Made available in DSpace on 2014-08-20T14:25:47Z (GMT). No. of bitstreams: 1 dissertacao_theo_hackbart.pdf: 2892534 bytes, checksum: 4bd14c0e449a0233a7d5e1098733bf8f (MD5) Previous issue date: 2008-11-28 / This work describes the construction of an unmanned aerial vehicle (UAV) with electrical propulsion and instrumentation in order to obtain the vertical and horizontal profiles by measuring temperature, pressure, and GPS location, in surface atmosphere layer between two heterogeneous surfaces near terrain. Using embedded electronic, one developed and built a small platform with approximately 50 grams in order to collect and acquire meteorological data. The electronic circuit is based on a PIC microcontroller with pressure and temperature sensors, and GPS. After capturing aerosondes data, the first analysis was done. By using these data, surface boundary layer structure compatible with general concepts in the literature were identified. It was also identified some details of closed structures to the frontier which separates the heterogeneous region of landcover. According to several researches aiming at sdudying different alternatives to obtaining the data, one demonstrated through this experimental that small UAVs can be very useful for agrometheorology and metheorology researches because they provide the atmospheric sounding to be performed in places where the access is difficult. Furthermore, they present a low cost. / Este trabalho descreve a construção de um veículo aéreo não tripulado (VANT) com propulsão elétrica, instrumentalizado, para obter perfis verticais e horizontais de medidas de temperatura, pressão e localização por GPS, na camada superficial da atmosfera entre duas superfícies heterogêneas próximas ao solo. Utilizando eletrônica embarcada, desenvolveu-se e implementou-se uma pequena plataforma com aproximadamente 50 gramas para coleta e aquisição de dados meteorológicos. O circuito eletrônico é baseado no microcontrolador PIC com sensores de temperatura, pressão e GPS. Após a captação de dados na aerossondagem, foi realizada uma primeira análise. Usando esses dados, identificaram-se as estruturas da camada-limite superficial compatíveis com os conceitos gerais descritos na literatura, assim como detalhes da estrutura próximos às fronteiras de separação de regiões heterogêneas de coberturas de solo. Tendo em vista as inúmeras pesquisas que visam a alternativas diferenciadas para obtenção de tais dados, mostrou-se, através deste experimento, que pequenas aeronaves não-tripuladas podem ser de grande valia para pesquisas em agrometeorologia e meteorologia, pois se destacam por possibilitar a realização de sondagens atmosféricas em lugares de difícil acesso, tendo um baixo custo.

La planification HTN (Hierarchical Task Network, ou Réseau Hiérarchique de Tâches) est une approche très souvent utilisée pour produire des séquences de tâches servant à contrôler des systèmes intelligents. Cette thèse présente le planificateur HATP (Hierarchical Agent-base Task Planner, ou Planificateur Hiérarchique centré Agent) qui étend la planification HTN classique en enrichissant la représentation des domaines et leur sémantique afin d'être plus adaptées à la robotique, tout en offrant aussi une prise en compte des humains. Quand on souhaite générer un plan pour des robots tout en prenant en compte les humains, il apparaît que les problèmes sont complexes et fortement interdépendants. Afin de faire face à cette complexité, nous avons intégré à HATP un planificateur géométrique apte à déduire l'effet réel des actions sur l'environnement et ainsi permettre de considérer la visibilité et l'accessibilité des éléments. Cette thèse se concentre sur l'intégration de ces deux planificateurs de nature différente et étudie comment par leur combinaison ils permettent de résoudre de nouvelles classes de problèmes de planification pour la robotique. / Hierarchical Task Network (HTN) planning is a popular approach to build task plans to control intelligent systems. This thesis presents the HATP (Hierarchical Agent-based Task Planner) planning framework which extends the traditional HTN planning domain representation and semantics by making them more suitable for roboticists, and by offering human-awareness capabilities. When computing human-aware robot plans, it appears that the problems are very complex and highly intricate. To deal with this complexity we have integrated a geometric planner to reason about the actual impact of actions on the environment and allow to take into account the affordances (reachability, visibility). This thesis presents in detail this integration between two heterogeneous planning layers and explores how they can be combined to solve new classes of robotic planning problems

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<div>With the constant advancement of modern radio technology, the safety of radio communication has become a growing concern for us. Communication has become an essential component, particularly in the application of modern technology such as unmanned aerial vehicle (UAV). As a result, it is critical to ensure that a drone can fly safely and reliably while completing duties. Simultaneously, machine learning (ML) is rapidly developing in the twenty-first century. For example, ML is currently being used in social media and digital marking for predicting and addressing users' varies interests. This also serves as the impetus for this thesis. The goal of this thesis is to combine ML and radio communication to identify and classify UAV interference with high accuracy.</div><div>In this work, a ML approach is explored for detecting and classifying jamming attacks against orthogonal frequency division multiplexing (OFDM) receivers, with applicability to UAVs. Four types of jamming attacks, including barrage, protocol-aware, single-tone, and successive-pulse jamming, are launched and analyzed using software-defined radio (SDR). The jamming range, launch complexity, and attack severity are all considered qualitatively when evaluating each type. Then, a systematic testing procedure is established, where a SDR is placed in the vicinity of a drone to extract radiometric features before and after a jamming attack is launched. Traditional ML methods are used to create classification models with numerical features such as signal-to-noise ratio (SNR), energy threshold, and important OFDM parameters. Furthermore, deep learning method (i.e., convolutional neural networks) are used to develop classification models trained with spectrogram images filling in it. Quantitative indicators such as detection and false alarm rates are used to evaluate the performance of both methods. The spectrogram-based model correctly classifies jamming with a precision of 99.79% and a false-alarm rate of 0.03%, compared to 92.20% and 1.35% for the feature-based counterpart.</div>

Measurements of root zone soil moisture play large roles in our understanding of the water cycle, weather, climate, land-heat exchanges, drought forecasting, and agriculture. Current measurements are made using a combination of ground-based sampling and active and passive microwave remote sensing. Signals of Opportunity (SoOp) has emerged as a promising method for sensing soil moisture, using satellite communication signals to make bi-static reflectometry measurements. The current combination of ground and satellite-based measurements for soil moisture results in a gap of useful spatial and temporal resolutions, as well as limited soil penetration depth. This thesis developed and constructed an Unmanned Aerial Vehicle (UAV) mountable, I-band SoOp instrument with calibration capabilities, along with supporting specular point mapping and mission planning software. This work advances the creation of a compact, mobile, root zone soil moisture (RZSM) remote sensing system.

<p>Unmanned aerial vehicles (UAVs) equipped with imaging systems and integrated global navigation satellite system/inertial navigation system (GNSS/INS) are used for a variety of applications. Disaster relief, infrastructure monitoring, precision agriculture, and ecological forestry growth monitoring are among some of the applications that utilize UAV imaging systems. For most applications, accurate 3D spatial information from the UAV imaging system is required. Deriving reliable 3D coordinates is conditioned on accurate geometric calibration. Geometric calibration entails both spatial and temporal calibration. Spatial calibration consists of obtaining accurate internal characteristics of the imaging sensor as well as estimating the mounting parameters between the imaging and the GNSS/INS units. Temporal calibration ensures that there is little to no time delay between the image timestamps and corresponding GNSS/INS position and orientation timestamps. Manual and automated spatial calibration have been successfully accomplished on a variety of platforms and sensors including UAVs equipped with frame and push-broom line cameras. However, manual and automated temporal calibration has not been demonstrated on both frame and line camera systems without the use of ground control points (GCPs). This research focuses on manual and automated spatial and temporal system calibration for UAVs equipped with GNSS/INS frame and line camera systems. For frame cameras, the research introduces two approaches (direct and indirect) to correct for time delay between GNSS/INS recorded event markers and actual time of image exposures. To ensure the best estimates of system parameters without the use of ground control points, an optimal flight configuration for system calibration while estimating time delay is rigorously derived. For line camera systems, this research presents the direct approach to estimate system calibration parameters including time delay during the bundle block adjustment. The optimal flight configuration is also rigorously derived for line camera systems and the bias impact analysis is concluded. This shows that the indirect approach is not a feasible solution for push-broom line cameras onboard UAVs due to the limited ability of line cameras to decouple system parameters and is confirmed with experimental results. Lastly, this research demonstrates that for frame and line camera systems, the direct approach can be fully-automated by incorporating structure from motion (SfM) based tie point features. Methods for feature detection and matching for frame and line camera systems are presented. This research also presents the necessary changes in the bundle adjustment with self-calibration to successfully incorporate a large amount of automatically-derived tie points. For frame cameras, the results show that the direct and indirect approach is capable of estimating and correcting this time delay. When a time delay exists and the direct or indirect approach is applied, horizontal accuracy of 1–3 times the ground sampling distance (GSD) can be achieved without the use of any ground control points (GCPs). For line camera systems, the direct results show that when a time delay exists and spatial and temporal calibration is performed, vertical and horizontal accuracy are approximately that of the ground sample distance (GSD) of the sensor. Furthermore, when a large artificial time delay is introduced for line camera systems, the direct approach still achieves accuracy less than the GSD of the system and performs 2.5-8 times better in the horizontal components and up to 18 times better in the vertical component than when temporal calibration is not performed. Lastly, the results show that automated tie points can be successfully extracted for frame and line camera systems and that those tie point features can be incorporated into a fully-automated bundle adjustment with self-calibration including time delay estimation. The results show that this fully-automated calibration accurately estimates system parameters and demonstrates absolute accuracy similar to that of manually-measured tie/checkpoints without the use of GCPs.</p>

Unmanned Aerial Vehicles, often called drones or abbreviated as UAVs, have been popularised and used by civilians for recreational use since the early 2000s. A majority of the entry- level commercial drones on the market are based on a WiFi connection with a controller, usually a smart phone. This makes them vulnerable to various WiFi attacks, which are evaluated and tested in this thesis, specifically on the Ryze Tello drone. Several threats were identified through threat modelling, in which a set of them was selected for penetration testing. This is done in order to answer the research question: How vulnerable is the Ryze Tello drone against WiFi based attacks? The answer to the research question is that the Ryze Tello drone is relatively safe, with the exception of it not having a default password for the network. A password was set for the network, however it was still exploited through a dictionary attack. This enabled attacks such as injecting flight instructions as well as the ability to gain access to the video feed of the drone while simultaneously controlling it through commands in terminal. / Drönare, eller UAV från engelskans Unmanned Aerial Vehicle, har ökat i popularitet bland privatpersoner sedan tidigt 2000tal. En majoritet av drönare för nybörjare är baserade på WiFi och styrs med en kontroll som vanligtvis är en smart phone. Detta innebär att dessa drönare kan vara sårbara för olika typer av attacker på nätverket, vilket utvärderas och testas i denna rapport på drönaren Ryze Tello. Flera hot identifierades med hotmodellering och ett flertal valdes ut för penetrationtest. Detta genomförs med syftet att svara på forskningsfrågan: Hur sårbar är Ryze Tello mot WiFi baserade attacker? Svaret på forskningsfrågan är att drönaren Ryze Tello är relativt säker, med undantaget att den inte har ett standardlösenord. Ett lösenord sattes på nätverket, men lösenordet knäcktes ändå med en ordboksattack. Detta möjliggjorde attacker så som instruktionsinjicering och förmågan att se videoströmmen från drönaren samtidigt som den kan kontrolleras via kommandon i terminalen.

<p>Safety Management Systems (SMS) in the aviation industry is increasingly an essential aspect of identifying hazards and managing the associated risks. While SMS has become commonplace and is often a regulatory requirement for air carriers, it remains voluntary for many other aviation service providers such as airports. Over the past decade, commercial UAS operations have significantly increased, leading to safety and economic challenges for airports. This research studied the current state of SMS and commercial UAS operations at airports. This research utilized a mix of quantitative and qualitative methods, which included an extensive literature review, interviews, and a survey of airport stakeholders. The literature review confirmed an increase in UAS hazards and risks within the airport operating area coupled with immature SMS practices that address these UAS operations. To build on the findings from the review of literature, a survey instrument was developed, distributed to airport stakeholders, and the responses were statistically analyzed. To gain greater insight into these findings, researchers interviewed three airport subject matter experts. The study compared the airports current state of SMS with UAS operations, the airport stakeholder’s level of familiarity with related policies, and their need for additional UAS SMS guidance material or training. Research results suggest a need for further development and adoption of robust SMS practices at airports along with education and training. This study may assist airport stakeholders, UAS operators, and regulators to further develop robust safety and risk management practices that support safe UAS operations within the airport operating area.</p>

The Micro Aerial Vehicless (MAVs) are gaining attention in numerous applications asthese platforms are cheap and can do complex maneuvers. Moreover, most of the commer-cially available MAVs are equipped with a mono-camera. Currently, there is an increasinginterest to deploy autonomous mono-camera MAVs with obstacle avoidance capabilitiesin various complex application areas. Some of the application areas have moving obstaclesas well as stationary, which makes it more challenging for collision avoidance schemes.This master thesis set out to investigate the possibility to avoid moving and station-ary obstacles with a single camera as the only sensor gathering information from thesurrounding environment.One concept to perform autonomous obstacle avoidance is to predict the time near-collision based on a Convolution Neural Network (CNN) architecture that uses the videofeed from a mono-camera. In this way, the heading of the MAV is regulated to maximizethe time to a collision, resulting in the avoidance maneuver. Moreover, another interestingperspective is when due to multiple dynamic obstacles in the environment there aremultiple time predictions for different parts of the Field of View (FoV). The method ismaximizing time to a collision by choosing the part with the largest time to collision.However, this is a complicated task and this thesis provides an overview of it whilediscussing the challenges and possible future directions. One of the main reason was thatthe available data set was not reliable and was not provide enough information for theCNN to produce any acceptable predictions.Moreover, this thesis looks into another approach for avoiding collisions, using objectdetection method You Only Lock Once (YOLO) with the mono-camera video feed. YOLOis a state-of-the-art network that can detect objects and produce bounding boxes in real-time. Because of YOLOs high success rate and speed were it chosen to be used in thisthesis. When YOLO detects an obstacle it is telling where in the image the object is,the obstacle pixel coordinates. By utilizing the images FoV and trigonometry can pixelcoordinates be transformed to an angle, assuming the lens does not distort the image.This position information can then be used to avoid obstacles. The method is evaluated insimulation environment Gazebo and experimental verification with commercial availableMAV Parrot Bebop 2. While the obtained results show the efficiency of the method. To bemore specific, the proposed method is capable to avoid dynamic and stationary obstacles.Future works will be the evaluation of this method in more complex environments with multiple dynamic obstacles for autonomous navigation of a team of MAVs. A video ofthe experiments can be viewed at:https://youtu.be/g_zL6eVqgVM.

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