Unmanned aircraft systems (UAS) represent the future of modern aviation. Over the past 10 years their use abroad by the military has become commonplace for surveillance and combat. Unfortunately, their use at home has been far more restrictive. Due to safety and regulatory concerns, UAS are prohibited from flying in the National Airspace System without special authorization from the FAA. One main reason for this is the lack of an on-board pilot to "see and avoid" other air traffic and thereby maintain the safety of the skies. Development of a comparable capability, known as "Sense and Avoid" (SAA), has therefore become a major area of focus. This research focuses on the SAA problem as it applies specifically to small UAS. Given the size, weight, and power constraints on these aircraft, current approaches fail to provide a viable option. To aid in the development of a SAA system for small UAS, various simulation and hardware tools are discussed. The modifications to the MAGICC Lab's simulation environment to provide support for multiple agents is outlined. The use of C-MEX s-Functions to improve simulation performance and code portability is also presented. For hardware tests, two RC airframes were constructed and retrofitted with autopilots to allow autonomous flight. The development of a program to interface with the ground control software and run the collision avoidance algorithms is discussed as well. Intruder sensing is accomplished using a low-power, low-resolution radar for detection and an Extended Kalman Filter (EKF) for tracking. The radar provides good measurements for range and closing speed, but bearing measurements are poor due to the low-resolution. A novel method for improving the bearing approximation using the raw radar returns is developed and tested. A four-state EKF used to track the intruder's position and trajectory is derived and used to provide estimates to the collision avoidance planner. Simulation results and results from flight tests using a simulated radar are both presented. To effectively plan collision avoidance paths a tree-branching path planner is developed. Techniques for predicting the intruder position and creating safe, collision-free paths using the estimates provided by the EKF are presented. A method for calculating the cost of flying each path is developed to allow the selection of the best candidate path. As multiple duplicate paths can be created using the branching planner, a strategy to remove these paths and greatly increase computation speed is discussed. Both simulation and hardware results are presented for validation.

To operate, autonomous systems of necessity employ a variety of sensors to perceive their environment. Many small unmanned aerial vehicles (UAV) are unable to carry redundant sensors due to size, weight, and power (SWaP) constraints. Faults in these sensors can cause undesired behavior, including system instability. Thus, detection of faults in these non-redundant sensors is of paramount importance.The problem of detecting sensor faults in non-redundant sensors on board autonomous aircraft is non-trivial. Factors that make development of a solution difficult include both an inability to perfectly characterize systems and sensors as well as the SWaP constraints inherent with small UAV. An additional challenge is the ability of a fault-detection method to strike a balance between false-alarm rate and detection rate.This thesis explores two model-based methods of fault-detection for non-redundant sensors, a Kalman filter based method and a particle filter based method. The Kalman filter based method employs tests of mean and covariance on the normalized innovation sequence to detect faults, while the particle filter based method uses a function of the average particle weights.The Kalman filter based approach was implemented in real time on board an autonomous rotorcraft using an extended Kalman Filter (EKF). Faults tested included varied levels of bias, drift, and increased noise. Metrics included false-alarm rate, detection rate, and delay to detection. The particle filter based approach was implemented on a simulated system. This was then compared with an implementation of the EKF based approach for the same system. The same fault types and metrics were also used for these tests.The EKF based method of fault-detection performed well onboard the autonomous rotorcraft and should be generalizable to other systems for which an EKF or Kalman filter can be implemented. The theory indicates that the particle filter based algorithm should have performed better, though the simulations showed poor detection characteristics in comparison to the Kalman filter based method. Future work should be performed to explore improvements to the particle filter based method.

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I samband med den globala uppvärmningen ökar efterfrågan för rena och förnybara bränslen alltmer i dagens samhälle. Eftersom flygindustrin idag är ansvarig för samma mängd växthusgaser som all motortrafik i Sverige, skulle ett byte till en avgasfri energikälla för flygfarkoster vara ett stort framsteg. Därför har projektet genom modellering framtagit ett hybridsystem av ett batteri och en bränslecell och undersökt hur kombinationen av olika storlekar på dem presterar i en driftcykel. Då batterier har hög specifik effekt men är tunga, kompletteras de med fördel av bränsleceller, som är lättviktiga och bidrar med uthållig strömförsörjning. På så sätt blir hybriden optimal för flygfarkoster. Kandidatarbetet är en del av projektet Green Raven, ett tvärvetenskapligt samarbete mellan instutitionerna Tillämpad Elektrokemi, Mekatronik och Teknisk Mekanik på Kungliga Tekniska Högskolan. Driftcykelmodelleringen gjordes i Simulink, och flera antaganden gjordes beträffande effektprofilen, samt bränslecellens mätvärden och effekt. Tre olika energihushållningsscheman skapades, vilka bestämde bränslecellseffekten beroende på vätgasnivån och batteriets laddningstillstånd. Skillnaden på systemen var vilka intervall av laddningstillstånd hos batteriet som genererade olika effekt hos bränslecellen.  Det bästa alternativet visade sig vara 0/100-systemet, eftersom det var det enda som inte orsakede någon degradering av bränslecellens kapacitet. / In today’s society, with several environmental challenges such as global warming, the demand for cleanand renewable fuels is ever increasing. Since the aviation industry in Sweden is responsible for the sameamount of greenhouse gas emissions as the motor traffic, a change to a non-polluting energy source forflying vehicles would be considerable progress. Therefore, this project has designed a hybrid system of abattery and a fuel cell and investigated how different combinations of battery and fuel cell sizes perform ina drive cycle, through computer modelling. As batteries possess a high specific power but are heavy, thefuel cells with high specific energy complement them with a sustained and lightweight power supply,which makes the hybrid perfect for aviation. The bachelor thesis is a part of Project Green Raven, aninterdisciplinary collaboration with the institutions of Applied Electrochemistry, Mechatronics andEngineering Mechanics at KTH Royal Institute of Techology. The drive cycle simulations were done inSimulink, and several assumptions regarding the power profile, fuel cell measurements and power weremade. Three different energy management strategies were set up, determining the fuel cell powerdepending on hydrogen availability and state of charge of the battery. The strategies were called 35/65,20/80 and 0/100, and the difference between them was at which state of charge intervals the fuel cellchanged its power output. The best strategy proved to be 0/100, since it was the only option which causedno degradation of the fuel cell whatsoever.

Drones are a very active area of research and object detection is a crucial part in achieving full autonomy of any robot. We investigated how state-of-the-art object detection algorithms perform on image data from a drone. For the evaluation we collected a number of datasets in an indoor office environment with different cameras and camera placements. We surveyed the literature of object detection and selected to research the algorithm R-FCN (Region based Fully Convolutional Network) for the evaluation. The performances on the different datasets were then compared, showing that using footage from a drone may be advantageous in scenarios where the goal is to detect as many objects as possible. Further, it was shown that the network, even if trained on normal angled images, can be used for detecting objects in fish eye images and that usage of a fisheye camera can increase the total number of detected objects in a scene. / Drönare är ett mycket aktivt forskningsområde och objektigenkänning är en viktig del för att uppnå full självstyrning för robotar. Vi undersökte hur dagens bästa objektigenkänningsalgoritmer presterar på bilddata från en drönare. Vi gjorde en literatturstudie och valde att undersöka algoritmen R-FCN (Region based Fully Convolutional Network). För att evaluera algoritmen spelades flera dataset in i en kontorsmiljö med olika kameror och kameraplaceringar. Prestandan på de olika dataseten jämfördes sedan och det visades att användningen av bilder från en drönare kan vara fördelaktig då målet är att hitta så många objekt som möjligt. Vidare visades att nätverket, även om det är tränat på bilder från en vanlig kamera, kan användas för att hitta objekt i vidvinklade bilder och att användningen av en vidvinkelkamera kan öka det totala antalet detekterade objekt i en scen.

Unmanned aerial vehicles (UAVs) is a rapidly expanding area of research due to their versatile usage, such as inspection of places inaccessible to humans and surveillance missions. This creates a demand for a reliable model that can accurately describe the dynamics of the system in order to improve the performance of the vehicle. System identification is a common tool used for the modelling of a system and is essential for developing an accurate and reliable model. The aim of this master's thesis is to develop an accurate non-linear grey-box model, with six degrees of freedom, of a fixed-wing UAV as well as a linearized version of the model. After a literature study a suitable model structure with sixstates and 28 parameters was chosen. The moment of inertia matrix is estimated separately using physical experiments,and the other parameters, related to the aerodynamic coefficients of the UAV, are estimated using flight experiments. Flight experiments are designed in order to capture all of the system dynamics and data was collected accordingly. The parameters are estimated using a prediction error method, which requires the solution of an optimal control problem. The derived models of the UAV are compared to each other and evaluated using model validation. In conclusion, the non-linear grey-box model shows great potential in becoming an accurate model, but further investigation and refining of the model is necessary.

<p>The LiDAR data of the maize used in this research were acquired from different stages, by different sensors, and from different flight heights, which results in different point densities. The ground reference data collected by LiCOR LAI-2200 represented the leaf area index of a two-row plot.</p>

The WITAS project aims to develop technologies to enable an Unmanned Airial Vehicle (UAV) to operate autonomously and intelligently, in applications such as traffic surveillance and remote photogrammetry. Many of the necessary control and reasoning tasks, e.g. state estimation, reidentification, planning and diagnosis, involve prediction as an important component. Prediction relies on models, and such models can take a variety of forms. Model design involves many choices with many alternatives for each choice, and each alternative carries advantages and disadvantages that may be far from obvious. In spite of this, and of the important role of prediction in so many areas, the problem of predictive model design is rarely studied on its own. In this thesis, we examine a range of applications involving prediction and try to extract a set of choices and alternatives for model design. As a case study, we then develop, evaluate and compare two different model designs for a specific prediction problem encountered in the WITAS UAV project. The problem is to predict the movements of a vehicle travelling in a traffic network. The main difficulty is that uncertainty in predictions is very high, du to two factors: predictions have to be made on a relatively large time scale, and we have very little information about the specific vehicle in question. To counter uncertainty, as much use as possible must be made of knowledge about traffic in general, which puts emphasis on the knowledge representation aspect of the predictive model design. The two mode design we develop differ mainly in how they represent uncertainty: the first uses coarse, schema-based representation of likelihood, while the second, a Markov model, uses probability. Preliminary experiments indicate that the second design has better computational properties, but also some drawbacks: model construction is data intensive and the resulting models are somewhat opaque. / <p>Report code: LiU-Tek-Lic-2002:15.</p>

Unmanned Aerial Vehicles are more and more used in various fields. Many of the Flight Missions they execute are remote-controlled by human operators. Their application range could be greatly extended if unsupervised computer-controlled Flight Missions were possible. To reach the goal of being able to run unsupervised Flight Missions, many hurdles are yet to be cleared. One of the difficult tasks is to provide a control mechanism that is capable of reacting to environmental changes, such as bad weather, unexpected obstacles or system failures. To get closer to the goal of unsupervised Flight Missions, existing Expert System mechanisms along with other technologies that provide automated sensor data gathering and actor control are explored and the limitations that hold back progress are highlighted. Limited Flight control approaches that use data from different sensors to safely adapt the drone’s behaviour and its mission execution are the main focus of the thesis. Furthermore an Expert-System-based concept and implementation for decisionmaking during Adaptive Flight Missions are presented and evaluated for their remaining limitations.

I inledningen av den storskaliga ryska invasionen av Ukraina tilldrog sig en relativt ny typ av vapensystem, den beväpnade drönaren, stor uppmärksamhet. Denna typ av system och dess framtida roll inom krigföringen har varit och är fortfarande omdebatterad. Forskningen har fram till nyligen, till stor del, saknat relevant empiri för att undersöka den beväpnade drönarens effekt i en mellanstatlig högteknologisk konflikt. Syftet med denna studie har varit att undersöka en mindre stats nyttjande av UCAV som ett sätt att projicera luftmakt. I det här fallet genom en fallstudie av konflikten i Ukraina. För att belysa Ukrainas användande av UCAV har en luftmaktsteori benämnd, Underdog’s Model, nyttjats. Modellen, framtagen av Arash Heydarian Pashakhanlou, syftar till att beskriva hur en mindre stat genom goda prestationer inom sex specifika faktorer, kan gå segrande ur en väpnad konflikt mot en starkare motståndare. Studien visar att Ukraina i mycket hög grad uppnått goda prestationer inom modellens faktorer. Effekten av de ukrainska UCAV-operationerna förefaller också, i krigets inledande fas vara mycket stor. Efter krigets första månader avtar dock denna effekt något, på grund av en motståndarens anpassning och nyttjande av motmedel. Underdog’s Model har en relativt god, om än inte fullständig, förklaringskraft beträffande Ukrainas användning av UCAV i konflikten. Nyttjande av UCAV framstår som en kostnadseffektiv väg för en småstat att projicera luftmakt i en asymmetrisk konflikt. Dock har vissa sårbarheter hos sådana system blivit mer framträdande varefter konflikten fortskridit.