The objective of this thesis is to design a relative navigation and guidance system for unmanned aerial vehicles (UAVs) for vision-based control applications. The vision-based navigation, guidance and control has been one of the most focused on research topics for the automation of UAVs. This is because in nature, birds and insects use vision as the exclusive sensor for object detection and navigation. In particular, this thesis studies the monocular vision-based navigation and guidance. Since 2-D vision-based measurements are nonlinear with respect to the 3-D relative states, an extended Kalman filter (EKF) is applied in the navigation system design. The EKF-based navigation system is integrated with a real-time image processing algorithm and is tested in simulations and flight tests. The first closed-loop vision-based formation flight has been achieved. In addition, vision-based 3-D terrain recovery was performed in simulations. A vision-based obstacle avoidance problem is specially addressed in this thesis. A navigation and guidance system is designed for a UAV to achieve a mission of waypoint tracking while avoiding unforeseen stationary obstacles by using vision information. A 3-D collision criterion is established by using a collision-cone approach. A minimum-effort guidance (MEG) law is applied for a guidance design, and it is shown that the control effort can be reduced by using the MEG-based guidance instead of a conventional guidance law. The system is evaluated in a 6 DoF flight simulation and also in a flight test. For monocular vision-based control problems, vision-based estimation performance highly depends on the relative motion of the vehicle with respect to the target. Therefore, this thesis aims to derive an optimal guidance law to achieve a given mission under the condition of using the EKF-based relative navigation. Stochastic optimization is formulated to minimize the expected cost including the guidance error and the control effort. A suboptimal guidance law is derived based on an idea of the one-step-ahead (OSA) optimization. Simulation results show that the suggested guidance law significantly improves the guidance performance. Furthermore, the OSA optimization is generalized as the n-step-ahead optimization for an arbitrary number of n, and their optimality and computational cost are investigated.

The conventional Mini and Large scale Unmanned Aerial Vehicle systems span anywhere from approximately 12 inches to 12 feet; endowing them with larger propulsion systems, batteries/fuel-tanks, which in turn provide ample power reserves for long-endurance flights, powerful actuators, on-board avionics, wireless telemetry etc. The limitations thus imposed become apparent when shifting to Micro Aerial Vehicles (MAVs) and trying to equip them with equal or near-equal flight endurance, processing, sensing and communication capabilities, as their larger scale cousins. The conventional MAV as outlined by The Defense Advanced Research Projects Agency (DARPA) is a vehicle that can have a maximum dimension of 6 inches and weighs no more than 100 grams. Under these tight constraints, the footprint, weight and power reserves available to on-board avionics and actuators is drastically reduced; the flight time and payload capability of MAVs take a massive plummet in keeping with these stringent size constraints. However, the demand for micro flying robots is increasing rapidly. The applications that have emerged over the years for MAVs include search&rescue operations for trapped victims in natural disaster succumbed urban areas; search&reconnaissance in biological, radiation, natural disaster/hazard succumbed/prone areas; patrolling&securing home/office/building premises/urban areas. VTOL capable rotary and fixed wing flying vehicles do not scale down to micro sized levels, owing to the severe loss in aerodynamic efficiency associated with low Reynolds number physics on conventional airfoils; whereas, present state of the art in flapping wing designs lack in one or more of the minimum qualities required from an MAV: Appreciable flight time, appreciable payload capacity for on-board sensors/telemetry and 6DoF hovering/VTOL performance. This PhD. work is directed towards overcoming these limitations. Firstly, this PhD thesis presents the advent of a novel Quad-Wing MAV configuration (called the QV). The Four-Wing configuration is capable of performing all 6DoF flight maneuvers including VTOL. The thesis presents the design, conception, simulation study and finally hardware design/development of the MAV. Secondly, this PhD thesis proves and demonstrates significant improvement in on-board Energy-Harvesting resulting in increased flight times and payload capacities of the order of even 200%-400% and more. Thirdly, this PhD thesis defines a new actuation principle called, Fixed Frequency, Variable Amplitude (FiFVA). It is demonstrated that by the use of passive elastic members on wing joints, a further significant increase in energy efficiency and consequently reduction in input power requirements is observed. An actuation efficiency increase of over 100% in many cases is possible. The natural evolution of actuation development led to invention of two novel actuation systems to illustrate the FiFVA actuation principle and consequently show energy savings and flapping efficiency improvement. Lastly, but not in the least, the PhD thesis presents supplementary work in the design, development of two novel Micro Architecture and Control (MARC) avionics platforms (autopilots) for the application of demonstrating flight control and communication capability on-board the Four-Wing Flapping prototype. The design of a novel passive feathering mechanism aimed to improve lift/thrust performance of flapping motion is also presented.

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.

Permafrost is a variable in Antarctic terrestrial ecosystems, and the role it plays in the cryosphere is not well understood. There is much still to be learnt about the thermal state, physical properties, thickness and age of permafrost in Western Dronning Maud Land (WDML). Active layer dynamics and observed change over time have the potential to improve our knowledge of climate change. Understanding the effects of a warming climate on permafrost can also be of benefit to infrastructure, especially in areas with a large amount of frozen ground such as Scandinavia, Canada and Russia. Active layer and permafrost dynamics of WDML, Antarctica, are presented and discussed using data from six study sites, namely the Robertskollen, Vesleskarvet, Flarjuven, Grunehogna, Slettjfell nunataks and the Troll research station in the Jutulsessen area. Ground and ambient air temperature, as well as ground moisture data were collected for each site. An inventory of active layer and permafrost landforms was compiled, as were the frequency of cycles over the zero-degree isotherm, and the depth of the active layer. Furthermore, 3D models, geo-referenced maps and Digital Elevation Models were created of study areas with the use of an Unmanned Aerial Vehicle (UAV). Polygonal features are the most common landscape feature and are common to most of the study sites. Robertskollen has the deepest active layer at over 66cm and Slettfjell the shallowest at 9cm. A maximum recorded air temperature of 8.76°C (10/11/2014) occurred at Troll with the second highest maximum of 6.77°C (22/12/2010) recorded at Vesleskarvet. Robertskollen has the highest observable biological growth and a maximum recorded ground temperature of 22.84°C (10/01/2014). Troll and Valterkulten, registered the second and third highest ground temperatures respectively. The high ground Temperature observed for Robertskollen may be ascribed to it being the lowest altitude site. The highest number of cycles over the zero-degree isotherm was observed at Troll (11.01%), followed by Robertskollen (10.99%). For relatively warm areas, such as Robertskollen it is recommended that two metre borehole loggers are installed in order to capture a detailed understanding of the active layer. The UAV proved to be a beneficial tool for capturing aerial photographs for post fieldwork analysis and 3D modelling.

<p>The use of drones, also called unmanned aerial vehicles (UAVs), is increasing every day. These aircraft are piloted either remotely by a human pilot or completely autonomously by an on-board computer. UAVs are typically equipped with a video camera providing a live video feed to the operator. While they were originally developed mainly for military purposes, many civil applications start to emerge as they become more affordable.<p><p><p>Micro air vehicles are a subgroup of UAVs with a size and weight limitation; many are designed also for indoor use. Designs with rotary wings are generally preferred over fixed wings as they can take off vertically and operate at low speeds or even hover. At small scales, designs with flapping wings are being explored to try to mimic the exceptional flight capabilities of birds and insects. <p><p><p>The objective of this thesis is to develop a control mechanism for a robotic hummingbird, a bio-inspired tail-less hovering flapping wing MAV. The mechanism should generate moments necessary for flight stabilization and steering by an independent control of flapping motion of each wing.<p><p><p>The theoretical part of this work uses a quasi-steady modelling approach to approximate the flapping wing aerodynamics. The model is linearised and further reduced to study the flight stability near hovering, identify the wing motion parameters suitable for control and finally design a flight controller. Validity of this approach is demonstrated by simulations with the original, non-linear mathematical model.<p><p><p>A robotic hummingbird prototype is developed in the second, practical part. Details are given on the flapping linkage mechanism and wing design, together with tests performed on a custom built force balance and with a high speed camera. Finally, two possible control mechanisms are proposed: the first one is based on wing twist modulation via wing root bars flexing; the second modulates the flapping amplitude and offset via flapping mechanism joint displacements. The performance of the control mechanism prototypes is demonstrated experimentally. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

The complexity of the software behind autonomous systems is rapidly growing, as are the applications of what they can do. It is not unusual for the lines of code to reach the millions, which adds to the verification challenge. The machine learning algorithms involved are often "black boxes" where the precise workings are not known by the developer applying them, and their behavior is undefined when encountering an untrained scenario. With so much code, the possibility of bugs or malicious code is considerable. An approach is developed to monitor and possibly override the behavior of autonomous systems independent of the software controlling them. Application-isolated safety monitors are implemented in configurable hardware to ensure that the behavior of an autonomous system is limited to what is intended. The sensor inputs may be shared with the software, but the output from the monitors is only engaged when the system violates its prescribed behavior. For each specific rule the system is expected to follow, a monitor is present processing the relevant sensor information. The behavior is defined in linear temporal logic (LTL) and the associated monitors are implemented in a field programmable gate array (FPGA). An off-the-shelf drone is used to demonstrate the effectiveness of the monitors without any physical modifications to the drone. Upon detection of a violation, appropriate corrective actions are persistently enforced on the autonomous system. / Master of Science / Autonomous systems are surprisingly vulnerable, not just from malicious hackers, but from design errors and oversights. The lines of code required can quickly climb into the millions, and the artificial decision algorithms can be inscrutable and fully dependent upon the information they are trained on. These factors cause the verification of the core software running our autonomous cars, drones, and everything else to be prohibitively difficult by traditional means. Independent safety monitors are implemented to provide internal oversight for these autonomous systems. A semi-automatic design process efficiently creates error-free monitors from safety rules drones need to follow. These monitors remain separate and isolated from the software typically controlling the system, but use the same sensor information. They are embedded in the circuitry and act as their own small, task-specific processors watching to make sure a particular rule is not violated; otherwise, they take control of the system and force corrective behavior. The monitors are added to a consumer off-the-shelf (COTS) drone to demonstrate their effectiveness. For every rule monitored, an override is triggered when they are violated. Their effectiveness depends on reliable sensor information as with any electronic component, and the completeness of the rules detailing these monitors.

The atmospheric boundary layer (ABL) is the layer of air directly influenced by the Earth’s surface and is the layer of the atmosphere most important to humans as this is the air we live in. Methods for measuring the properties of the ABL include three general approaches: satellite-based, ground- based and airborne. A major research challenge is that many contemporary methods provide a restricted spatial resolution or coverage of variations of ABL properties such as how wind speed varies across a landscape with complex topography. To enhance our capacity to measure the properties of the ABL, this thesis presents a new technique that involves a small unmanned aircraft system (sUAS) equipped with a customized payload for measuring wind speed and particulate matter. The research presented herein outlines two key phases in establishing the proof-of-concept of the payload and its integration on the sUAS: (1) design and testing and (2) field demonstration. The first project focuses on measuring wind speed, which has been measured with fixed wing sUASs in previous research, but not with a helicopter sUAS. The second project focuses on the measurement of particulate matter, which is a major air pollutant typically measured with ground- based sensors. Results from both proof-of-concept projects suggest that ABL research could benefit from the proposed techniques.