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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.

When tracking a moving target, an Unmanned Aerial Vehicle (UAV) mustkeep the target within its sensory range while simultaneously remaining awareof its surroundings. However, small flight computers must have sufficientenvironmental knowledge and computational capabilities to provide real-timecontrol to function without a ground station connection. Using a Raspberry Pi4 model B, this thesis presents a practical implementation for evaluating pathplanning generators in the context of following a moving target. The practicalmodel integrates two waypoint generators for the path planning scenario: A*and 3D Vector Field Histogram* (3DVFH*). The performances of the pathplanning algorithms are evaluated in terms of the required processing time,distance from the target, and memory consumption. The simulations are runin two types of environments. One is modelled by hand with a target walkinga scripted path. The other is procedurally generated with a random walker.The study shows that 3DVFH* produces paths that follow the moving targetmore closely when the actor follows the scripted path. With a random walker,A* consistently achieves the shortest distance. Furthermore, the practicalimplementation shows that the A* algorithm’s persistent approach to detectand track objects has a prohibitive memory requirement that the Raspberry Pi4 with a 2GBRAMcannot handle. Looking at the impact of object density, the3DVFH* implementation shows no impact on distance to the moving target,but exhibits lower execution speeds at an altitude with fewer obstacles to detect.The A* implementation has a marked impact on execution speeds in the formof longer distances to the target at altitudes with dense obstacle detection.This research project also realized a communication link between thepath planning implementations and a Geographical Information System (GIS)application supported by the Carmenta Engine SDK to explore how locallystored geospatial information impact path planning scenarios. Using VMapgeospatial data, two levels of increasing geographical resolution werecompared to show no performance impact on the planner processes, but asignificant addition in memory consumption. Using geospatial informationabout a region of interest, the waypoint generation implementations are ableto query the map application about the legality of its current position. / När en obemannade luftfarkost, även kallad drönare, spårar ett rörligt mål, måste drönaren behålla målet inom sensorisk räckvidd medan den håller sig uppdaterad om sin omgivning. Små flygdatorer måste dock ha tillräckligt med information om sin omgivning och nog med beräkningsresurser för att erbjuda realtidskontroll utan kommunikation med en markstation. Genom att använda en Raspberry Pi 4 modell B presenterar denna studie en praktisk applicering utav vägplanerare som utvärderas utifrån deras lämplighet att följa ett rörligt mål. Den praktiska implementationen jämför två vägplaneringsalgoritmer: A* och 3D Vector Field Histogram* (3DVFH*). Vägplaneringsalgoritmernas prestanda utvärderas genom att studera deras hastighet, avstånd från målet och minnesresurser. Vägplaneringsalgoritmerna utvärderas i två situationer. Den första är en simulationsvärld som är gjord för hand där målet rör sig efter en fördefinierad väg. Den andra är en procedurellt genererad värld där målet rör sig slumpmässigt. Studien visar att 3DVFH* producerar vägar som håller drönaren närmare målet när målet rör sig efter en fördefinierad väg. Med en slumpvandring i en procedurell värld är A* närmast målet. Resultaten från Raspberry Pi visar också att A* algoritmen sätter prohibitivt höga minneskrav på Raspberry Pi 4 som bara har 2GBRAM. Studerar man påverkan av synbara objekt på avståndet till målet så ser man ingen för 3DVFH* algoritmens egenskap att hålla sig nära, men man ser snabbare bearbetningshastighet när det är färre objekt att upptäcka. A* algoritmen ser en påverkan på dess distans från målet när fler objekt finns att upptäcka. Denna studie visar också hur en kommunikationslänk mellan vägplaneringsalgoritmer och kartapplikationer som stöds utav Carmenta Engine SDK skall implementeras. Detta används för att studera hur lokal geografisk information kan användas i ett spårningssammanhang. Genom att använda två nivåer av geografisk upplösning från VMap data, jämförs påverkan på vägplaneringarnas prestanda. Studien visar att ingen påverkan på prestandan kan ses men att kartapplikationen kräver mer minnesresurser. Genom att använda geografisk information om en region av intresse visar denna applikation hur vägplaneringsalgoritmerna kan fråga kartapplikationen om legaliteten om sin nuvarande position.

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.

Mini Unmanned Aerial Vehicles (mUAVs) have the potential to be a great asset to Wilderness Search and Rescue groups by providing a bird's eye view of the search area. These vehicles can carry a variety of sensors to better understand the world below. This paper proposes using both Infrared (IR) and Visible Spectrum cameras on a mUAV for Wilderness Search and Rescue. It details a method for combining the color and heat information from these two cameras into a single fused display to reduce needed screen space for remote field use. To align the video frames for fusion, a method for simultaneously pre-calibrating the intrinsic and extrinsic parameters of the cameras and their mount using a single multi-spectral calibration rig is also presented. A user study conducted to validate the proposed image fusion methods showed no reduction in performance when detecting objects of interest in the single-screen fused display compared to a side-by-side display. Furthermore, the users' increased performance on a simultaneous auditory task showed that increased performance on a simultaneous auditory task showed that their cognitive load was reduced when using the fused display.

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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