Yes / Unmanned Aerial Vehicles (UAVs), commercially known as drones, have received great attention. This is due to their versatility and applicability to a large number of domains such as surveillance system, aerial photography, traffic control, flyable base stations to provide a broadband coverage and even for future urban transportation services. In this paper, the optimal distance between multiple aerial base stations has analytically been derived, based on an aerial coverage area computation. This is a fundamental wireless metric that can significantly minimise the intra-overlapped coverage and also can enhance wireless coverage connectivity and performance of aerial wireless networks. The novelty of our approach brings a better aerial optimal design understanding for UAVs communications performance without the need for establishing an aerial deployment setup.

The work presented in this dissertation on piezoelectric inchworm motors (IWM) is part of a process to gain an understanding of the design, analysis and testing of this smart actuator technology. This work will form the foundation of what will hopefully lead to the realisation of a production-ready IWM design to be used in energy-scarce, battery-operated Unmanned Aerial Vehicles (UAVs), and forms part of a larger national drive to expand the UAV industry in South Africa. Although the principles used in the design of IWMs are well known, a new innovation is employed. A novel way to increase the force capacity of IWMs without compromising on the speed or displacement when compared to conventional methods is shown to be effective, and was used for the first time on IWMs. The use of a simple design equation is demonstrated to be useful in predicting the load limits and step displacements. Challenges of finding a correlation between predicted and measured performance values are discussed and solutions are presented. The history of IWMs and some background on piezoelectricity are given for the reader not familiar with these. The use of micro ridges on the clamp mechanisms is explored. The effects of the control signals on the mechanism of the motor are discussed in detail and some important comments on electrical controllers are made. The emphasis is on designing a strong motor that capitalises on the high-force density of piezoelectric material. / Dissertation (MEng)--University of Pretoria, 2014. / gm2014 / Mechanical and Aeronautical Engineering / unrestricted

A new Trapped Vortex Combustor (TVC) concept has been studied for applications such as those in Unmanned Aerial Vehicles (UAVs) as it offers potential for superior flame stability and low pressure loss. Flame stability is ensured by a strong vortex in a physical cavity attached to the combustor wall, and low pressure loss is due to the absence of swirl. Earlier studies on a compact combustor concept showed that there are issues with ensuring stable combustion over a range of operating conditions. The present work focuses on experimental studies and numerical simulations to study the stability issues and performance optimization in this compact single-cavity TVC configuration. For performing numerical simulations, an accurate and yet computationally affordable Modified Eddy Dissipation Concept combustion model is built upon the KIVA-3V platform to account for turbulence-chemistry interactions. Detailed validation with a turbulent non-premixed CH4/H2/N2 flame from literature showed that the model is sufficiently accurate and the effect of various simulation strategies is assessed. Transient flame simulation capabilities are assessed by comparison with experimental data from an acoustically excited oscillatory H2-air diffusion flame reported in literature. Subsequent to successful validation of the model, studies on basic TVC flow oscillations are performed. Frequencies of flow oscillations are found to be independent of flow velocities and cavity length, but dependent on the cavity depth. Cavity injection and combustion individually affect the magnitude of flow oscillations but do not significantly alter the resonant frequencies. Reacting flow experiments and flow visualization studies in an existing experimental TVC rig with optical access and variable cavity L/D ratio show that TVC flame stability depends strongly on the cavity air velocity. A detailed set of numerical simulations also confirms this and helps to identify three basic modes of TVC flame stabilization. A clockwise cavity vortex stabilized flame is formed at low cavity air velocities relative to the mainstream, while a strong anticlockwise cavity vortex is formed at high cavity air velocities and low L/Ds. At intermediate conditions, the cavity vortex structure is found to be in a transition state which leads to large scale flame instabilities and flame blow-out. For solving the flame instability problem, a novel strategy of incorporating a flow guide vane is proposed to establish the advantageous anticlockwise vortex without the use of cavity air. Experimental results with the modified configuration are quite encouraging for TVC flame stability at laboratory conditions, while numerical results show good stability even at extreme operating conditions. Further design optimization studies are performed in a multi-parameter space using detailed simulations. From the results, a strategy of using inclined struts in the main flow path along with the flow guide vane seems most promising. This configuration is tested experimentally and results pertaining to pressure drop, pattern factor and flame stability are found to be satisfactory.

The main objective of this thesis is to propose a localization method for a UAV using various computer vision and machine learning techniques. It plays a major role in planning the strategy for the flight, and acts as a navigational contingency method, in event of a GPS failure. The implementation of the algorithms employs high processing capabilities of the graphics processing unit, making it more efficient. The method involves the working of various neural networks, working in synergy to perform the localization. This thesis is a part of a collaborative project between The University of North Texas, Denton, USA, and the University of Windsor, Ontario, Canada. The localization has been divided into three phases namely object detection, recognition, and location estimation. Object detection and position estimation were discussed in this thesis while giving a brief understanding of the recognition. Further, future strategies to aid the UAV to complete the mission, in case of an eventuality, like the introduction of an EDGE server and wireless charging methods, was also given a brief introduction.

This dissertation explores the realm of high-accuracy localization through the utilization of ranging-based techniques, encompassing a spectrum of signals ranging from low-frequency ultrasound acoustic signals to more intricate high-frequency signals like Wireless Fidelity (Wi-Fi) IEEE 802.11az, 5G New Radio (NR), and 6G. Moreover, another contribution is the conception of a novel timing mechanism and synchronization protocol grounded in tunable quantum photonic oscillators. In general, our primary focus is to facilitate precise indoor localization, where conventional GPS signals are notably absent. To showcase the significance of this innovation, we present two vital use cases at the forefront: drone localization and metaverse user positioning. In the context of indoor drone localization, the spectrum of applications ranges from recreational enthusiasts to critical missions requiring pinpoint accuracy. At the hobbyist level, drones can autonomously navigate intricate indoor courses, enriching the recreational experience. As a finer illustration of a hobbyist application, consider the case of ``follow me drones". These specialized drones are tailored for indoor photography and videography, demanding an exceptionally accurate autonomous flight capability. This precision is essential to ensure the drone can consistently track and capture its designated subject, even as it moves within the confined indoor environment. Moving on from hobby use cases, the technology extends its profound impact to more crucial scenarios, such as search and rescue operations within confined spaces. The ability of drones to localize with high precision enhances their autonomy, allowing them to maneuver seamlessly, even in environments where human intervention proves challenging. Furthermore, the technology holds the potential to revolutionize the metaverse. Within the metaverse, where augmented and virtual realities converge, the importance of high-accuracy localization is amplified. Immersive experiences like Augmented/Virtual/Mixed Reality (AR/VR/MR) gaming rely heavily on precise user positioning to create seamless interactions between digital and physical environments. In entertainment, this innovation sparks innovation in narrative design, enhancing user engagement by aligning virtual elements with real-world surroundings. Beyond entertainment, applications extend to areas like telemedicine, enabling remote medical procedures with virtual guidance that matches physical reality. In light of all these examples, the imperative for an advanced high-accuracy localization system has become increasingly pronounced. The core objective of this dissertation is to address this pressing need by engineering systems endowed with exceptional precision in localization. Among the array of potential techniques suitable for GPS-absent scenarios, we have elected to focus on ranging-based methods. Specifically, our methodologies are built upon the fundamental principles of time of arrival, time difference of arrival, and time of flight measurements. In essence, each of our devised systems harnesses the capabilities of beacons such as ultrasound acoustic sensors, 5G femtocells, or Wi-Fi access points, which function as the pivotal positioning nodes. Through the application of trilateration techniques, based on the calculated distances between these positioning nodes and the integrated sensors on the drone or metaverse user side, we facilitate robust three-dimensional localization. This strategic approach empowers us to realize our ambition of creating localization systems that not only compensate for the absence of GPS signals but also deliver unparalleled accuracy and reliability in complex and dynamic indoor environments. A significant challenge that we confronted during our research pertained to the disparity in z-axis localization performance compared to that of the x-y plane. This nuanced yet pivotal concern often remains overlooked in much of the prevailing state-of-the-art literature, which predominantly emphasizes two-dimensional localization methodologies. Given the demanding context of our work, where drones and metaverse users navigate dynamically across all three dimensions, the imperative for three-dimensional localization became evident. To address this, we embarked on a comprehensive analysis, encompassing mathematical derivations of error bounds for our proposed localization systems. Our investigations unveiled that localization errors trace their origins to two distinct sources: errors induced by ranging-based factors and errors stemming from geometric considerations. The former category is chiefly influenced by factors encompassing the quality of measurement devices, channel quality in which the signal communication between the sensor on the user and the positioning nodes takes place, environmental noise, multipath interference, and more. In contrast, the latter category, involving geometry-induced errors, arises primarily from the spatial configuration of the positioning nodes relative to the user. Throughout our journey, we dedicated efforts to mitigate both sources of error, ensuring the robustness of our system against diverse error origins. Our approach entails a two-fold strategy for each proposed localization system. Firstly, we introduce innovative techniques such as Frequency-Hopping Spread Spectrum (FHSS) and Frequency-Hopping Code Division Multiple Access (FH-CDMA) and incorporate devices such as Reconfigurable Intelligent Surfaces (RIS) and photonic oscillators to fortify the system against errors stemming from ranging-related factors. Secondly, we devised novel evolutionary-based optimization algorithms, adept at addressing the complex NP-Hard challenge of optimal positioning node placement. This strategic placement mitigates the impact of geometry-induced errors on localization accuracy across the entire environmental space. By meticulously addressing both these sources of error, our localization systems stand as a testament to comprehensive robustness and accuracy. Our methodologies not only extend the frontiers of three-dimensional localization but also equip the systems to navigate the intricacies of indoor environments with precision and reliability, effectively fulfilling the evolving demands of drone navigation and metaverse user interaction. / Doctor of Philosophy / In this dissertation, we first explore some promising substitutes for the Global Positioning System (GPS) for the autonomous navigation of drones and metaverse user positioning in indoor spaces. Then, we will make the scope of research more comprehensive and try to explore substitutes to GPS for autonomous navigation of drones in general, both in indoor environments and outdoors. For the first part, we make our small indoor GPS. Similar to GPS, in our system, a receiver onboard the drone or the metaverse user can receive signals from our small semi-satellites in the room, and with that, it can localize itself. The idea is very similar to how the well-known GPS works, with some modifications. Unlike the GPS, we are using acoustic ultrasound signals or some RF signal based on 5G or Wi-Fi for transmission. Also, we have more freedom compared to GPS because, in GPS, they have to transmit signals from far ahead distances, whereas, in our scenario, it is just a room in which we put all of our semi-satellite transmitters. Moreover, we can put them anywhere we want in the room. This is, in fact important, because the positions of these semi-satellites have a huge effect on the accuracy of our system. Also, we can decide how many of them we need to cover every point in the room and not have any blind spots. We propose our novel techniques for finding the optimal placement to improve localization accuracy. In GPS, they propose a technique that is suitable for the case of those satellites and their distance to the targets. Similarly, we offer our novel techniques to have a robust transmission against noise and other factors and guarantee a localization scheme with high accuracy. All being said, our proposed system for indoor localization of drones and metaverse users in three dimensions has considered all the possible sources of error and proposed solutions to conquer them; hence a robust system with high accuracy in three-dimensional space.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: The development of a control allocation system for use as part of a fault-tolerant control (FTC) system in unmanned aerial vehicles (UAVs) is presented. This system plays a vital role in minimising the possibility that a fault will necessitate the reconfiguration of the control, guidance or navigation systems of the aircraft by minimising the difference between the desired and achievable aircraft performance parameters. This is achieved by optimising the allocation of control effort commanded by the virtual actuators to the physical actuators present on the aircraft. A simple general six degree of freedom aircraft model is presented that contains all of the relevant terms needed to find the trim biases of the aircraft actuators and evaluate the performance of the virtual actuators. This model was used to develop a control allocation formulation that optimises the performance of the virtual actuators of the aircraft while minimising adverse effects and avoiding actuator saturation. The resulting problem formulation was formulated as a multi-objective optimisation problem which was solved using the sequential quadratic programming method. The control allocation system was practically implemented and tested. A number of failure categories of varying severity were defined and two aircraft with different levels of actuator redundancy were used to test the system. The control allocation algorithm was evaluated for each failure category, aircraft test case and for a number of differing control allocation system configurations. A number of enhancements were then made to the control allocation system which included adding frequency-based allocation and adapting the algorithm for an unconventional ducted-fan UAV. The control allocation system is shown to be applicable to a number of different conventional aircraft configurations with no alterations as well as being applicable to unconventional aircraft with minor alterations. The control allocation system is shown to be capable of handling both single and multiple actuator failures and the importance of actuator redundancy is highlighted as a factor that influences the effectiveness of control allocation. The control allocation system can be effectively used as part of a FTC system or as a tool that can be used to investigate control allocation and aircraft redundancy. / AFRIKAANSE OPSOMMING: Die ontwikkeling van ’n beheertoekenning sisteem vir gebruik as deel van ’n fout verdraagsame beheersisteem in onbemande lugvaartuie word voorgelê. Hierdie sisteem speel ’n essensiële rol in die vermindering van die moontlikheid dat ’n fout die herkonfigurasie van die beheer, bestuur of navigasiesisteme van die vaartuig tot gevolg sal hê, deur die verskil te verminder tussen die verlangde en bereikbare werkverrigtingsraamwerk van die vaartuig. Dit word bereik deur die optimisering van die toekenning van beheerpoging aangevoer deur die virtuele aktueerders na die fisiese aktueerders teenwoordig op die vaartuig. ’n Eenvoudige algemene ses grade van vryheid lugvaartuig model word voorgestel wat al die relevante terme bevat wat benodig word om die onewewigtigheid verstelling van die vaartuig se aktueerders te vind en die werksverrigting van die virtuele aktueerders te evalueer. Hierdie model is gebruik om ’n beheer toekenning formulering te ontwikkel wat die werkverrigting van die virtuele aktueerders van die vaartuig optimiseer terwyl nadelige gevolge verminder word asook aktueerder versadiging vermy word. Die gevolglike probleem formulering is omskryf as ’n multi-doel optimiserings probleem wat opgelos is deur gebruik van die sekwensiële kwadratiese programmerings metode. Die beheertoekenning sisteem is prakties geïmplementeer en getoets. ’n Aantal fout kategorieë van verskillende grade van erns is gedefinieer en twee vaartuie met verskillende vlakke van aktueerder oortolligheid is gebruik om die sisteem te toets. Die beheer toekenning algoritme is geëvalueer vir elke fout kategorie, vaartuig toetsgeval, asook vir ’n aantal verskillende beheertoekenning sisteem konfigurasies. ’n Aantal verbeterings is aangebring aan die beheertoekenning sisteem, naamlik die toevoeging van frekwensie gebaseerde toekenning en wysiging van die algoritme vir ’n onkonvensionele onbemande geleide waaier lugvaartuig. Die beheertoekenning sisteem is van toepassing op ’n aantal verskillende konvensionele vaartuig konfigurasies met geen verstellings asook van toepassing op onkonvensionele vaartuie met geringe verstellings. Die beheertoekenning sisteem kan beide enkel- en veelvoudige aktueerder tekortkominge hanteer en die belangrikheid van aktueerder oortolligheid is beklemtoon as ’n faktor wat die effektiwiteit van beheertoekenning beïnvloed. Die beheertoekenning sisteem kan effektief geïmplementeer word as deel van ’n fout verdraagsame beheersisteem of as ’n werktuig om beheertoekenning en vaartuig oortolligheid te ondersoek.

Yes / Unmanned Aerial Vehicles (UAVs) are considered, nowadays, as a futuristic and robust paradigm for 5G wireless networks, in terms of providing Internet connectivity services onto infrastructure cellular networks. In this paper, the interference regime caused by multiple downlink aerial wireless transmission beams has been highlighted. This has been introduced by estimating the UAVs coverage area that is analytically derived in a tractable closed-form expression. The rationale of the analysed coverage approach relies on observing and adapting the joint aerial distance between the aerial base stations. This can minimize the intra-overlapped coverage and ultimately maximize the overall coverage performance for a better quality of service demands. The novelty of our approach brings useful design insights for UAVs system-level performance that technically helps in aerial coverage computations without the need of performing an aerial deployment setup. To the end, the performance effectiveness of our methodology has been tested under an urban propagation environment conditions, in which the original probabilistic channel model approximation has been taken into account. Moreover, this paper identifies the interference issue of such an aerial network as a shrinkage or distortion phenomenon.

The highly optimized performance of nature’s creations and biological assemblies has inspired the development of their engineered counter parts that can potentially outperform conventional systems. In particular, bat wings are populated with air flow hair receptors which feedback the information about airflow over their surfaces for enhanced stability and maneuverability during their flight. The hairs in the bat wing membrane play a role in the maneuverability tasks, especially during low-speed flight. The developments of artificial hair sensors (AHS) are inspired by biological hair cells in aerodynamic feedback control designs. Current mathematical models for hair receptors are limited by strict simplifying assumptions of creeping flow hair Reynolds number on AHS fluid-structure interaction (FSI), which may be violated for hair structures integrated on small-scaled Unmanned Aerial Vehicles (UAVs). This study motivates by an outstanding need to understand the dynamic response of hair receptors in flow regimes relevant to bat-scaled UAVs. The dynamic response of the hair receptor within the creeping flow environment is investigated at distinct freestream velocities to extend the applicability of AHS to a wider range of low Reynolds number platforms. Therefore, a threedimensional FSI model coupled with a finite element model using the computational fluid dynamics (CFD) is developed for a hair-structure and multiple hair-structures in the airflow. The Navier-Stokes equations including continuity equation are solved numerically for the CFD model. The grid independence of the FSI solution is studied from the simulations of the hairstructure mesh and flow mesh around the hair sensor. To describe the dynamic response of the hair receptors, the natural frequencies and mode shapes of the hair receptors, computed from the finite element model, are compared with the excitation frequencies in vacuum. This model is described with both the boundary layer effects and effects of inertial forces due to fluid-structure xiv interaction of the hair receptors. For supporting the FSI model, the dynamic response of the hair receptor is also validated considering the Euler-Bernoulli beam theory including the steady and unsteady airflow.

<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>

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.