In the pursuit of research in remote areas, robots may be employed to deploy sensor networks. These robots need a method of classifying a surface to determine if it is a suitable installation site. Developing surface classification models manually requires significant time and detracts from the goal of automating systems. We create a system that automatically collects the data using an Unmanned Aerial Vehicle (UAV), extracts features, trains a large number of classifiers, selects the best classifier, and programs the UAV with that classifier. We design this system with user configurable parameters for choosing a high accuracy, efficient classifier. In support of this system, we also develop an algorithm for evaluating the effectiveness of individual features as indicators of the variable of interest. Motivating our work is a prior project that manually developed a surface classifier using an accelerometer; we replicate those results with our new automated system and improve on those results, providing a four-surface classifier with a 75% classification rate and a hard/soft classifier with a 100% classification rate. We further verify our system through a field experiment that collects and classifies new data, proving its end-to-end functionality. The general form of our system provides a valuable tool for automation of classifier creation and is released as an open-source tool.

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As Unmanned Aerial Vehicles (UAVs) are getting more popular and their area of use is expanding rapidly, the security aspect becomes important to investigate. This thesis is a systematic literature review that examines which type of cyber attacks are most common among attacks directed at civilian use UAVs and what consequences they bring. All cyber attacks presented in the report are categorized using the STRIDE threat model, which risk they pose and what equipment is required for the adversary to follow through with the attack. The findings are that Spoofing and Denial of Service attacks are the most common cyber attack types against UAVs and that hijacking and crashing are the most common results of the attacks. No equipment that is difficult to access is required for either of the attack types in most cases, making the result an indicator that the security state for civilian use UAVs today needs improving. / Obemannade luftburna farkoster (OLF) blir mer vanliga allteftersom deras användningsområde utökas, vilket innebär att cybersäkerhetsaspekten behöver studeras. Detta arbete är en systematisk litteraturstudie som undersöker vilka typer av cyberattacker riktade mot drönare som är vanligast och vilka risker de medför. Attackerna i rapporten är kategoriserade med hjälp av metoden STRIDE samt efter vilka mål attackerna haft och vilken utrustning som krävs. Resultatet är att Spoofing och Denial of Service-attacker är vanligast och att de medför att attackeraren kan kapa eller krascha drönaren. Ingen svåråtkomlig utrustning krävs för någon av dessa attacktyper vilket indikerar att säkerhetsläget för civila drönare behöver förbättras.

For acquisition of narrow-beam free-space optical (FSO) terminals, a Global Positioning System (GPS) is typically required for coarse localization of the terminal. However, the GPS signal may be shadowed, or may not be present at all, especially in rough or unnameable terrains. In this study, we propose a lidar-assisted acquisition of an unmanned aerial vehicle (UAV) for FSO communications in a poor GPS environment. Such an acquisition system consists of a lidar subsystem and an FSO acquisition subsystem: The lidar system is used for coarse acquisition of the UAV, whereas, the FSO system is utilized for fine acquisition to obtain the UAV’s accurate position. This study investigates the optimal allocation of energy between the lidar and FSO systems to minimize the acquisition time. Here, we minimize the average acquisition time, and maximize the cumulative distribution function of acquisition time for a fixed threshold. We learn that an optimal value of the energy allocation factor exists that provides the best performance of the proposed system.

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).

With increasing focus on climate change more research for net-zero emission are being made in the aviation industry.This project focuses on electric propulsion on a unmanned aerial vehicle (UAV) with a blended wing body (BWB) design. More specifically finding a solution for a propulsion system using electric ducted fan (EDF) engines for a scaled version of the KTH Aerospace project Green Raven. The system consists of a powerplant and power supply i.e engine(s) and a sufficient battery package. The goal is to find a solution to power this 7 kg aerial vehicle for 60 minutes with a consistent cruising speed of 30 m/s. To accomplish this an understanding of thrust and drag profile is essential in order to determine the requirements for the EDFs. Understanding the limitations of the scaled Green Raven is also necessary in order to provide a feasible solution for power supply. The result is to use 2x 50 mm EDF engines providing a total thrust of 16.7 Newtons that is integrated in the main body. To supply these engines two battery sets (one per EDF) composed of three different battery types have been chosen, giving a total capacity of 24 000 mAh for one hour flight time. This propulsion setup fulfils the requirements, though not without flaws because of the choice of integrating the EDFs. An alternative solution would be having the engines externally mounted in order to free up the space in the body for more efficient batteries.

Stockpile volumes are monitored by their companies as the product (i.e., aggregate, soil) is moved in and out of the facilities to ensure minimal product loss. Companies are mandated to report product movement to the government to ensure that the aggregate and soil is going where it is supposed to go. Many tools are used to monitor stockpile volumes including truck scales (to weigh incoming and outgoing trucks), light detection and ranging (LiDAR), Global Navigation Satellite System (GNSS) equipment, and unmanned aerial vehicle (UAV) photogrammetry. These processes give a good estimate of stockpile volumes. Errors in these estimates typically come from transportation and natural degradation of the stockpile. Not much research has been done on the best practices when using UAV photogrammetry to find the volume of a stockpile. Most recent research is about specific situations for finding a stockpile volume and whether UAV photogrammetry is as good as traditional methods for finding stockpile's volume. This study focuses on the effect of the flight height, camera angle, and presence of ground control points (GCP) in processing on the final volume calculated. Six UAV flights were done for this study; three different flight heights and two different camera angles. Additionally, the UAV reconstructed models were run with and without the GCPs to give twelve reconstructed volumes to examine for statistically significant differences. A similar study was done by Tucci et. al\cite{Tucci2019} where they focused on only camera orientation and found that the camera orientation was not statistically significant. We found that the differences between if GCPs in processing or not and between each flight elevation was statistically insignificant. We found that the differences in camera orientation between nadir and oblique were statistically significant. These different results could be due to many variables including differences in the dataset, differences in the statistical analysis, or the difference in stockpile size. We recommend using a high flight elevation and oblique photos to develop an efficient, accurate model.

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.

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