[ES] Aquesta tesi presenta els resultats de la feina de recerca dut a terme sobre el modelatge i el disseny de controladors per a micro-aeronaus no tripulades mitjançant tècniques d'optimització multi-objectiu. Dos principals camps d'estudi estan presents al llarg d'ella. D'una banda, l'estudi de com modelar i controlar plataformes aèries de petita envergadura. I, de l'altra, l'estudi sobre l'ús de tècniques heurístiques d'optimització multi-objectiu per aplicar en el procés de parametrització de models i controladors en micro-aeronaus no tripulades. S'obtenen com a resultat principal una sèrie d'eines que permeten prescindir d'experiments en túnels de vent o de sensòrica d'alt cost, passant directament a la utilització de dades de vol experimental a la identificació paramètrica de models dinàmics. A més, es demostra com la utilització d'eines d'optimització multi-objectiu en diferents fases de desenvolupament de controladors ajuda a augmentar el coneixement sobre la plataforma a controlar i augmenta la fiabilitat i robustesa dels controladors desenvolupats, disminuint el risc de passar de les fases prèvies de el disseny a la validació en vol real. / [CA] Esta tesis presenta los resultados del trabajo de investigación llevado a cabo sobre el modelado y el diseño de controladores para micro-aeronaves no tripuladas mediante técnicas de optimización multi-objetivo. Dos principales campos de estudio están presentes a lo largo de ella. Por un lado, el estudio de cómo modelar y controlar plataformas aéreas de pequeña envergadura. Y, por otro, el estudio sobre el empleo de técnicas heurísticas de optimización multi-objetivo para aplicar en el proceso de parametrización de modelos y controladores en micro-aeronaves no tripuladas. Se obtienen como resultado principal una serie de herramientas que permiten prescindir de experimentos en túneles de viento o de sensórica de alto coste, pasando directamente a la utilización de datos de vuelo experimental en la identificación paramétrica de modelos dinámicos. Además, se demuestra como la utilización de herramientas de optimización multi-objetivo en diferentes fases del desarrollo de controladores ayuda a aumentar el conocimiento sobre la plataforma a controlar y aumenta la fiabilidad y robustez de los controladores desarrollados, disminuyendo el riesgo de pasar de las fases previas del diseño a la validación en vuelo real. / [EN] This thesis presents the results of the research work carried out on the modelling and design of controllers for micro-unmanned aerial vehicles by means of multi-objective optimization techniques. Two main fields of study are present throughout it. On one hand, the study of how to model and control small aerial platforms. And, on the other, the study on the use of heuristic multi-objective optimization techniques to apply in the process of models and controllers parameterization in micro-unmanned aerial vehicles. The main result is a series of tools that make it possible manage without wind tunnel experiments or high-cost air-data sensors, going directly to the use of experimental flight data in the parametric identification of dynamic models. In addition, a demonstration is given on how the use of multi-objective optimization tools in different phases of controller development helps to increase knowledge about the platform to be controlled and increases the reliability and robustness of the controllers developed, reducing the risk of hoping from the initial design phases to validation in real flight. / Velasco Carrau, J. (2020). Unmanned Aerial Vehicles Modelling and Control Design. A Multi-Objective Optimization Approach [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/156034 / TESIS

<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>

No description available.

This thesis presents the design of an autopilot capable of flying small unmanned aerial vehicles with wingspans less then 21 inches. The autopilot is extremely small and lightweight allowing it to fit in aircraft of this size. The autopilot features an advanced, highly autonomous flight control system with auto-launch and auto-landing algorithms. These features allow the autopilot to be operated by a wide spectrum of skilled and unskilled users. Innovative control techniques implemented in software, coupled with light weight, robust, and inexpensive hardware components were used in the design of the autopilot.

Coalitions are collections of agents that join together to solve a common problem that either cannot be solved individually or can be solved more efficiently as a group. Each individual agent has capabilities that can benefit the group when working together as a coalition. Typically, individual capabilities are joined together in an additive way when forming a coalition. This work will introduce a new operator that is used when combining capabilities, and suggest that the behavior of the operator is contextual, depending on the nature of the capability itself. This work considers six different capabilities of Unmanned Air Vehicles (UAV) and determines the nature of the new operator in the context of each capability as coalitions (squadrons) of UAVs are formed. Coalitions are formed using three different search algorithms, both with and without heuristics: Depth-First, Depth-First Iterative Deepening, and Genetic Algorithm (GA). The effectiveness of each algorithm is evaluated. Multi agent-based UAV simulation software was developed and used to test the ideas presented. In addition to coalition formation, the software aims to address additional multi-agent issues such as agent identity, mutability, and communication as applied to UAV systems, in a realistic simulated environment. Social potential fields provide a means of modeling a clustering attractive force at the same time as a collision-avoiding repulsive force, and are used by the simulation to maintain aircraft position relative to other UAVs.

Unmanned Aerial Vehicles (UAVs) provides a platform for many automated tasks and with an ever increasing advances in computing, these tasks can be more complex. The use of UAVs is expanded in this thesis with the goal of Search and Rescue (SAR), where a UAV can assist fast responders to search for a lost person and relay possible search areas back to SAR teams. To identify a person from an aerial perspective, low-level Histogram of Oriented Gradients (HOG) feature descriptors are used over a segmented region, provided from thermal data, to increase classification speed. This thesis also introduces a dataset to support a Bird’s-Eye-View (BEV) perspective and tests the viability of low level HOG feature descriptors on this dataset. The low-level feature descriptors are known as Boosted Histogram of Oriented Gradients (BHOG) features, which discretizes gradients over varying sized cells and blocks that are trained with a Cascaded Gentle AdaBoost Classifier using our compiled BEV dataset. The classification is supported by multiple sensing modes with color and thermal videos to increase classification speed. The thermal video is segmented to indicate any Region of Interest (ROI) that are mapped to the color video where classification occurs. The ROI decreases classification time needed for the aerial platform by eliminating a per-frame sliding window. Testing reveals that with the use of only color data iv and a classifier trained for a profile of a person, there is an average recall of 78%, while the thermal detection results with an average recall of 76%. However, there is a speed up of 2 with a video of 240x320 resolution. The BEV testing reveals that higher resolutions are favored with a recall rate of 71% using BHOG features, and 92% using Haar-Features. In the lower resolution BEV testing, the recall rates are 42% and 55%, for BHOG and Haar-Features, respectively.

No description available.

This thesis aims to analyze how object detectors perform under winter weather conditions, specifically in areas with varying degrees of snow cover. The investigation will evaluate the effectiveness of commonly used object detection methods in identifying vehicles in snowy environments, including YOLO v8, Yolo v5, and Faster R-CNN. Additionally, the study explores the method of labeling vehicle objects within a set of image frames for the purpose of high-quality annotations in terms of correctness, details, and consistency. Training data is the cornerstone upon which the development of machine learning is built. Inaccurate or inconsistent annotations can mislead the model, causing it to learn incorrect patterns and features. Data augmentation techniques like rotation, scaling, or color alteration have been applied to enhance some robustness to recognize objects under different alterations. The study aims to contribute to the field of deep learning by providing valuable insights into the challenges of detecting vehicles in snowy conditions and offering suggestions for improving the accuracy and reliability of object detection systems. Furthermore, the investigation will examine edge devices' real-time tracking and detection capabilities when applied to aerial images under these weather conditions. What drives this research is the need to delve deeper into the research gap concerning vehicle detection using drones, especially in adverse weather conditions. It highlights the scarcity of substantial datasets before Mokayed et al. published the Nordic Vehicle Dataset. Using unmanned aerial vehicles(UAVs) or drones to capture real images in different settings and under various snow cover conditions in the Nordic region contributes to expanding the existing dataset, which has previously been restricted to non-snowy weather conditions. In recent years, the leverage of drones to capture real-time data to optimize intelligent transport systems has seen a surge. The potential of drones in providing an aerial perspective efficiently collecting data over large areas to precisely and timely monitor vehicular movement is an area that is imperative to address. To a greater extent, snowy weather conditions can create an environment of limited visibility, significantly complicating data interpretation and object detection. The emphasis is set on edge devices' real-time tracking and detection capabilities, which in this study introduces the integration of edge computing in drone technologies to explore the speed and efficiency of data processing in such systems.