UAVs have gained in popularity and the number of applications has soared over the past years, ranging from leisure to commercial activities. This thesis is discussing specifically railroad applications, which is a domain rarely explored. Two different aspects are analysed. While developing a new application or migrating a ground-based system to UAV platform, the different challenges encountered are often unknown. Therefore, this thesis highlights the most important ones to take into consideration during the development process. From a more technical aspect, the implementation of autonomous guidance for UAVs over railroads using vanishing point extraction is studied. Two different algorithms are presented and compared, the first one is using line extraction method whereas the second uses joint activities of Gabor filters. The results demonstrate that the applied methodologies provide good results and that a significant difference exists between both algorithms in terms of computation time. A second implementation tackling the detection of railway topologies to enable the use on multiple rail road configurations is discussed. A first technique is presented using exclusively vanishing points for the detection, however, the results for complex images are not satisfactory. Therefore, a second method is studied using line characteristics on top of the previous algorithm. This second implementation has proven to give good results.

Approved for public release, distribution is unlimited / A control system was developed for autonomous visual tracking of a stationary target using a small unmanned aerial vehicle. The kinematic equations of this problem were developed, and the insight obtained from examination was applied in developing controllers for the system. This control system controlled the orientation of the camera to keep it constantly pointing at the target, and also controlled the trajectory of the aircraft in flight around the target. The initial control law that was developed drives the aircraft trajectory to a constant radius around the target. The range to the target is not directly measurable, so it was estimated using steady state Kalman filters. Once a range estimate is obtained, it is used to control the range to the target, and the aircraft trajectory is driven toward a circle with a specified radius. Initial tests of the control system with Simulink simulations have shown good performance of the control system. Further testing with hardware will be conducted, and flight tests are scheduled to be conducted in the near future. Conclusions are drawn and recommendations for further study are presented. / Ensign, United States Navy

Cette thèse présente l'implémentation d'une stratégie visuelle bio-inspirée sur un aéroglisseur miniature totalement actionné, qui lui permet de naviguer dans le plan horizontal d'un tunnel inconnu. L'élaboration de ce pilote automatique, nommé LORA, fait suite aux études éthologiques menées sur l'abeille depuis ces dernières décennies et nous ont amené à énoncer le principe de la régulation du flux optique pour le contrôle du vol de croisière. Ce pilote automatique est un double régulateur de flux optique latéral constitué de deux boucles visuo-motrices interdépendantes contrôlant conjointement la vitesse d'avance et la position du robot par rapport aux obstacles sans avoir à mesurer ou estimer aucun de ces paramètres. La clé de voûte de ce système de guidage est une troisième boucle destinée à maintenir le cap grâce à un micro-gyromètre et un micro-compas magnétique permettant au robot d'effectuer des mouvements de translation qui génèrent sur son œil composé artificiel du flux optique de translation, seul dépendant du ratio vitesse/distance aux obstacles. Cet œil estime le flux optique grâce à ses deux ou quatre Détecteurs élémentaires de mouvement (total de 4 ou 8 pixels). L'aéroglisseur est alors capable de franchir sans collision, à la manière d'une abeille, divers tunnels : droit, fuselé ou présentant une pente, un virage, une absence de texture sur un mur ou même une zone non-stationnaire. Cette stratégie visuelle bio-inspirée fournit non seulement une solution de navigation élégante à destination de robots totalement actionnés mais elle permet aussi d'expliquer comment une abeille de 100mg peut naviguer sans l'aide de SONAR, RADAR, LIDAR, ou GPS. / In this work, we present for the first time a bio-inspired motion vision-based navigation strategy embedded on a miniature fully-actuated hovercraft allowing it to navigate safely on the horizontal plane of an unknown corridor. The design of this autopilot, called LORA, follows the ethological findings made on honeybees these last decades, which led us to elaborate the principle of the optic flow regulation which might be used by insects to control their flight. The bee-inspired LORA autopilot is a dual optic flow regulator which consists in two intertwined visuomotor feedback loops which control jointly the forward speed of the robot and its clearance to the obstacles. The keystone of this bio-inspired guidance system is a heading-lock system enabling the robot to move in translations and therefore experience a purely translational optic flow which depends only on the ratio speed/clearance to obstacles thanks to a micro-gyrometer and a micro-magnetic compass. The estimation of optic flow is made by a minimalist compound eye, made of two or four Elementary Motion Detectors (only 4 or 8 pixels). The hovercraft is therefore able to cross without crashing a straight or a tapered corridor, presenting a frontal sloping terrain, a bend, a textureless wall, or even a non-stationary section by automatically adapting both its forward speed and its clearance to the walls imitating the honeybee. This bio-inspired visual strategy not only provides an elegant navigation solution in an unknown environment aimed to equip fully-actuated miniature vehicles but also to explain how a 100mg honeybee can navigate with few computational ressources, i.e., without any SONAR, RADAR, LIDAR or GPS.

This thesis presents the initial components of an integrated guidance, navigation, and control system for vertical take-off and landing (VTOL) autonomous unmanned aerial vehicles (UAVs) such that they may map complex environments that may be hostile. The first part of this thesis presents an autonomous guidance system. For goal selection, the map is partitioned around the presence of obstacles and whether that area has been explored. To perform this partitioning, the Octree algorithm is implemented. In this thesis, we test this algorithm to find a parameter set that optimizes this algorithm. Having selected goal points, we perform a comparison of the LPA* and A* path planning algorithms with a custom heuristic that enables reckless or tactical maneuvers as the UAV maps the environment. For trajectory planning, the fMPC algorithm is applied to the feedback-linearized equations of motion of a quadcopter. For collision avoidance, standalone versions of 4 different constraint generation algorithms are evaluated to compare their resulting computation times, accuracy, and computed volume on a voxel map that simulates a 2-story house along with fixed paths that vary in length at fixed intervals as basis of tests. The second part of this thesis presents the theory of Model Reference Adaptive Control(MRAC) along with augmentation for output signal tracking and switched-dynamic systems. We then detail the development of longitudinal and lateral controllers a Quad-Rotor Tailsitter(QRBP) style UAV. In order to successfully implement the proposed controller on the QRBP, significant effort was placed upon physical design and testing apparatus. / Master of Science / For an autonomously operated, Unmanned Aerial Vehicle (UAV), to operate, it requires a guidance system to determine where and how to go, and a control system to effectively actuate the guidance system's commands. In this thesis, we detail the characterization and optimization of the algorithms comprising the guidance system. We then delve into the theory of MRAC and apply it toward a control system for a QRBP. We then detail additional tools developed to support the testing of the QRBP.