Desde o início do século XX que há registros do uso de Veículos Aéreos Não Tripulados (VANTs) utilizados com finalidade militar. Mas esta não é a única forma que se pode utilizá-los, o ambiente civil também é próspero em possíveis utilizações deste tipo de aeronave. Faltam ainda estudos e comprovações de que a integração dos VANTs no espaço aéreo juntamente com a aviação tripulada convencional não vá trazer alterações nos níveis de segurança já estabelecidos. Juntam-se a este desafio alguns outros, tais como padronizações de normas, classificações e legislação que regulamente o vôo das aeronaves não tripuladas. A presente dissertação faz uma coletânea de alguns modelos relacionados a cálculos de níveis de segurança no vôo de VANTs, bem como compara esses modelos entre si visando o estabelecimento de um método de escolha do modelo mais adequado para aplicação em alguns cenários civis de utilização. Esse trabalho ainda faz a aplicação desse método considerando os modelos relacionados à segurança dos VANTs. / There are records of the use of Unmanned Aerial Vehicles (UAVs) used for military activities since the beginning of the 20th century. But that is not the only way to use it; the civil environment is also promising as to the use of this kind of aircraft. More studies and validations have to be performed about the alterations in the safety levels with the integration of UAVs in the air space with classic manned air vehicles. There are others challenges such as the standardization of norms, classification and legislation to regulate the Unmanned Aerial Vehicle flights. This dissertation presents some models related to the calculation of the safety levels in UAVs flight, it compares them to establish a method for choosing the most suitable model to apply in some civil scenarios. This work also brings the application of this method considering the models related to the safety of UAVs.

Uncrewed aerial vehicles (UAVs), commonly known as drones, have become increasingly prevalent in various applications. However, the localization and payload detection of drones is crucial for ensuring safety and security. This thesis proposes a vision-based solution using deep learning techniques to address these challenges. Existing solutions like radars and acoustic sensors have limitations, including high costs, limited accuracy, and the need for prior knowledge of the drone's model. Normal radars lack angle estimation accuracy and rely on known micro-Doppler features for payload detection, while acoustic sensors are restricted to close ranges for payload analysis. In contrast, cameras offer a cost-effective alternative as they have become widely available and can capture visual data. In addition, due to resource constraints, mounting multiple sensors on the UAV along with the camera is impractical, making reliance on cameras alone essential for addressing the mentioned problems. Recent advancements in deep learning algorithms enable regression and classification tasks, making vision data a promising choice for solving drone localization and payload detection problems. The proposed solution leverages convolutional neural networks (CNNs) for regression tasks, estimating the distance of a drone from the captured image. The CNN takes a cropped image within the drone's bounding box as input and outputs the estimated distance. Additionally, the drone's azimuth and elevation angles have been estimated based on its position in the captured image using a simple pinhole model for the camera. Also, the ResNet and EfficientNet classifiers could accurately classify drones as loaded or unloaded, even without prior knowledge of their shape. Due to a scarcity of publicly available datasets, this study developed the first air-to-air simulated dataset specifically for the classification of loaded versus unloaded drones. To evaluate the performance of the proposed solution, both simulated and experimental tests were conducted. The results showcased promising accuracy, with a root mean square error (RMSE) of less than 10 meters for distance estimation and an RMSE of less than 3 degrees for angle estimation. Furthermore, the payload detection problem was effectively addressed, achieving a classification accuracy of over 85\% for distinguishing between loaded and unloaded drones using the trained network based on the simulated dataset. The numerical highlights demonstrate the effectiveness of using camera sensors for 3D localization, with accurate distance and angle estimations. The high accuracy achieved in payload classification showcases the potential of the proposed solution for detecting drone payloads at distances up to 100 meters. These results pave the way for enhanced safety and security in drone environments.

Through this research, a robust aircraft design methodology is developed for analysis and optimization of the Air Vehicle (AV) segment of Unmanned Aerial Vehicle (UAV) systems. The analysis functionality of the AV design is integrated with a Genetic Algorithm (GA) to form an integrated Multi-disciplinary Design Optimization (MDO) methodology for optimal AV design synthesis. This research fills the gap in integrated subsonic fixed-wing UAV AV MDO methods. No known single methodology captures all of the phenomena of interest over the wide range of UAV families considered here. Key advancements include: 1) parametric Low Reynolds Number (LRN) airfoil aerodynamics formulation, 2) UAV systems mass properties definition, 3) wing structural weight methods, 4) self-optimizing flight performance model, 5) automated geometry algorithms, and 6) optimizer integration. Multiple methods are provided for many disciplines to enable flexibility in functionality, level of detail, computational expediency, and accuracy. The AV design methods are calibrated against the High-Altitude Long-Endurance (HALE) Global Hawk, Medium-Altitude Endurance (MAE) Predator, and Tactical Shadow 200 classes, which exhibit significant variations in mission performance requirements and scale from one another. Technology impacts on the design of the three UAV classes are evaluated from a representative system technology year through 2025. Avionics, subsystems, aerodynamics, design, payloads, propulsion, and structures technology trends are assembled or derived from a variety of sources. The technology investigation serves the purposes of validating the effectiveness of the integrated AV design methods and to highlight design implications of technology insertion through future years. Flight performance, payload performance, and other attributes within a vehicle family are fixed such that the changes in the AV designs represent technology differences alone, and not requirements evolution. The optimizer seeks to minimize AV design gross weight for a given mission requirement and technology set. All three UAV families show significant design gross weight reductions as technology improves. The predicted design gross weight in 2025 for each class is: 1) 12.9% relative to the 1994 Global Hawk, 2) 6.26% relative to the 1994 Predator, and 3) 26.3% relative to the 2000 Shadow 200. The degree of technology improvement and ranking of contributing technologies differs among the vehicle families. The design gross weight is sensitive to technologies that directly affect the non-varying weights for all cases, especially payload and avionics/subsystems technologies. Additionally, the propulsion technology strongly affects the high performance Global Hawk and Predator families, which have high fuel mass fractions relative to the Tactical Shadow 200 family. The overall technology synergy experienced 10-11 years after the initial technology year is 6.68% for Global Hawk, 7.09% for Predator, and 4.22% for the Shadow 200, which means that the technology trends interact favorably in all cases. The Global Hawk and Shadow 200 families exhibited niche behavior, where some vehicles attained higher aerodynamic performance while others attained lower structural mass fractions. The high aerodynamic performance Global Hawk vehicles had high aspect ratio wings with sweep, while the low structural mass fraction vehicles had straight, relatively low aspect ratios and smaller wing spans. The high aerodynamic performance Shadow 200 vehicles had relatively low wing loadings and large wing spans, while the lower structural mass fraction counterparts sought to minimize physical size. / Ph. D.

One of the major application areas for UAVs is the automated traversing and inspection of infrastructure. Much of this infrastructure is linear, such as roads, pipelines, rivers, and railroads. Rather than hard coding all of the GPS coordinates along these linear components into a flight plan for the UAV to follow, one could take advantage of computer vision and machine learning techniques to detect and travel along them. With regards to roads and railroads, two separate algorithms were developed to detect the angle and distance offset of the UAV from these linear infrastructure components to serve as control inputs for a flight controller. The road algorithm relied on applying a Gaussian SVM to segment road pixels from rural farmland using color plane and texture data. This resulted in a classification accuracy of 96.6% across a 62 image dataset collected at Kentland Farm. A trajectory can then be generated by fitting the classified road pixels to polynomial curves. These trajectories can even be used to take specific turns at intersections based on a user defined turn direction and have been proven through hardware-in-the-loop simulation to produce a mean cross track error of only one road width. The combined segmentation and trajectory algorithm was then implemented on a PC (i7-4720HQ 2.6 GHz, 16 GB RAM) at 6.25 Hz and a myRIO 1900 at 1.5 Hz proving its capability for real time UAV control. As for the railroad algorithm, template matching was first used to detect railroad patterns. Upon detection, a region of interest around the matched pattern was used to guide a custom edge detector and Hough transform to detect the straight lines on the rails. This algorithm has been shown to detect rails correctly, and thus the angle and distance offset error, on all images related to the railroad pattern template and can run at 10 Hz on the aforementioned PC. / Master of Science

The use of aerial platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, has emerged as a promising solution for providing reliable and cost-effective wireless communications. In particular, UAVs can be quickly and efficiently deployed to support cellular networks and enhance their quality-of-service (QoS) by establishing line-of-sight communication links. With their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. Remarkably, despite these inherent advantages of UAVbased communications, little work has analyzed the performance tradeoffs associated with using UAVs as aerial wireless platforms. The key goal of this dissertation is to develop the analytical foundations for deployment, performance analysis, and optimization of UAV-enabled wireless networks. This dissertation makes a number of fundamental contributions to various areas of UAV communications that include: 1) Efficient deployment of UAVs, 2) Performance evaluation and optimization, and 3) Design of new flying, three-dimensional (3D) wireless systems. For deployment, using tools from optimization theory, holistic frameworks are developed for the optimal 3D placement of UAV base stations in uplink and downlink scenarios. The results show that the proposed deployment approaches significantly improve the downlink coverage for ground users, and enable ultra-reliable and energy-efficient uplink communications in Internet of Things (IoT) applications. For performance optimization, a novel framework is developed for maximizing the performance of a UAV-based wireless system, in terms of data service, under UAVs’ flight time constraints. To this end, using the mathematical framework of optimal transport theory, the optimal cell associations, that lead to a maximum data service to ground users within the limited UAVs’ hover duration, are analytically derived. The results shed light on the tradeoff between hover time and quality-of-service in UAV-based wireless networks. For performance evaluation, this dissertation provides a comprehensive analysis on the performance of a UAV-based communication system in coexistence with a terrestrial network. In particular, a tractable analytical framework is proposed for analyzing the coverage and rate performance of a network with a UAV base station and deviceto-device (D2D) users. The results reveal the fundamental tradeoffs in such a UAV-D2D network that allow adopting appropriate system design parameters. Then, this dissertation sheds light on the design of three new drone-enabled wireless systems. First, a novel framework for effective use of cache-enabled UAVs in wireless networks is developed. The results demonstrate how the users’ quality of experience substantially improves by exploiting UAVs’ mobility and user-centric information. Second, a new framework is proposed for deploying and operating a drone-based antenna array system that delivers wireless service to ground users within a minimum time. The results show significant enhancement in QoS, spectral and energy efficiency while levering the proposed drone antenna array system. Finally, to effectively incorporate various use cases of drones ranging from aerial users to base stations, the new concept of a fully-fledged 3D cellular network is introduced. For this new type of 3D wireless network, a unified framework for deployment, network planning, and performance optimization is developed that yields a maximum coverage and minimum latency in the network. In a nutshell, the analytical foundations and frameworks presented in this dissertation provide key guidelines for effective design and operation of UAV-based wireless communication systems. / Ph. D.

Det finns flertal användningsområden för digitala höjdmodeller där det krävs hög noggrannhet för att problematik och ekonomiska konsekvenser inte ska uppstå. Digitala höjdmodeller kan användas till volymberäkning, projektering och geografiska analyser. Digitala höjdmodeller kan kategoriseras som antingen digital ytmodell eller digital terräng-modell. Då hög noggrannhet eftersträvas i digitala terrängmodeller har SIS framställt en standard benämnd SIS-TS 21144:2016 som beskriver hur inmätning och kontroll av data till digitala terrängmodeller ska hanteras. För insamling av höjdinformation till en digital terrängmodell finns olika terrestra och flygburna mätmetoder. Vanliga terrestra mätmetoder är totalstation, GNSS och terrester laserskanning medan flygburna mätmetoder är flygburen laserskanning eller olika metoder med digital fotogrammetri. Syftet med studien är att undersöka noggrannheten hos höjdmodeller kategoriserade som digitala terräng-modeller. Insamling av höjdinformation skedde med totalstation, GNSS-metoden NRTK och UAV samt inhämtning av LAS-data från NH för tre olika karaktäristiska grönområden inom Karlstad med omnejd. SIS-TS 21144:2016 har klassificerat terrängmodeller beroende på användningsområde och terräng. Klassificeringen går mellan klass 1–10 och varje klass har en maximal tolerans i höjd. För studien har tre studieområden som går under klassificeringarna klass 2, klass 3 och klass 5 valts ut för undersökning. Samtliga studieområden är avgränsade till 40 x 40 meter. Innan insamling av data markerades och mättes bakåtobjekt och avvägning genomfördes. Samtlig insamlad data bearbetades i programvaran SBG Geo och UAV data bearbetades även i programvaran Agisoft PhotoScan Professional. För kontroll av samtliga terrängmodeller genomfördes inmätning av tre kontrollprofiler med totalstation enligt SIS-TS 21144:2016. Resultatet visade att UAV är inom tolerans för samtliga studieområden medan NH-data resulterade i enstaka kontrollpunkter utanför klassningens tolerans för samtliga studie-områden. De två terrestra mätmetoderna är båda inom tolerans för klass 2 och varsin kontrollpunkt utanför tolerans för klass 5. Vid studieområde klass 3 är fem kontrollpunkter för totalstation utanför tolerans respektive åtta för NRTK. Vid analys av vilken mätmetod som resulterar i noggrannast terrängmodell inom samtliga studieområden krävs beaktning av antal inmätningspunkter och trianglar som terrängmodellen är uppbyggd av. För klass 2 ger de flygburna mätmetoderna flest antal inmätningspunkter och trianglar medan UAV resulterar i betydligt högre värden för de två resterande studieområdena. Antal inmätnings-punkter för de terrestra mätmetoderna har operatör beslutat om under mätning, vilket har kunnat ökas för att generera terrängmodeller som består av fler trianglar. Resultatet från studien visar att UAV resulterar i terrängmodeller som klarar toleranser inom undersökta studieområden och SIS-TS 21144:2016 klassificeringar. / There are previous research about digital terrain models and how different methods of producing digital terrain models varies in accuracy and there are several different methods to produce a digital terrain models.  In this study the following methods, tools and data are used to produce digital terrain models over three different characteristic study areas: total station, GNSS, UAV and NH. Previous work has failed to address the accuracy given by these four methods over the same three characteristic study areas thus preventing the understanding of most suitable methods for different areas. In this study three different green areas have been studied and the different digital terrain models has been produced and controlled with SIS standard SIS-TS 21144:2016. Data in form of height information were collected by the aforementioned methods and processed to generate results over the accuracy of each methods. The results shows that UAV provide most accurately digital terrains models in least time spent in field but also total station and GNSS generate digital terrain models that are accurate.

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.

Forage quality is a principal factor in managing both herbivores and the landscapes they use. Nutrition varies across the landscape, and in turn, so do the distributions of these populations. With the rise of remote sensing technologies (i.e. satellites, unmanned aerial vehicles, and multi/hyperspectral sensors), comes the ability to index forage health and nutrition swiftly. However, no methodology has been developed which allows managers to use unmanned aerial systems to the fullest capacity. The following methodologies produce compelling evidence for predicting forage quality metrics (such as fiber, carbohydrates, and digestibility) using 5 measured bands of reflectance (Blue, Green, Red, Red Edge, and NIR), 3 derived vegetation indices (NDVI, EVI and VARI), and a variety of environmental factors (i.e. time and sun angles) in a LASSO framework. Fiber content, carbohydrates, and digestibility showed promising model performance in terms of goodness-of-fit (R2= 0.624, 0.637, and 0.639 respectively).

The focus of this thesis is to analyse the flight characteristics of the blended wingbody (BWB) unmanned aerial vehicle (UAV) Green Raven currently being developed by students at the Royal Institute of Technology (KTH) in Stockholm,Sweden. The purpose of evaluating a BWB aircraft is due to its potential increasein fuel efficiency and payload compared to conventional aircrafts which would enable more sustainable flights. The analysis is conducted in ANSYS Fluent 2020R2 where the goals are to extrapolate lift, drag and pitching moment coefficients,aerodynamic efficiency and evaluate stall patterns. The analysis is conducted with free stream velocities from 5 m/s to 40 m/s with5 m/s increments at angles of attack from −4◦ to stall plus 4◦. The result of thisthesis is that an analysis have not been able to be conducted due to a lack ofcomputational power. Thusly, the conclusion to this thesis is that to be able toperform a complete analysis of the Green Raven, a more powerful computer needsto be used.

Orientador: Vania Silvia Rosolen / Resumo: No Brasil, a captação de água para irrigação é de aproximadamente 1000 m³/s, caracterizando o maior consumo de água do território nacional. Como possível ambiente de estoque natural de água que cobre quase 20% do território, as áreas úmidas vem sendo drasticamente reduzidas pela conversão do uso da terra. As áreas úmidas promovem a infiltração das águas superficiais e caracterizam áreas de recarga de aquíferos. Nesse sentido, medidas e modelos relacionados aos solos com propriedades hidromórficas e seu papel na recarga de aquíferos constituem um desafio para compreender a dinâmica entre solo e água, a fim de atender o desenvolvimento sustentável. Neste estudo, análises baseadas em sensoriamento remoto, com o uso de sensores ópticos de alta resolução espaço-temporal a bordo de Veículos Aéreos Não Tripulados (VANT), associadas ao uso de técnicas não invasivas que permitem o mapeamento da arquitetura subsuperficial dos sistemas pedológicos (ensaios geofísicos de Eletrorresistividade, por Tomografia Elétrica), foram aplicadas para compreender a relação água-solo superficial e subsuperficial em uma área úmida da chapada sedimentar do oeste mineiro. A integração destes dados com ensaios in situ de permeabilidade e de densidade e granulometria dos solos, permitiu uma abordagem ampla e tridimensional do comportamento dos parâmetros hidrogeológicos na área úmida. Os resultados permitiram determinar que a área úmida estudada é uma depressão que possui três compartimentos com distinções... (Resumo completo, clicar acesso eletrônico abaixo) / Mestre