Numerical weather modeling can benefit from improved wind sensing in the Earth's atmospheric boundary layer (ABL). Small, low-cost, uncrewed aircraft (drones) can be used to measure wind and a distribution of these vehicles could potentially provide measurements with much greater density and resolution, in both space and time, than current methods allow. To measure wind, a drone could be equipped with dedicated wind-measuring sensors, although these can be costly and obtrusive and must be carefully calibrated to account for interference effects. State estimation algorithms that combine a drone's operational measurements with a flight dynamic model can be used to infer wind without a dedicated wind sensor, although the sensor quality affects measurement accuracy. Previous studies have explored the effects of various sensors on wind estimate accuracy, but the effect of flight dynamic model fidelity has received less attention. This dissertation presents analysis of different aerodynamic model-free and model-based wind estimation methods, comparing six wind estimation formulations using experimental flight data from a small, fixed-wing aircraft. Each formulation is implemented using a Kalman filter, an extended Kalman filter, and an unscented Kalman filter. These filters are designed based on different assumptions related to the flight dynamic model, available sensors, and available measurements. Having identified a promising estimation approach, the dissertation next explores the value of incorporating unsteady effects into a flight dynamic model for model-based wind estimation. An unsteady aerodynamic model for a small, fixed-wing aircraft is developed, identified, and validated using experimental flight data. An extended Kalman filter is then designed and implemented for two motion models -- one that includes unsteady effects and another that does not. Analysis of the wind estimates and the estimation differences show that, while the unsteady flight dynamic model better predicts the aircraft motion, the value of incorporating this model for wind estimation is questionable. / Doctor of Philosophy / Wind velocity sensing is crucial to understanding the meteorological processes at low altitudes. The integration of low-cost drones has allowed them to be used as wind-sensing platforms. This is achieved by equipping small drones with dedicated wind-measuring sensors, often costly and infeasible, or inferring wind velocity from the drone's motion. Algorithms designed to infer wind can be used by combining onboard flight sensor measurements with a drone's flight dynamic model to infer wind. However, low-cost drones are usually equipped with low-cost flight sensors, which frequently lead to higher measurement uncertainty and degrade the accuracy of wind estimates. Previous studies have explored the effects of various sensors on wind estimates, but errors due to low-fidelity dynamic models have received less attention. This dissertation first presents a detailed analysis of different flight dynamic model-free and model-based wind estimation methods. It compares six wind estimation formulations. Each formulation is implemented in wind inferring algorithms called a Kalman filter, an extended Kalman filter, and an unscented Kalman filter. These algorithms are designed based on different assumptions related to the flight dynamic model, available flight sensors, and available measurements. Secondly, the value of incorporating a fixed-wing, unsteady flight dynamic model in a wind estimation scheme is analyzed. To this end, an unsteady flight dynamic model for a fixed-wing drone is developed, identified, and validated from data acquired from the drone's flight history. Furthermore, an extended Kalman filter is designed and implemented for two motion models -- one that includes unsteady effects and another that does not. The analysis of the time histories of the wind estimates and the wind estimate differences show that both model-based estimators perform equally well.

The development and implementation of a flight test program for an unmanned aircraft is a multidisciplinary challenge. This thesis presents the development and implementation of a rigorous test program for the flight test of a Geometrically Scaled Joined Wing SensorCraft Remotely Piloted Vehicle from concept through successful flight test. The design methodology utilized in the development of the test program is presented, along with the extensive formal review process required for the approval of the test plan by the Air Force Research Laboratory. The design, development and calibration of a custom instrumentation package is also presented along with the setup, procedure and results from all testing. Results are presented for a wind tunnel test for air data boom calibration, propulsion system static thrust testing, a bifilar pendulum test for experimental calculation of mass moments of inertia, a static structural loading test for structural design validation, a full taxi test and a successful first flight. / Master of Science

The problem addressed in this research is the in-flight generation of trajectories for autonomous unmanned aircraft, which requires a method of generating pseudo-optimal trajectories in near-real-time, on-board the aircraft, and without external intervention. The focus of this research is the enhancement of a particular inverse dynamics direct method that is a candidate solution to the problem. This research introduces the following contributions to the method. A quaternion-based inverse dynamics model is introduced that represents all orientations without singularities, permits smooth interpolation of orientations, and generates more accurate controls than the previous Euler-angle model. Algorithmic modifications are introduced that: overcome singularities arising from parameterization and discretization; combine analytic and finite difference expressions to improve the accuracy of controls and constraints; remove roll ill-conditioning when the normal load factor is near zero, and extend the method to handle negative-g orientations. It is also shown in this research that quadratic interpolation improves the accuracy and speed of constraint evaluation. The method is known to lead to a multimodal constrained nonlinear optimization problem. The performance of the method with four nonlinear programming algorithms was investigated: a differential evolution algorithm was found to be capable of over 99% successful convergence, to generate solutions with better optimality than the quasi- Newton and derivative-free algorithms against which it was tested, but to be up to an order of magnitude slower than those algorithms. The effects of the degree and form of polynomial airspeed parameterization on optimization performance were investigated, and results were obtained that quantify the achievable optimality as a function of the parameterization degree. Overall, it was found that the method is a potentially viable method of on-board near- real-time trajectory generation for unmanned aircraft but for this potential to be realized in practice further improvements in computational speed are desirable. Candidate optimization strategies are identified for future research.

Crop improvement is necessary for food security as the global population is expected to exceed 9 billion by 2050. Limitations in water resources and more frequent droughts and floods will make it increasingly difficult to manage agricultural resources and increase yields. Therefore, we must improve our ability to monitor agronomic research plots and use the information they provide to predict impacts of moisture stress on crop growth and yield. Towards this end, agronomists have used reductions in leaf expansion rates as a visible ‘plant-based’ indicator of moisture stress. Also, modeling researchers have developed crop models such as AquaCrop to enable quantification of the severity of moisture stress and its impacts on crop growth and yield. Finally, breeders are using Unmanned Aircraft Systems (UAS) in field-based High-Throughput Phenotyping (HTP) to quickly screen large numbers of small agronomic research plots for traits indicative of drought and flood tolerance. Here we investigate whether soybean canopy cover and color time series from high-resolution UAS ortho-images can be collected with enough spatial and temporal resolution to accurately quantify and differentiate agronomic research plots, pinpoint the timing of the onset of moisture stress, and constrain crop models such as AquaCrop to more accurately simulate the timing and severity of moisture stress as well as its impacts on crop growth and yield. We find that canopy cover time series derived from multilayer UAS image ortho-mosaics can reliably differentiate agronomic research plots and pinpoint the timing of reductions in soybean canopy expansion rates to within a couple of days. This information can be used to constrain the timing of the onset of moisture stress in AquaCrop resulting in a more realistic simulation of moisture stress and a lower likelihood of underestimating moisture stress and overestimating yield. These capabilities will help agronomists, crop modelers, and breeders more quickly develop varieties tolerant to moisture stress and achieve food security.

Emergency landing is a critical safety mechanism for aerial vehicles. Commercial aircraft have triply-redundant systems that greatly increase the probability that the pilot will be able to land the aircraft at a designated airfield in the event of an emergency. In general aviation, the chances of always reaching a designated airfield are lower, but the successful pilot might use landmarks and other visual information to safely land in unprepared locations. For small unmanned aircraft systems (sUAS), triply- or even doubly-redundant systems are unlikely due to size, weight, and power constraints. Additionally, there is a growing demand for beyond visual line of sight (BVLOS) operations, where an sUAS operator would be unable to guide the vehicle safely to the ground. This thesis presents a machine vision-based approach to emergency landing for small unmanned aircraft systems. In the event of an emergency, the vehicle uses a pre-compiled database of potential landing sites to select the most accessible location to land based on vehicle health. Because it is impossible to know the current state of any ground environment, a camera is used for real-time visual feedback. Using the recently developed Recursive-RANSAC algorithm, an arbitrary number of moving ground obstacles can be visually detected and tracked. If obstacles are present in the selected ditch site, the emergency landing system chooses a new ditch site to mitigate risk. This system is called Safe2Ditch.

<p>A dual core Atmel DIOPSIS 940 chip consists of a DSP and an ARM9 functional units in a single silicon die. This thesis presents the process of integration and assessment of using this processor in a flight control system. A complete design of the system is provided including a description of the DIOPSIS 940 from the perspective of requirements of the application. The integration of the processor with a typical set of components of a flight control system is provided. Additionally, a suite of programs required for developing software for the system is included. Capabilities of both cores of the processor are analysed in a series of experiments. Computational performance in typical tasks of a flight control system is analyzed and compared. The application of attitude stabilization for a micro-scale UAS is described.</p>

The problem addressed in this research is the in-flight generation of trajectories for autonomous unmanned aircraft, which requires a method of generating pseudo-optimal trajectories in near-real-time, on-board the aircraft, and without external intervention. The focus of this research is the enhancement of a particular inverse dynamics direct method that is a candidate solution to the problem. This research introduces the following contributions to the method. A quaternion-based inverse dynamics model is introduced that represents all orientations without singularities, permits smooth interpolation of orientations, and generates more accurate controls than the previous Euler-angle model. Algorithmic modifications are introduced that: overcome singularities arising from parameterization and discretization; combine analytic and finite difference expressions to improve the accuracy of controls and constraints; remove roll ill-conditioning when the normal load factor is near zero, and extend the method to handle negative-g orientations. It is also shown in this research that quadratic interpolation improves the accuracy and speed of constraint evaluation. The method is known to lead to a multimodal constrained nonlinear optimization problem. The performance of the method with four nonlinear programming algorithms was investigated: a differential evolution algorithm was found to be capable of over 99% successful convergence, to generate solutions with better optimality than the quasi- Newton and derivative-free algorithms against which it was tested, but to be up to an order of magnitude slower than those algorithms. The effects of the degree and form of polynomial airspeed parameterization on optimization performance were investigated, and results were obtained that quantify the achievable optimality as a function of the parameterization degree. Overall, it was found that the method is a potentially viable method of on-board near- real-time trajectory generation for unmanned aircraft but for this potential to be realized in practice further improvements in computational speed are desirable. Candidate optimization strategies are identified for future research.

The aim of this master’s thesis is to summarize legislation dealing with UAVs and to create structure of trainings for UAV pilots. First part of this thesis is generally about UAVs, next one deals with UAV pilot training and the last part deals with future development of different areas in unmanned aircraft industry.

This thesis focuses explores the laws for drone use in the real estate practice. In each chapter, the legislation for the commercial use of drones is described for the following states - the Czech Republic, Slovakia, France, Belgium, the Great Britain and the United States of America. In the final chapter there is a comparison of different law aspects from all studied countries.

Advances in technology have given rise to the widespread use of small unmanned aerial systems (sUAS), more commonly known as ‘drones.’ The sUAS market is expected to continue to increase at a rapid pace, with the FAA forecasting around 8,000 registrations monthly (FAA, 2019). High profile drone incidents include use in an attack on the Venezuelan president, an undetected landing on the property of the White House, and use in dropping crude explosives on troops in the Middle East (Gramer, 2017; Grossman, 2018; Wallace & Loffi, 2015). The rate of proliferation and high-performance characteristics of these drones has raised serious concerns for safety in high-density outdoor events. Counter-unmanned aerial systems are currently illegal for all but a few Federal entities within the U.S., leaving private and public entities at risk. This exploratory research investigates several legal facility and patron behavioral interventions to reduce possible casualties during a drone attack by using AnyLogic simulation modeling in an amusement park scenario. Data from this experiment suggest that behavioral interventions implemented 30 seconds before a drone attack can reduce casualties by more than 55%, and up to 62% casualty reductions can be realized with a 60-second implementation time. Testing suggests that venue design considerations, such as a reduction in hard corners, covered high-density areas, and smoother area transitions can synergistically reduce casualties when used in conjunction with a warning system. While casualty mitigation did occur throughout the study, active threat interdiction methods would be necessary to design a system that may prevent casualties overall.