Unmanned aerial vehicle technology is rapidly maturing. In recent years, the sight of hobbyist aircraft has become more common. Corporations and governments are also interested in using drone aircraft for applications such as package delivery, surveillance and communications. These autonomous UAV technologies demand robust systems that perform under any circumstances. Many UAV applications rely on GPS to obtain information about their location and velocity. However, the GPS system has known vulnerabilities, including environmental signal degradation, terrestrial or solar weather, or malicious attacks such as GPS spoofing. These conditions occur with enough frequency to cause concern. Without a GPS signal, the state estimation in many autopilots quickly degrades. In the absence of a reliable backup navigation scheme, this loss of state will cause the aircraft to drift off course, and in many cases the aircraft will lose power or crash. While no single approach can solve all of the issues with GPS signal degradation, individual events can be addressed and solved. In this thesis, we present a system which will return an aircraft to its launch point upon the loss of GPS. This functionality is advantageous because it allows recovery of the UAV in circumstances which the lack of GPS information would make difficult. The system presented in this thesis accomplishes the return of the aircraft by means of onboard visual navigation, which removes the dependence of the aircraft on external sensors and systems. The system presented here uses an downward-facing onboard camera and computer to capture a string of overlapping images (keyframes) of the ground as the aircraft travels on its outbound journey. When a signal is received, the aircraft switches into return-to-home mode. The system uses the homography matrix and other vision processing techniques to produce information about the location of the current keyframe relative to the aircraft. This information is used to navigate the aircraft to the location of each saved keyframe in reverse order. As each keyframe is reached, the system programmatically loads the next target keyframe. By following the chain of keyframes in reverse, the system reaches the launch location. Contributions in this thesis include the return-to-home visual flight system for UAVs, which has been tested in simulation and with flight tests. Features of this system include methods for determining new keyframes and switching keyframes on the inbound flight, extracting data between images, and flight navigation based on this information. This system is a piece of the wider GPS-denied framework under development in the BYU MAGICC lab.

Submitted by Lara Oliveira (lara@ufersa.edu.br) on 2017-06-27T20:40:29Z No. of bitstreams: 1 FláviaEFD_DISSERT.pdf: 4838703 bytes, checksum: cb75ca0f24038306bbea559a45db472e (MD5) / Rejected by Vanessa Christiane (referencia@ufersa.edu.br), reason: Corrigir referência on 2017-07-03T12:20:16Z (GMT) / Submitted by Lara Oliveira (lara@ufersa.edu.br) on 2017-07-04T16:12:28Z No. of bitstreams: 1 FláviaEFD_DISSERT.pdf: 4838703 bytes, checksum: cb75ca0f24038306bbea559a45db472e (MD5) / Approved for entry into archive by Vanessa Christiane (referencia@ufersa.edu.br) on 2017-07-04T16:56:52Z (GMT) No. of bitstreams: 1 FláviaEFD_DISSERT.pdf: 4838703 bytes, checksum: cb75ca0f24038306bbea559a45db472e (MD5) / Approved for entry into archive by Vanessa Christiane (referencia@ufersa.edu.br) on 2017-07-04T17:02:49Z (GMT) No. of bitstreams: 1 FláviaEFD_DISSERT.pdf: 4838703 bytes, checksum: cb75ca0f24038306bbea559a45db472e (MD5) / Made available in DSpace on 2017-07-04T17:02:57Z (GMT). No. of bitstreams: 1 FláviaEFD_DISSERT.pdf: 4838703 bytes, checksum: cb75ca0f24038306bbea559a45db472e (MD5) Previous issue date: 2017-02-17 / Research on Unmanned Aerial Vehicles (UAVs) has been intensified since the 2000s with the aim of replacing manned vehicles. Its maneuverability makes it capable of handling various types of applications such as surveillance of a particular area, inspection of structures, in difficult to access environments, among others. When the first research began, this type of aerial vehicle was only used for military applications, but at the moment they are studied for other applications; the studies focus on control techniques for stability and flight autonomy. This work aimed the development of an altitude control and attitude of a UAV type quadrotor; the implementation was carried out on the Arduino platform and the flight tests indoors. Design of stability and height control, comparisons between two types of height control, PID (Proportional-Integral-Derivative) and Fuzzy in a Simulink® / MATLAB environment were performed / anos 2000, com o objetivo de substituir os veículos tripulados. Sua manobrabilidade o torna apto a lidar com diversos tipos de aplicações como vigilância de uma determinada área, inspeção de estruturas, em ambientes de difícil acesso, entre outros. Quando as primeiras pesquisas iniciaram, esse tipo de veículo aéreo era usado apenas para aplicações militares, mas atualmente são estudados para outras aplicações; os estudos se concentram em técnicas de controle para estabilidade e autonomia dos voos. Este trabalho objetiva o desenvolvimento de um controle de altitude e atitude de um VANT do tipo quadricóptero; a implementação foi realizada na plataforma Arduino e os testes de voo em ambientes fechados. Foi realizado o controle de estabilidade e de altura, comparações entre dois tipos de controle de altura, PID (Proporcional-Integral-Derivativo) e Fuzzy em ambiente Simulink®/MATLAB / 2017-06-27

Connected aviation, the Internet of Flying Things and related emerging technologies, such as the System-Wide Information Management infrastructure of the FAA NextGen program, present numerous opportunities for the aviation sector. The ubiquity of aeronautical, flight, weather, aerodrome, and maintenance data accelerates the development of smarter software systems to cope with the ever increasing requirements of the industry sector. The increasing amount, frequency and variety of real-time data available to modern air transport and tactical systems, and their crews, creates exciting new challenges and research opportunities. We present an architectural approach toward the vision of increasingly self-separating and self-governed flight operations within the bigger picture of an evolving set of future Autonomous Flight Rules. The challenges in this field of research are manifold and include autonomic airborne trajectory optimization, data sharing, fusion and information derivation, the incorporation of and communication with rational actors—both human and machine—via a connected aviation infrastructure, to facilitate smarter decision making and support while generating economical, environmental and tactical advantages. We developed a concept and prototype implementation of our Smart Autoflight Control System. The concept and implemented system follow the design principle of an Autonomic Element, consisting of an Autonomic Manager and its Managed Element, acting within an Autonomic Context. The Managed Element concept embraces an infrastructure featuring suitable models of manageable environments, airborne agents, planners, applicable operational cost and risk policies, and connections to the System-Wide Information Management cloud as well as to relevant rational actors, such as Air Traffic Control, Command and Control, Operations or Dispatch. The Autonomic Manager concept incorporates the extraction, that is, short-term sensing, of features from operational scenarios and the categorization of these scenarios according to their level of criticality and associated flight phase. The Autonomic Manager component, furthermore, continuously tunes, that is, actuates, manageable items of its Managed Element, such as environments and planners, and triggers competitions to assess their performance under the various extracted and dynamically changing features of their Autonomic Context. The performance reputations of the tuned manageable items are collected in a knowledge base and may serve as a long-term sensor. Both the managed items of the Managed Element as well the managing items of the Autonomic Manager are extendable and may realize very different paradigms, including deterministic, non-deterministic, heuristically guided, and biologically inspired approaches. We assessed the extensibility and maintainability of our Smart Autoflight Control System infrastructure by including manageable environments and planners of the Classical Grid Search, Probabilistic Roadmaps, and Rapidly-Exploring Random Trees families into its core component. Furthermore, we evaluated the viability of a simple heuristic and a more sophisticated Sequential Model-Based Algorithm Configuration Autonomic Manager to adaptively select and tune manageable planners of the supported families based on the extracted features from very simple to highly challenging scenarios. We were able to show that a self-adaptive approach, that heuristically tunes and selects the best performing planner following a performance competition, produces suitable flight trajectories within reasonable deliberation times. Additionally, we discovered options for improving our heuristic Autonomic Manager through a series of evaluation runs of the Sequential Model-Based Algorithm Configuration Autonomic Manager. Our contributions answer how the manageable items, that is, environments and planners, of our Smart Autoflight Control System core component have to be modified in order to embed System-Wide Information Management data that feature both spatial and temporal aspects. We show how operational cost and risk policies help to assess environments differently and plan suitable flight trajectories accordingly. We identify and implement the necessary extensions and capabilities that have to be supported by manageable and managing items, respectively, to enable continuous feature extraction, adaptive tuning, performance competitions, and planner selection in dynamic flight scenarios. / Graduate

UAV development and usage has increased dramatically in the last 15 years. In this time frame the potential has been realized for deployable UAVs to the extent that a new class of UAV was defined for these systems. Inflatable wing UAVs provide a unique solution for deployable UAVs because they are highly packable (some collapsing to 5-10% of their deployed volume) and have the potential for the incorporation of wing shaping. In this thesis, aerodynamic coefficients and aileron effectiveness were derived from the equations of motion of aircraft as necessary parameters for autonomous flight. A wind tunnel experiment was performed to determine the aerodynamic performance of a bumpy inflatable wing airfoil for comparison with the baseline smooth airfoil from which it was derived. Results showed that the bumpy airfoil has improved aerodynamics over the smooth airfoil at low-Re. The results were also used to create aerodynamic performance curves to supplement results of aerodynamic modeling with a smooth airfoil. A modeling process was then developed to calculate the aileron effectiveness of a wing shaping demonstrator aircraft. Successful autonomous flight tests were then performed with the demonstrator aircraft including in-flight aileron doublets to validate the predicted aileron effectiveness, which matched within 8%.

Heavy reliance on GPS is preventing unmanned air systems (UAS) from being fully inte- grated for many of their numerous applications. In the absence of GPS, GPS-reliant UAS have difficulty estimating vehicle states resulting in vehicle failures. Additionally, naively using erro- neous measurements when GPS is available can result in significant state inaccuracies. We present a simultaneous localization and mapping (SLAM) solution to GPS-degraded navigation that al- lows vehicle state estimation and control independent of global information. Optionally, a global map can be constructed from odometry measurements and can be updated with GPS measurements while maintaining robustness against outliers.We detail a relative navigation SLAM framework that distinguishes a relative front end and global back end. It decouples the front-end flight critical processes, such as state estimation and control, from back-end global map construction and optimization. Components of the front end function relative to a locally-established coordinate frame, completely independent from global state information. The approach maintains state estimation continuity in the absence of GPS mea- surements or when there are jumps in the global state, such as after map optimization. A global graph-based SLAM back end complements the relative front end by constructing and refining a global map using odometry measurements provided by the front end.Unlike typical approaches that use GPS in the front end to estimate global states, our unique back end uses a virtual zero and virtual constraint to allow intermittent GPS measurements to be applied directly to the map. Methods are presented to reduce the scale of GPS induced costs and refine the map’s initial orientation prior to optimization, both of which facilitate convergence to a globally consistent map. The approach uses a state-of-the-art robust least-squares optimization algorithm called dynamic covariance scaling (DCS) to identify and reject outlying GPS measure- ments and loop closures. We demonstrate our system’s ability to generate globally consistent and aligned maps in GPS-degraded environments through simulation, hand-carried, and flight test re- sults.

Traditionally, the task of determining aircraft position and attitude for automatic control has been handled by the combination of an inertial measurement unit (IMU) with a Global Positioning System (GPS) receiver. In this configuration, accelerations and angular rates from the IMU can be integrated forward in time, and position updates from the GPS can be used to bound the errors that result from this integration. However, reliance on the reception of GPS signals places artificial constraints on aircraft such as small unmanned aerial vehicles (UAVs) that are otherwise physically capable of operation in indoor, cluttered, or adversarial environments. Therefore, this work investigates methods for incorporating a monocular vision sensor into a standard avionics suite. Vision sensors possess the potential to extract information about the surrounding environment and determine the locations of features or points of interest. Having mapped out landmarks in an unknown environment, subsequent observations by the vision sensor can in turn be used to resolve aircraft position and orientation while continuing to map out new features. An extended Kalman filter framework for performing the tasks of vision-based mapping and navigation is presented. Feature points are detected in each image using a Harris corner detector, and these feature measurements are corresponded from frame to frame using a statistical Z-test. When GPS is available, sequential observations of a single landmark point allow the point's location in inertial space to be estimated. When GPS is not available, landmarks that have been sufficiently triangulated can be used for estimating vehicle position and attitude. Simulation and real-time flight test results for vision-based mapping and navigation are presented to demonstrate feasibility in real-time applications. These methods are then integrated into a practical framework for flight in GPS-denied environments and verified through the autonomous flight of a UAV during a loss-of-GPS scenario. The methodology is also extended to the application of vehicles equipped with stereo vision systems. This framework enables aircraft capable of hovering in place to maintain a bounded pose estimate indefinitely without drift during a GPS outage.