Letecká fotogrammetrie v oblasti bezpilotních systémů představuje rychle rozvíjející se obor nalézající uplatnění napříč nejen průmyslovými odvětvími. Široce rozšířená metoda nepřímého georeferencování založená na vlícovacích bodech sice dosahuje vysoké přesnosti a spolehlivosti, v některých speciálních aplikacích nicméně není použitelná. Tato disertační práce se zabývá vývojem senzorického systému pro přímé georeferencování aplikovatelného na malých bezpilotních prostředcích a dále také návrhem vhodných kalibračních metod a testováním přesnosti. Významná část práce je věnována novým oblastem, kde může navržený systém pomoci eliminovat bezpečnostní rizika spojená s daným prostředím. V tomto kontextu byl systém testován v reálných podmínkách při mapování sněhu v horských oblastech a při robotickém mapování radiace.

Small unmanned aerial vehicles (sUAV) can produce valuable data for inspections, topography, mapping, and 3D modeling of structures. Used by multiple industries, sUAV can help inspect and study geographic and structural sites. Typically, the sUAV and camera specifications require optimal conditions with known geography and fly pre-determined flight paths. However, if the environment changes, new undetectable aerial hazards may intersect new flight paths. This makes it difficult to construct autonomous flight path missions that are safe in post-hazard areas where the flight paths are based on previously built models or previously known terrain details. The goal of this research is to make it possible for an unskilled pilot to obtain high quality images at key angles which will facilitate the inspections of dangerous environments affected by natural disasters through the construction of accurate 3D models. An iterative process with converging variables can circumvent the current deficit in flying UAVs autonomously and make it possible for an unskilled pilot to gather high quality data for the construction of photogrammetric models. This can be achieved by gaining preliminary photogrammetric data, then creating new flight paths which consider new developments contained in the generated dense clouds. Initial flight paths are used to develop a coarse representation of the target area by aligning key tie points of the initial set of images. With each iteration, a 3D mesh is used to compute a new optimized view and flight path used for the data collection of a better-known location. These data are collected, the model updated, and a new flight path is computed until the model resolution meets the required heights or ground sample distances (GSD). This research uses basic UAVs and camera sensors to lower costs and reduce the need for specialized sensors or data analysis. The four basic stages followed in the study include: determination of required height reductions for comparison and convergent limitation, construction of real-time reconnaissance models, optimized view and flight paths with vertical and horizontal buffers constructed from previous models, and develop an autonomous process that combines the previous stages iteratively. This study advances the use of autonomous sUAV inspections by developing an iterative process of flying a sUAV to potentially detect and avoid buildings, trees, wires, and other hazards in an iterative manner with minimal pilot experience or human intervention; while optimally collecting the required images to generate geometric models of predetermined quality.

This thesis presents the work of two significant projects. In the first project, a suite of benchmark problems for grid energy management are presented which demonstrate several issues characteristic to the dynamic optimization of these systems. These benchmark problems include load following, cogeneration, tri-generation, and energy storage, and each one assumes perfect foresight of the entire time horizon. The Gekko Python package for dynamic optimization is introduced and two different solution methods are discussed and applied to solving these benchmarks. The simultaneous solve mode out-performs the sequential solve mode in each benchmark problem across a wide range of time horizons with increasing resolution, demonstrating the ability of the simultaneous mode to handle many degrees of freedom across a range of problems of increasing difficulty. In the second project, combined optimization of propulsion system design, flight trajectory planning and battery mass optimization is applied to solar-regenerative high-altitude long-endurance (SR-HALE) aircraft through a sequential iterative approach. This combined optimization approach yields an increase of 20.2% in the end-of-day energy available on the winter solstice at 35°N latitude, resulting in an increase in flight time of 2.36 hours. The optimized flight path is obtained by using nonlinear model predictive control to solve flight and energy system dynamics over a 24 hour period with a 15 second time resolution. The optimization objective is to maximize the total energy in the system while flying a station-keeping mission, staying within a 3 km radius and above 60,000 ft. The propulsion system design optimization minimizes the total energy required to fly the optimal path. It uses a combination of blade element momentum theory, blade composite structures, empirical motor and motor controller mass data, as well as a first order motor performance model. The battery optimization seeks to optimally size the battery for a circular orbit. Fixed point iteration between these optimization frameworks yields a flight path and propulsion system that slightly decreases solar capture, but significantly decreases power expended. Fully coupling the trajectory and design optimizations with this level of accuracy is infeasible with current computing resources. These efforts show the benefits of combining design and trajectory optimization to enable the feasibility of SR-HALE flight.

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.

With the current increased emphasis on the development of energy optimized vehicle systems architectures during the early phases in aircraft conceptual design, accurate predictions of these off-nominal requirements are needed to justify architecture concept selection. A process was developed for capturing architecture specific performance degradation strategies and optimally imposing their associated requirements. This process is enabled by analog extensions to traditional safety design and assessment tools and consists of six phases: Continuous Functional Hazard Assessment, Architecture Definition, Load Shedding Optimization, Analog System Safety Assessment, Architecture Optimization, and Architecture Augmentation. Systematic off-nominal analysis of requirements was performed for dissimilar architecture concepts. It was shown that traditional discrete application of safety and reliability requirements have adverse effects on the prediction of requirements. This design bias was illustrated by cumulative unit importance metrics. Low fidelity representations of the loss/hazard relationship place undue importance on some units and yield under or over-predictions of system performance.

Master’s thesis deals with problems of maintenance of small transport aircraft with application MSG-3 methodology and maintenance plan development. It is aimed at systems and powerplant maintenance. This method is applied on L-410UVP-E20 aircraft. Other parts of this work are assessment of recent situation in maintenance of airplanes, used approaches to maintenance, methods and documentation for maintenance. Last part of this work is practical example of MSG-3 process on assign item and its interpretation.

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.

This thesis focuses on vision-based detection and observation of small, slow-moving targets using a gimballed fixed-wing unmanned aircraft system (UAS). Generally, visual tracking algorithms are tuned to detect motion of relatively large objects in the scene with noticeably significant motion; therefore, applications such as high-altitude visual searches for human motion often ignore target motion as noise. Furthermore, after a target is identified, arbitrary maneuvers for transitioning to overhead orbits for better observation may result in temporary or permanent loss of target visibility. We present guidelines for tuning parameters of the Visual Multiple Target Tracking (Visual MTT) algorithm to enhance its detection capabilities for very small, slow-moving targets in high-resolution images. We show that the tuning approach is able to detect walking motion of a human described by 10-15 pixels from high altitudes. An algorithm is then presented for defining rotational bounds on the controllable degrees of freedom of an aircraft and gimballed camera system for maintaining visibility of a known ground target. Critical rotations associated with the fastest loss or acquisition of target visibility are also defined. The accuracy of these bounds are demonstrated in simulation and simple applications of the algorithm are described for UAS. We also present a path planning and control framework for defining and following both dynamically and visually feasibly transition trajectories from an arbitrary point to an orbit over a known target for further observation. We demonstrate the effectiveness of this framework in maintaining constant target visibility while transitioning to the intended orbit as well as in transitioning to a lower altitude orbit for more detailed visual analysis of the intended target.

<p>Safety Management Systems (SMS) in the aviation industry is increasingly an essential aspect of identifying hazards and managing the associated risks. While SMS has become commonplace and is often a regulatory requirement for air carriers, it remains voluntary for many other aviation service providers such as airports. Over the past decade, commercial UAS operations have significantly increased, leading to safety and economic challenges for airports. This research studied the current state of SMS and commercial UAS operations at airports. This research utilized a mix of quantitative and qualitative methods, which included an extensive literature review, interviews, and a survey of airport stakeholders. The literature review confirmed an increase in UAS hazards and risks within the airport operating area coupled with immature SMS practices that address these UAS operations. To build on the findings from the review of literature, a survey instrument was developed, distributed to airport stakeholders, and the responses were statistically analyzed. To gain greater insight into these findings, researchers interviewed three airport subject matter experts. The study compared the airports current state of SMS with UAS operations, the airport stakeholder’s level of familiarity with related policies, and their need for additional UAS SMS guidance material or training. Research results suggest a need for further development and adoption of robust SMS practices at airports along with education and training. This study may assist airport stakeholders, UAS operators, and regulators to further develop robust safety and risk management practices that support safe UAS operations within the airport operating area.</p>