Accurately forecasting the contribution of the Greenland Ice Sheet to global sea-level requires precise observations to constrain present-day processes and incorporate them into models. However, the spatial and temporal resolution of satellite imagery and representativeness of in situ measurements often precludes or obscures our understanding of mass loss processes. This thesis investigates whether imagery from unmanned aerial vehicles (UAVs) have the potential to 1) bridge the scale gap between in situ and satellite observations and, 2) resolve processes of mass loss which are beyond the resolution of satellite imagery. It is found that the footprints of ground-based pyranometers are insufficient to capture the spatial heterogeneity of the ice surface as it progressively ablates and darkens. Point-to-pixel albedo comparisons are therefore often invalid, meaning that satellite-derived albedo measurements may be more accurate than previously thought. A 25 km transect intersecting the dark zone reveals that distributed impurities, not cryoconite nor surface water, govern spatial albedo patterns and may have implications for the future expansion of the dark zone. Repeat surveys over Store Glacier show that UAVs can be used to quantify calving rates and surface velocities of tidewater glaciers. The surveys indicate that large calving events cause short-term terminus velocity accelerations and can explain the seasonal pattern of acceleration and retreat. Any process which accelerates calving, such as removal of the ice m ́elange, therefore has important implications for the glaciers future behaviour.

In this thesis, we build a robust end-to-end pipeline for the localization of multiple drones in a GPS-denied environment. This pipeline would help us with cooperative formation control, autonomous delivery, search and rescue operations etc. To achieve this we integrate a custom trained YOLO (You Only Look Once) object detection network, for drones, with the ZED2 stereo camera system. With the help of this sensor we obtain a relative vector from the left camera to that drone. After calibrating it from the left camera to that drone's center of mass, we then estimate the location of all the drones in the leader drone's frame of reference. We do this by solving the localization problem with least squares estimation and thus acquire the location of the follower drone's in the leader drone's frame of reference. We present the results with the stereo camera system followed by simulations run in AirSim to verify the precision of our pipeline. / Master of Science / In the recent years, technologies like Deep Learning and Machine Learning have seen many rapid developments. This has lead to the rise of fields such as autonomous drones and their application in fields such as bridge inspection, search and rescue operations, disaster management relief, agriculture, real estate etc. Since GPS is a highly unreliable sensor, we need an alternate method to be able to localize the drones in various environments in real time. In this thesis, we integrate a robust drone detection neural network with a camera which estimates the location. We then use this data to get the relative location of all the follower drones from the leader drone. We run experiments with the camera and in a simulator to show the accuracy of our results.

Yes / Unmanned Aerial Vehicles (UAVs), commercially known as drones, have received great attention. This is due to their versatility and applicability to a large number of domains such as surveillance system, aerial photography, traffic control, flyable base stations to provide a broadband coverage and even for future urban transportation services. In this paper, the optimal distance between multiple aerial base stations has analytically been derived, based on an aerial coverage area computation. This is a fundamental wireless metric that can significantly minimise the intra-overlapped coverage and also can enhance wireless coverage connectivity and performance of aerial wireless networks. The novelty of our approach brings a better aerial optimal design understanding for UAVs communications performance without the need for establishing an aerial deployment setup.

The thesis employs critical discourse analysis to map the debate regarding the deployment of armed Unmanned Aerial Vehicles (UAVs) in warfare and analyses the arguments that legitimise drone strikes and those which criticise their deployment. It also identifies the contentious issues regarding new technologies in warfare. The thesis is aimed at examining the kinds of arguments and justifications that have been provided by different actors for the deployment of armed drone strikes by the United States in Pakistan over a fifteen-year period, beginning with the first strikes in June 2004. It focuses on the bureaucratic debates regarding the strikes and how political leaders have framed the rationale for their deployment. Consequently, it is important to critically analysis how the strikes by United States have been interpreted by different voices and whether the actions of the United States and its drone policy can or cannot be normatively and ethically justified. The thesis sets out by identifying the common themes that emerge from the public discourse and sets out to answer one key question that assesses the intertextual framework that has bounded the official discourse, the wider political, academic and public debate regarding armed unmanned drone strikes. That is: How have the US drone campaigns...

Os veículos aéreos não tripulados desempenham diversas funções que vão desde tarefas de monitoramento e inspeção, em aplicações não militares, até tarefas de espionagem e detecção de alvos na área militar. Estes veículos têm como principal componente de controle um piloto automático capaz de manter a aeronave estabilizada e de conduzi-la através de uma rota selecionada. Atualmente, o desenvolvimento de veículos aéreos não tripulados, para aplicações civis, está sendo favorecido e facilitado pelo atual estágio de desenvolvimento tecnológico e, principalmente, pela redução do custo e do tamanho dos componentes eletrônicos. O projeto ARARA (Aeronaves de Reconhecimento Assistidas por Rádio e Autônomas), visa a construção de um veículo aéreo não tripulado para monitoramento. Tem como principal objetivo a substituição de aeronaves convencionais utilizadas na obtenção de imagens aéreas para o monitoramento de plantações e áreas sob controle ecológico. O piloto automático para as aeronaves do projeto ARARA está dividido nos módulos Sistema de Navegação e Sistema de Controle. O módulo Sistema de Navegação mantém a aeronave na rota e corrige os desvios em seu curso. O Sistema de Controle mantém a aeronave estabilizada e executa as manobras solicitadas pelo Sistema de Navegação. O Sistema de Controle é o foco principal deste Trabalho. O Sistema de Controle nas aeronaves do projeto ARARA é o único módulo que atua diretamente nos servomecanismos existentes no avião, sendo dependente de suas características. As simulações foram realizadas no MATLAB Simulink utilizando módulos específicos para a simulação do modelo do avião e para o ajuste dos controladores.

This research applies techniques from the field of optimization to spatial positioning and energy management in Unmanned Aerial Vehicles (UAVs). Two specific areas are treated: optimization of UAV view plans for 3D modeling of infrastructure, and trajectory optimization of solar powered high-altitude long-endurance (HALE) UAVs. Structure-from-Motion (SfM) is a computer vision technique for creating 3D models from 2D images. View planning is the process of planning image sets that will effectively model a given scene. First, a genetic algorithm based view planning approach is demonstrated. A novel terrain simulation environment is developed, and the algorithm is tested at multiple sites of interest. The genetic algorithm is compared quantitatively to traditional flights, and is found to yield terrain models with up to 43% greater accuracy than a standard grid flight pattern. Next a greedy heuristic planner is developed, and used to combine anomaly detection with automatic on-board 3D view planning for long linear infrastructure objects such as canals and pipelines. The proposed method is shown in simulation to decrease total flight time by up to 55%, while reducing the amount of image data to be processed by 89% and maintaining 3D model accuracy at areas of interest. The planning algorithm is then extended to select images of ground targets from an existing data set. The algorithm is tested on five different targets, and is shown to reduce processing time for target models by up to a factor of 50 with little decrease in accuracy. The second portion of the research demonstrates the use of nonlinear dynamic optimization to calculate energy optimal trajectories for a high-altitude, solar-powered Unmanned Aerial Vehicle (UAV). The objective is to maximize the total energy in the system while staying within a 3 km mission radius and meeting other system constraints. Solar energy capture is modeled using the vehicle orientation and solar position, and energy is stored both in batteries and in potential energy through elevation gain. Energy capture is maximized by optimally adjusting the angle of the aircraft surface relative to the sun. The UAV flight and energy system dynamics are optimized over a 24-hour period at an eight-second time resolution using Nonlinear Model Predictive Control (NMPC). Results of the simulated flights are presented for all four seasons, showing 8.2% increase in end-of-day battery energy for the most limiting flight condition of the wintersolstice.

Os veículos aéreos não tripulados desempenham diversas funções que vão desde tarefas de monitoramento e inspeção, em aplicações não militares, até tarefas de espionagem e detecção de alvos na área militar. Estes veículos têm como principal componente de controle um piloto automático capaz de manter a aeronave estabilizada e de conduzi-la através de uma rota selecionada. Atualmente, o desenvolvimento de veículos aéreos não tripulados, para aplicações civis, está sendo favorecido e facilitado pelo atual estágio de desenvolvimento tecnológico e, principalmente, pela redução do custo e do tamanho dos componentes eletrônicos. O projeto ARARA (Aeronaves de Reconhecimento Assistidas por Rádio e Autônomas), visa a construção de um veículo aéreo não tripulado para monitoramento. Tem como principal objetivo a substituição de aeronaves convencionais utilizadas na obtenção de imagens aéreas para o monitoramento de plantações e áreas sob controle ecológico. O piloto automático para as aeronaves do projeto ARARA está dividido nos módulos Sistema de Navegação e Sistema de Controle. O módulo Sistema de Navegação mantém a aeronave na rota e corrige os desvios em seu curso. O Sistema de Controle mantém a aeronave estabilizada e executa as manobras solicitadas pelo Sistema de Navegação. O Sistema de Controle é o foco principal deste Trabalho. O Sistema de Controle nas aeronaves do projeto ARARA é o único módulo que atua diretamente nos servomecanismos existentes no avião, sendo dependente de suas características. As simulações foram realizadas no MATLAB Simulink utilizando módulos específicos para a simulação do modelo do avião e para o ajuste dos controladores.

This master thesis focuses on the cutting-edge application of AI in developing intrusion detection systems (IDS) for unmanned aerial vehicles (UAVs) in smart cities. The objective is to address the escalating problem of UAV intrusions, which pose a significant risk to the safety and security of citizens and critical infrastructure. The thesis explores the current state of the art and provides a comprehensive understanding of recent advancements in the field, encompassing both physical and network attacks. The literature review examines various techniques and approaches employed in the development of AI-based IDS. This includes the utilization of machine learning algorithms, computer vision technologies, and edge computing. A proposed solution leveraging computer vision technologies is presented to detect and identify intruding UAVs in the sky effectively. The system employs machine learning algorithms to analyze video feeds from city-installed cameras, enabling real-time identification of potential intrusions. The proposed approach encompasses the detection of unauthorized drones, dangerous UAVs, and UAVs carrying suspicious payloads. Moreover, the thesis introduces a Cycle GAN network for image denoising that can translate noisy images to clean images without the need for paired training data. This approach employs two generators and two discriminators, incorporating a cycle consistency loss that ensures the generated images align with their corresponding input images. Furthermore, a distributed architecture is proposed for processing collected images using an edge-offloading approach within the UAV network. This architecture allows flying and ground cameras to leverage the computational capabilities of their IoT peers to process captured images. A hybrid neural network is developed to predict, based on input tasks, the potential edge computers capable of real-time processing. The edge-offloading approach reduces the computational burden on the centralized system and facilitates real-time analysis of network traffic, offering an efficient solution. In conclusion, the research outcomes of this thesis provide valuable insights into the development of secure and efficient IDS for UAVs in smart cities. The proposed solution contributes to the advancement of the UAV industry and enhances the safety and security of citizens and critical infrastructure within smart cities.

The introduction of electric autonomous Unmanned Arial Vehicles (UAVs) in cities is considered the ultimate disruptive sustainable technological solution due to the promised speed, affordability, and significant greenhouse gas (GHG) emission reductions. The integration of UAVs into the future smart city fabric offers a wide range of applications. In particular, UAVs are ideal for last-mile operation, which is expected to reduce delivery costs, GHG emissions, and delivery time compared to light trucks and other traditional delivery methods. As UAVs operate in the city airspace, and with the current generation of older cities, several technological challenges arise with the anticipated proliferation of heterogeneous UAV fleets in low-altitude airspace of dense urban areas. Being a fairly new disruptive technology with no real-world operation data, the literature only considers a few of the system design parameters and often disregards the impact of other essential parameters such as Kinematics and airspace policies. This leads to significant uncertainty in the estimated UAV energy consumption, ranges, and emissions yielding inaccurate conclusions regarding the full system design predilections. Therefore, an effective UAV system design should strive to understand the broad spectrum of parameters’ impacts to optimize the integration and operation. Towards that end, this research aims at investigating the different UAV system design parameters and their intertwined impacts on operation efficiency to obtain accurate system optimization results. The research utilized several datasets for the delivery demand and digital-twin city model data of Toronto, Ontario, Canada. The research employed a state-of-the-art flexible energy use model for UAVs calibrated to experimental measurements to generate a minimum-energy trajectory along with several proposed novel airspace discretization, trajectory optimization, and charging infrastructure allocation optimization models. In this respect, this dissertation quantified the impact of airspace policies, discretization, and trajectory generation on the energy consumption of UAVs. Furthermore, it unveiled the operation uncertainties and their implications on the cost, emissions, and allocated charging infrastructure demand. Unlike the UAV literature, our research included all system design parameters and their impact on the performance metrics. The dissertation also proposes a novel combined airspace discretization and trajectory generation algorithm for optimal UAV energy consumption, airspace capacity maximization, airspace traffic control, and off-grid solar charging station allocation. For instance, it is found that UAV deployment with carefully tailored airspace policies in delivery could reduce GHG emissions in the freight sector by up to 35% compared to EVs. Furthermore, the research highlighted how building integrated photovoltaic BIPV upgrades with associated buildings can eliminate GHG emissions and significantly reduce the decarbonization price through associated savings and excess generated electricity. Overall, this research presents a unique contribution to the knowledge of UAV research for practitioners, policymakers, and academia. / Thesis / Doctor of Philosophy (PhD)

Sandy beaches are vulnerable to extreme erosion during large storms, as well as gradual erosion processes over months and years. Without monitoring and adaptation strategies, erosion can put people, homes, and other infrastructure at risk. To effectively manage beach resources and respond to erosion hazards, coastal managers must have a reliable means of surveying the beach to monitor erosion and accretion. These elevation surveys typically incorporate traditional ground-based surveying methods or lidar surveys flown from large, fixed-wing aircraft. While both strategies are effective, advancements in photogrammetric technology offers a new solution for topographic surveying: Structure from Motion (SfM). Using a set of overlapping aerial photographs, the SfM workflow can generate accurate topographic surveys, and promises to provide a fast, inexpensive, and reliable method for routine beach surveying. Unmanned aerial vehicles (UAVs) are often successfully employed for SfM surveys but can be limited by poor weather ad government regulations, which can make flying difficult or impossible. To circumvent these limitations, a digital camera can be attached to a tall pole on a mobile platform to obtain aerial imagery, avoiding the restrictions of UAV flight. This thesis compares these two techniques of image acquisition for routine beach monitoring. Three surveys were conducted at monthly intervals on a beach on the central South Carolina coast, using both UAV and pole photogrammetry. While both methods use the same software and photogrammetric workflow, the UAV produced better results with far fewer processing artifacts compared to pole photogrammetry. / Master of Science / Beach environments are vulnerable to extreme erosion, especially in the face of sea level rise and large storms like hurricanes. Monitoring erosion is a crucial part of a coastal management strategy, to mitigate risk to coastal hazards like extreme erosion, storm surge, and flooding. Erosion monitoring usually involves repeated elevation surveys to determine how much sand is being lost from the beach, and where that sand is being eroded away. Within the past decade, Structure from Motion (SfM) photogrammetry, the process of deriving ground elevation maps from multiple overlapping aerial photographs, has become a common technique for repeated elevation surveys. Unmanned aerial vehicles (UAVs) are often used to gather aerial imagery for SfM elevation surveys but are limited by poor weather conditions and government flight regulations, both of which can prohibit flight. However, similar aerial photographs can be taken with a camera mounted atop a tall pole, which can be used in wider range of weather conditions and without government regulations, providing an alternative when UAV flight is not an option. This study compares these two platforms for routine beach erosion monitoring surveys, evaluating them based on performance, cost, and feasibility. The UAV system is found to be fast, affordable, and effective, while the pole photogrammetry system is heavily affected by the slow speed of surveying and processing errors that make it unusable without significant improvement.