[pt] O crescente uso de veículos aéreos não tripulados (VANTs) em diversos setores da sociedade é fruto de avanços da tecnologia. Por sua vez, a ampliação de aplicações de VANTs traz consigo a necessidade de aumento de robustez destes sistemas, especialmente em ambientes compartilhados com o ser humano. A presente dissertação aborda o desenvolvimento de uma plataforma para testes de veículos multicópteros, com o objetivo de contribuir para o processo de desenvolvimento e implementação de drones, permitindo sua movimentação em torno dos seus graus de liberdade de rotação e realizando medições de atitude e força geradas pelo sistema, sem colocar o veículo ou seu operador em risco. Todos os dados adquiridos pela plataforma são transmitidos para um computador, onde foi desenvolvida uma interface virtual para sua visualização em tempo real, além de permitir armazenamento para pós-processamento e análises futuras. Também apresenta-se e discute-se o desenvolvimento do simulador de trajetórias proposto, que mostra o deslocamento do veículo em função da sequência de comandos fornecida, com base nos dados adquiridos pela plataforma. No intuito de se propiciar um melhor entendimento do funcionamento do sistema aqui desenvolvido, é apresentado um estudo detalhado dos subsistemas que compõem um multicóptero, bem como do processo de modelagem dinâmica de um veículo quadricóptero, por meio da técnica de grafos de ligação. A modelagem do veículo é complementada com a identificação de parâmetros fundamentais para a implementação do modelo, sendo discutidos métodos para identificação de parâmetros inerciais do veículo e parâmetros dinâmicos do sistema motopropulsor. / [en] The growing use of unmanned aerial vehicles (UAVs) in various sectors of society is a result of advances in technology. In turn, the expansion of UAV applications brings with it the need to increase the robustness of these systems, especially in environments shared with humans. and comes together with the need for increased robustness due to its use in shared environments with humans. This dissertation approaches the development of a platform for testing multicopter vehicles aiming at assisting the process of developing and implementing drones, allowing movements around their rotational degrees of freedom and making measurements of attitude and forces generated by the system, without putting the vehicle or its operator at risk. All data acquired by the platform is transmitted to a computer, where a virtual interface was developed to provide real time visualization, in addition to allowing data storage for post-processing and future analysis. The development of the proposed trajectory simulator is also presented and discussed, that shows the displacement of the vehicle as a function of the sequence of commands provided, based on the acquired data. Aiming at allowing a better understanding of the functioning of the developed system, a detailed study of the subsystems that compose a multicopter is presented, as well as the process of dynamic modeling of a quadcopter vehicle, by using bond graph technique. The modeling of the vehicle is complemented with the identification of fundamental parameters for the model implementation, such as methods for the identification of inertial parameters of the vehicle and dynamics of the powertrain system.

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.

This dissertation progressively addresses research problems related to the trajectory optimization for fuel cell powered UAVs, from propulsion system model development, to optimal trajectory analyses and optimal trajectory applications. A dynamic model of a fuel cell powered UAV propulsion system is derived by combining a fuel cell system dynamic model, an electric motor dynamic model, and a propeller performance model. The influence of the fuel cell system dynamics on the optimal trajectories of a fuel cell powered UAV is investigated in two phases. In the first phase, the optimal trajectories of a fuel cell powered configuration and that of a conventional gas powered configuration are compared for point-to-point trajectory optimization problems with different performance index functions. In the second phase, the influence of the fuel cell system parameters on the optimal fuel consumption cost of the minimum fuel point-to-point optimal trajectories is investigated. This dissertation also presents two applications for the minimum fuel point-to-point optimal trajectories of a fuel cell powered UAV: three-dimensional minimum fuel route planning and path generation, and fuel cell system size optimization with respect to a UAV mission.

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.

As plantações de eucaliptos representam grande potencial econômico para a indústria de papel, celulose, entre outras, além de apresentar uma série de características positivas como alta produtividade, grande potencial de adaptação e ampla diversidade de espécies. Em consequência a tais vantagens, há décadas diversas pesquisas vem sendo realizadas com o intuito de monitorar e detectar diversas doenças que aferem este tipo de cultura. O monitoramento rápido das doenças em eucaliptos torna-se um requisito para evitar grandes perdas econômicas. Neste projeto de pesquisa utilizou-se imagens aéreas obtidas por VANTs (Veículos Aéreos Não-Tripulados) para detectar um tipo específico de estresse que afeta as plantações de eucaliptos: a Murcha de Ceratocyst is. Após rotular eucaliptos doentes e saudáveis e outras estruturas em imagens aéreas, técnicas de Aprendizado de Máquina Supervisionado foram desenvolvidas para generalizar o conhecimento e possibilitar uma rápida detecção através das imagens RGB e multiespectrais. Dentre as técnicas utilizadas, destacou-se a arquitetura de Redes Neurais Convolucional chamada de Custom- CNN, inspirada no modelo da tradicional arquitetura Lenet -5 agregando-se melhorias do estado-da-arte, como a camada convolucional 1x1. Na classificação do conjunto RGB, a Custom-CNN obteve o maior F-score, de 0,81, sendo que a técnica SVM-rbf obteve 0,67. No conjunto de dados com imagens multiespectrais, a Lenet -5 e a Custom-CNN at ingiram, respectivamente, 0,63 e 0,66 de F-score, enquanto o SVM-rbf obteve 0,46. Esta dissertação apresenta a metodologia utilizada para a classificação, elencando as principais características dos algoritmos utilizados, bem como os resultados experimentais obtidos. Há ainda uma aplicação do classificador Regressão Logística para o planejamento de trajetória com VANTs. / Eucalypt us plantations represent great economic potential for t he paper, pulp, among others, in addition to presenting a number of positive characteristics such as high productivity, great potential for adaptaion and wide diversity of species. In consequence of t hese advantages, there are several decades research has been conducted in order to monitor and detect various diseases that affect s this type of culture. The rapid monitoring of diseases in eucalyptus becomes a requirement to avoid major economic losses. In t his research project we used aerial images obtained by UAVs (Unmanned Aerial Vehicles) to detect an specific type of stress t hat a effect s eucalyptus plantations: the Ceratocyst is wilt . After labeling diseased eucalyptus, healthy eucalyptus and other structures in aerial images, Supervised Machine Learning techniques were developed to generalize knowledge and enable rapid detection through RGB and multispectral images. Among the techniques used, stood out t he Convolutional Neural Network architecture called Custom-CNN, that was inspired by the model of t raditional Lenet -5 architecture and with state-of-the-art improvements, such as t he 1x1 convolution layer. In t he classification of RGB dataset , the Custom-CNN obtained the highest F-score of 0.81, and SVM-RBF technique obtained 0.67. In t he dataset with multispectral images, Lenet -5 and Custom-CNN obtained, respectively, 0.63 and 0.66 of F-score, while SVM-rbf obtained 0.46. This paper presents the methodology used for classification, listing the main features of the algorithms and the experimental results. There is also an application of Logistic Regression classifier for path planning with UAVs.

L’objectif principal du travail de recherche présenté dans ce mémoire est l’amélioration de la sécurité et les performances du vol des mini drones soumis à des perturbations atmosphériques. Pour ce faire, un couplage entre un estimateur ensembliste à erreurs bornées et une stratégie de guidage pilotage est mise en œuvre. L’estimateur ensembliste a été utilisé pour restituer l’état du modèle dynamique du drone en présence de perturbations et de bruits de mesure supposés bornés. L’utilisation de ces techniques avait pour objet tout d’abord de détecter l’occurrence d’une perturbation atmosphérique par estimation de l’état du drone puis d’estimer l’amplitude et la direction du vent agissant sur le véhicule. Des expérimentations dans le générateur de rafale B20 à Lille ont été ainsi présentées afin de valider ces approches et d’évaluer leurs performances. La stratégie de guidage pilotage développée favorise le déplacement du véhicule dans une direction qui tient compte de l’évolution de la perturbation atmosphérique et du prochain point de passage désigné au véhicule. Cette loi de guidage est basée sue la loi de guidage par navigation proportionnelle et a été adaptée pour tenir compte des perturbations dans le déplacement du véhicule. Les résultats obtenus montrent qu’il est possible d’améliorer la sécurité du vol des mini-drones en présence de perturbations atmosphériques transversales, en modifiant en ligne la trajectoire. / The principal objective of this thesis is to enhance the safety of flight for small UAVs in presence of atmospheric perturbation. The approach suggested here consists in coupling a bounded–error estimation method with a new guidance strategy. The bounded error estimation has been used to estimate the states of the dynamical systems corrupted by perturbations and measurement noises, assumed to remain bounded. The method has been first used to detect the occurrence of a wind gust and afterwards to characterize the amplitude and direction of the wind acting on the vehicle Experiments in the B20 gust generator are also presented to validate these approaches and evaluate their performance. The developed guidance strategy provides the vehicle with a direction that takes into account the atmospheric perturbation and the next waypoint position. The guidance law is designed by using proportional navigation guidance that has been adapted to take the perturbations into account. The results presented in this thesis show that it is possible to improve the flight safety in a perturbed environement using the combination of the two methods.

In the last decade, the development and deployment of Uninhabited Airborne Vehicles (UAVs) has increased dramatically. This has in turn increased the desire to operate UAVs in civilian-airspace. Current UAV platforms can be integrated into civilian-airspace, with other air traffic, however they place a high burden on their human operators in order to do so. In order to meet the competing objectives of improved integration and low operator workload it will be necessary to increase the intelligence on-board the UAV. This thesis presents the results of the research which has been conducted into increasing the on-board intelligence of the UAV. The intent in increasing the on-board intelligence is to improve the ability of a UAV to integrate into civilian-airspace whilst also reducing the workload placed upon the UAV's operator. The research has focused upon increasing the intelligence in two key areas: mission planning; and mission piloting. Mission planning is the process of determining how to fly from one location to another, whilst avoiding entities (eg. airspace boundaries and terrain) on the way. Currently this task is typically performed by a trained human operator. This thesis presents a novel multidisciplinary approach for enabling a UAV to perform, on-board, its own mission planning. The novel approach draws upon techniques from the 3D graphics and robotics fields in order to enable the UAV to perform its own mission planning. This enables the UAV's operator to provide the UAV with the locations (waypoints) to fly to. The UAV will then determine for itself how to reach the locations safely. This relieves the UAV's operator of the burden of performing the mission planning for the UAV. As part of this novel approach to on-board mission planning, the UAV constructs and maintains an on-board situational awareness of the airspace environment. Through techniques drawn from the 3D graphics field the UAV becomes capable of constructing and interacting with a 3D digital representation of the civilian-airspace environment. This situational awareness is a fundamental component of enabling the UAV to perform its own mission planning and piloting. The mission piloting research has focused upon the areas of collision avoidance and communications. These are tasks which are often handled by a human operator. The research identified how these processes can be performed on-board the UAV through increasing the on-board intelligence. A unique approach to collision avoidance was developed, which was inspired by robotics techniques. This unique approach enables the UAV to avoid collisions in a manner which adheres to the applicable Civil Aviation Regulations, as defined by the Civil Aviation Safety Authority (CASA) of Australia. Furthermore, the collision avoidance algorithms prioritise avoiding collisions which would result in a loss of life or injury. Finally, the communications research developed a natural language-based interface to the UAV. Through this interface, the UAV can be issued commands and can also be provided with updated situational awareness information. The research focused upon addressing issues related to using natural language for a civilian-airspace-integrated UAV. This area has not previously been addressed. The research led to the definition of a vocabulary targeted towards a civilian-airspace-integrated UAV. This vocabulary caters for the needs of both Air Traffic Controllers and general UAV operators. This requires that the vocabulary cater for a diverse range of skill levels. The research established that a natural language-based communications system could be applied to a civilian-airspace-integrated UAV for both command and information updates. The end result of this research has been the development of the Intelligent Mission Planner and Pilot (IMPP). The IMPP represents the practical embodiment of the novel algorithms developed throughout the research. The IMPP was used to evaluate the performance of the algorithms which were developed. This testing process involved the execution of over 3000 hours of simulated flights. The testing demonstrated the high performance of the algorithms developed in this research. The research has led to the successful development of novel on-board situational awareness, mission planning, collision avoidance and communications capabilities. This thesis presents the development, implementation and testing of these capabilities. The algorithms which provide these capabilities go beyond the existing body of knowledge and provide a novel contribution to the established research. These capabilities enable the UAV to perform its own mission planning, avoid collisions and receive natural language-based communications. This provides the UAV with a direct increase in the intelligence on-board the UAV, which is the core objective of this research. This increased on-board intelligence improves the integration of the UAV into civilian-airspace whilst also reducing the operator's workload.

As plantações de eucaliptos representam grande potencial econômico para a indústria de papel, celulose, entre outras, além de apresentar uma série de características positivas como alta produtividade, grande potencial de adaptação e ampla diversidade de espécies. Em consequência a tais vantagens, há décadas diversas pesquisas vem sendo realizadas com o intuito de monitorar e detectar diversas doenças que aferem este tipo de cultura. O monitoramento rápido das doenças em eucaliptos torna-se um requisito para evitar grandes perdas econômicas. Neste projeto de pesquisa utilizou-se imagens aéreas obtidas por VANTs (Veículos Aéreos Não-Tripulados) para detectar um tipo específico de estresse que afeta as plantações de eucaliptos: a Murcha de Ceratocyst is. Após rotular eucaliptos doentes e saudáveis e outras estruturas em imagens aéreas, técnicas de Aprendizado de Máquina Supervisionado foram desenvolvidas para generalizar o conhecimento e possibilitar uma rápida detecção através das imagens RGB e multiespectrais. Dentre as técnicas utilizadas, destacou-se a arquitetura de Redes Neurais Convolucional chamada de Custom- CNN, inspirada no modelo da tradicional arquitetura Lenet -5 agregando-se melhorias do estado-da-arte, como a camada convolucional 1x1. Na classificação do conjunto RGB, a Custom-CNN obteve o maior F-score, de 0,81, sendo que a técnica SVM-rbf obteve 0,67. No conjunto de dados com imagens multiespectrais, a Lenet -5 e a Custom-CNN at ingiram, respectivamente, 0,63 e 0,66 de F-score, enquanto o SVM-rbf obteve 0,46. Esta dissertação apresenta a metodologia utilizada para a classificação, elencando as principais características dos algoritmos utilizados, bem como os resultados experimentais obtidos. Há ainda uma aplicação do classificador Regressão Logística para o planejamento de trajetória com VANTs. / Eucalypt us plantations represent great economic potential for t he paper, pulp, among others, in addition to presenting a number of positive characteristics such as high productivity, great potential for adaptaion and wide diversity of species. In consequence of t hese advantages, there are several decades research has been conducted in order to monitor and detect various diseases that affect s this type of culture. The rapid monitoring of diseases in eucalyptus becomes a requirement to avoid major economic losses. In t his research project we used aerial images obtained by UAVs (Unmanned Aerial Vehicles) to detect an specific type of stress t hat a effect s eucalyptus plantations: the Ceratocyst is wilt . After labeling diseased eucalyptus, healthy eucalyptus and other structures in aerial images, Supervised Machine Learning techniques were developed to generalize knowledge and enable rapid detection through RGB and multispectral images. Among the techniques used, stood out t he Convolutional Neural Network architecture called Custom-CNN, that was inspired by the model of t raditional Lenet -5 architecture and with state-of-the-art improvements, such as t he 1x1 convolution layer. In t he classification of RGB dataset , the Custom-CNN obtained the highest F-score of 0.81, and SVM-RBF technique obtained 0.67. In t he dataset with multispectral images, Lenet -5 and Custom-CNN obtained, respectively, 0.63 and 0.66 of F-score, while SVM-rbf obtained 0.46. This paper presents the methodology used for classification, listing the main features of the algorithms and the experimental results. There is also an application of Logistic Regression classifier for path planning with UAVs.

<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>

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