l'eau (liée à la température, la salinité et la pression hydrostatique), ainsi que les propriétés du fond, lorsque le milieu est peu profond. La propagation de ces ondes, typiquement guidée par les variations d'indice de réfraction et les conditions aux limites, permet de transmettre une quantité d'énergie acoustique importante sur de longues distances. Associées à des modèles de propagation, des mesures de transmission acoustique peuvent être inversées afin de déterminer les propriétés de l'environnement sondé, que ce soit de la colonne d'eau (tomographie) ou du fond marin (géoacoustique).<p><p>Dans cette thèse, une nouvelle méthode d'inversion en milieu peu profond, basée sur l'assimilation séquentielle de mesures acoustiques dans des filtres de Kalman, est développée. Les algorithmes de filtrage développés pour les systèmes non linéaires, tel que l'ensemble Kalman filter (EnKF), permettent d'intégrer des modèles de propagation acoustique complexes au sein du modèle de mesure. Le problème inverse est reformulé de façon séquentielle, en un modèle d'espace d'états, de sorte que l'évolution des paramètres (température, positions des récepteurs, etc.) et de leur incertitude est suivie au fur et à mesure de l'assimilation de nouvelles mesures.<p><p>Différentes applications sont proposées pour démontrer les performances du filtrage séquentiel. Le premier problème abordé est celui de l'inversion et du suivi des inhomogénéités horizontales du champ de vitesse entre une source acoustique et une antenne verticale de récepteurs. A partir d'un profil de vitesse moyen sur la distance source-récepteurs, le filtrage de mesures complexes multi-fréquences permet d'estimer la dépendance horizontale du champ de vitesse et son évolution au cours du temps. La nature séquentielle de l'algorithme de filtrage motive la seconde application, dédiée à l'estimation des paramètres géoacoustiques d'un environnement à partir d'une configuration source-récepteur mobile. Les résultats démontrent que l'approche par filtrage permet d'obtenir des estimations géoacoustiques plus stables que celles obtenues par les méthodes d'inversion conventionnelles avec un coût de calcul réduit. La troisième et dernière application est dédiée au suivi de structures océaniques marquées, tels que les fronts thermiques. Une paramétrisation originale spécifique à la structure inversée est proposée et permet d'estimer et de suivre les caractéristiques principales du champ de température par filtrage de données acoustiques multi-fréquences.<p><p>Ce travail montre que l'approche séquentielle de l'inversion des données acoustiques peut mener à des estimations environnementales sur des échelles spatiales et temporelles d'intérêt pour les modèles océanographiques côtiers et régionaux, de façon à compléter les données assimilées quotidiennement pour les prédictions. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.

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This thesis deals with design of electronic laboratory model for teaching mechatronic subjects. The main part of the model consists of a RLC-circuit embedded in PCB. Other parts of PCB and data acquisition card mediate communication with Matlab environment. In the thesis the progress of design process, simulation, manufacture and model testing is described. The results are functioning educational model and several educational tasks, for which the solution are presented.

<div>Using sensor measurements to estimate the states and parameters of a system is a fundamental task in understanding the behavior of the system. Moreover, as modern systems grow rapidly in scale and complexity, it is not always possible to deploy sensors to measure all of the states and parameters of the system, due to cost and physical constraints. Therefore, selecting an optimal subset of all the candidate sensors to deploy and gather measurements of the system is an important and challenging problem. In addition, the systems may be targeted by external attackers who attempt to remove or destroy the deployed sensors. This further motivates the formulation of resilient sensor selection strategies. In this thesis, we address the sensor selection problem under different settings as follows. </div><div><br></div><div>First, we consider the optimal sensor selection problem for linear dynamical systems with stochastic inputs, where the Kalman filter is applied based on the sensor measurements to give an estimate of the system states. The goal is to select a subset of sensors under certain budget constraints such that the trace of the steady-state error covariance of the Kalman filter with the selected sensors is minimized. We characterize the complexity of this problem by showing that the Kalman filtering sensor selection problem is NP-hard and cannot be approximated within any constant factor in polynomial time for general systems. We then consider the optimal sensor attack problem for Kalman filtering. The Kalman filtering sensor attack problem is to attack a subset of selected sensors under certain budget constraints in order to maximize the trace of the steady-state error covariance of the Kalman filter with sensors after the attack. We show that the same results as the Kalman filtering sensor selection problem also hold for the Kalman filtering sensor attack problem. Having shown that the general sensor selection and sensor attack problems for Kalman filtering are hard to solve, our next step is to consider special classes of the general problems. Specifically, we consider the underlying directed network corresponding to a linear dynamical system and investigate the case when there is a single node of the network that is affected by a stochastic input. In this setting, we show that the corresponding sensor selection and sensor attack problems for Kalman filtering can be solved in polynomial time. We further study the resilient sensor selection problem for Kalman filtering, where the problem is to find a sensor selection strategy under sensor selection budget constraints such that the trace of the steady-state error covariance of the Kalman filter is minimized after an adversary removes some of the deployed sensors. We show that the resilient sensor selection problem for Kalman filtering is NP-hard, and provide a pseudo-polynomial-time algorithm to solve it optimally.</div><div> </div><div> Next, we consider the sensor selection problem for binary hypothesis testing. The problem is to select a subset of sensors under certain budget constraints such that a certain metric of the Neyman-Pearson (resp., Bayesian) detector corresponding to the selected sensors is optimized. We show that this problem is NP-hard if the objective is to minimize the miss probability (resp., error probability) of the Neyman-Pearson (resp., Bayesian) detector. We then consider three optimization objectives based on the Kullback-Leibler distance, J-Divergence and Bhattacharyya distance, respectively, in the hypothesis testing sensor selection problem, and provide performance bounds on greedy algorithms when applied to the sensor selection problem associated with these optimization objectives.</div><div> </div><div> Moving beyond the binary hypothesis setting, we also consider the setting where the true state of the world comes from a set that can have cardinality greater than two. A Bayesian approach is then used to learn the true state of the world based on the data streams provided by the data sources. We formulate the Bayesian learning data source selection problem under this setting, where the goal is to minimize the cost spent on the data sources such that the learning error is within a certain range. We show that the Bayesian learning data source selection is also NP-hard, and provide greedy algorithms with performance guarantees.</div><div> </div><div> Finally, in light of the COVID-19 pandemic, we study the parameter estimation measurement selection problem for epidemics spreading in networks. Here, the measurements (with certain costs) are collected by conducting virus and antibody tests on the individuals in the epidemic spread network. The goal of the problem is then to optimally estimate the parameters (i.e., the infection rate and the recovery rate of the virus) in the epidemic spread network, while satisfying the budget constraint on collecting the measurements. Again, we show that the measurement selection problem is NP-hard, and provide approximation algorithms with performance guarantees.</div>

Smart structure control systems commonly use piezoceramic sensors or accelerometers as vibration measurement devices. These measurement devices often produce noisy and/or low-precision signals, which makes it difficult to measure small-amplitude vibrations. Laser interferometry devices pose as an alternative high-precision position measurement method, capable of nanometer-scale resolution. The aim of this research is to utilize a model-based design approach to develop and implement a real-time Linear Quadratic Gaussian (LQG) regulator for a piezoelectric actuated smart structure using a high-precision laser interferometry measurement system to suppress the excitation of vibratory modes. The analytical model of the smart structure is derived using the extended Hamilton Principle and Euler-Bernoulli beam theory, and the equations of motion for the system are constructed using the assumed-modes method. The analytical model is organized in state-space form, in which the effects of a low-pass filter and sampling of the digital control system are also accounted for. The analytical model is subsequently validated against a finite-element model in Abaqus, a lumped parameter model in Simscape Multibody, and experimental modal analysis using the physical system. A discrete-time proportional-derivative (PD) controller is designed in a heuristic fashion to serve as a baseline performance criterion for the LQG regulator. The Kalman Filter observer and Linear Quadratic Regulator (LQR) components of the LQG regulator are also derived from the state-space model. It is found that the behavior of the analytical model closely matches that of the physical system, and the performance of the LQG regulator exceeds that of the PD controller. The LQG regulator demonstrated quality estimation of the state variables of the system and further constitutes an exceptional closed-loop control system for active vibration control and disturbance rejection of the smart structure.

L'objectif de cette thèse est d'étudier le problème de la détection de données dans le système de communication sans fil, à la fois pour le cas de l'information d'état de canal parfaite et imparfaite au niveau du récepteur. Comme on le sait, la complexité de MLSE est exponentielle en la mémoire de canal et la cardinalité de l'alphabet symbole est rapidement ingérable, ce qui force à recourir à des approches sousoptimales. Par conséquent, en premier lieu, nous proposons une nouvelle égalisation itérative lorsque le canal est inconnu à l'émetteur et parfaitement connu au niveau du récepteur. Ce récepteur est basé sur une approche de continuation, et exploite l'idée d'approcher une fonction originale de coût d'optimisation par une suite de fonctions plus dociles et donc de réduire la complexité de calcul au récepteur.En second lieu, en vue de la détection de données sous un canal dynamique linéaire, lorsque le canal est inconnu au niveau du récepteur, le récepteur doit être en mesure d'effectuer conjointement l'égalisation et l'estimation de canal. De cette manière, on formule une représentation de modèle état-espace combiné du système de communication. Par cette représentation, nous pouvons utiliser le filltre de Kalman comme le meilleur estimateur des paramètres du canal. Le but de cette section est de motiver de façon rigoureuse la mise en place du filltre de Kalman dans l'estimation des sequences de Markov par des canaux dynamiques Gaussien. Par la présente, nous interprétons et explicitons les approximations sous-jacentes dans les approaches heuristiques.Enfin, si nous considérons une approche plus générale pour le canal dynamique non linéaire, nous ne pouvons pas utiliser le filtre de Kalman comme le meilleur estimateur. Ici, nous utilisons des modèles commutation d’espace-état (SSSM) comme modèles espace-état non linéaires. Ce modèle combine le modèle de Markov caché (HMM) et le modèle espace-état linéaire (LSSM). Pour l'estimation de canal et la detection de données, l'approche espérance et maximisation (EM) est utilisée comme approche naturelle. De cette façon, le filtre de Kalman étendu (EKF) et les filtres à particules sont évités. / The aim of this thesis is to study the problem of data detection in wireless communication system, for both case of perfect and imperfect channel state information at the receiver. As well known, the complexity of MLSE being exponential in the channel memory and in the symbol alphabet cardinality is quickly unmanageable and forces to resort to sub-optimal approaches. Therefore, first we propose a new iterative equalizer when the channel is unknown at the transmitter and perfectly known at the receiver. This receiver is based on continuation approach, and exploits the idea of approaching an original optimization cost function by a sequence of more tractable functions and thus reduce the receiver's computational complexity. Second, in order to data detection under linear dynamic channel, when the channel is unknown at the receiver, the receiver must be able to perform joint equalization and channel estimation. In this way, we formulate a combined state-space model representation of the communication system. By this representation, we can use the Kalman filter as the best estimator for the channel parameters. The aim in this section is to motivate rigorously the introduction of the Kalman filter in the estimation of Markov sequences through Gaussian dynamical channels. By this we interpret and make clearer the underlying approximations in the heuristic approaches. Finally, if we consider more general approach for non linear dynamic channel, we can not use the Kalman filter as the best estimator. Here, we use switching state-space model (SSSM) as non linear state-space model. This model combines the hidden Markov model (HMM) and linear state-space model (LSSM). In order to channel estimation and data detection, the expectation and maximization (EM) procedure is used as the natural approach. In this way extended Kalman filter (EKF) and particle filters are avoided.