Autonomous aerial vehicles like missiles and unmanned aerial vehicles (UAVs) have attracted various military and civilian applications. The primary guidance objective of any autonomous vehicle is to reach the desired destination point (target or waypoint). However, many practical engagements impose additional constraints like minimum control effort, a desired final velocity direction or a predefined engagement time. This thesis addresses engagement time constrained guidance problems pertaining to missiles and UAVs. The first part of the thesis discusses a nonlinear guidance law for impact time control of missiles against stationary target. The guidance law is designed with a particular choice of missile heading error variation as a function of ran to-target. The proposed heading error variation leads to an exact closed-form expression for the impact time. controlling the impact time, a closed-form relation is derived relating the control parameter to the desired impact time. A new Lyapunov based guidance law with a monotonically decreasing lateral acceleration is proposed in the next part of the thesis. An exact expression for impact time with minimum and maximum achievable impact times is derived. A control parameter is proposed with a closed-form relationship to the desired impact time. Using the concept of predicted interception point, the two guidance laws are extended for impact time control against non-maneuvering and moving targets. The proposed guidance models are extended to three-dimensional engagements by deducing yaw and pitch lateral accelerations satisfying the desired heading error profile. Extensive simulation studies are carried out for single missile and salvo attack scenarios. The last part of the thesis presents a guidance methodology governing the arrival time of a UAV at a waypoint. A specific arrival angle is considered as an additional constraint. The arrival constraints are satisfied by varying the navigation gain of the proportional navigation guidance law. The methodology is applied for simultaneous and sequential arrival of UAVs at a waypoint.

Quad-rotor helicopters are agile aircraft which are lifted and propelled by four rotors. Unlike traditional helicopters, they do not require a tail-rotor to control yaw, but can use four smaller fixed-pitch rotors. However, without an intelligent control system it is very difficult for a human to successfully fly and manoeuvre such a vehicle. Thus, most of recent research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. This research demonstrates a consistent way of developing a robust adaptive controller for quad-rotor helicopters, using fuzzy-neural networks; creating an intelligent system that is able to monitor and control the non-linear multi-variable flying states of the quad-rotor, enabling it to adapt to the changing environmental situations and learn from past missions. Firstly, an analytical dynamic model of the quad-rotor helicopter was developed and simulated using Matlab/Simulink software, where the behaviour of the quad-rotor helicopter was assessed due to voltage excitation. Secondly, a 3-D model with the same parameter values as that of the analytical dynamic model was developed using Solidworks software. Computational Fluid Dynamics (CFD) was then used to simulate and analyse the effects of the external disturbance on the control and performance of the quad-rotor helicopter. Verification and validation of the two models were carried out by comparing the simulation results with real flight experiment results. The need for more reliable and accurate simulation data led to the development of a neural network error compensation system, which was embedded in the simulation system to correct the minor discrepancies found between the simulation and experiment results. Data obtained from the simulations were then used to train a fuzzy-neural system, made up of a hierarchy of controllers to control the attitude and position of the quad-rotor helicopter. The success of the project was measured against the quad-rotor’s ability to adapt to wind speeds of different magnitudes and directions by re-arranging the speeds of the rotors to compensate for any disturbance. From the simulation results, the fuzzy-neural controller is sufficient to achieve attitude and position control of the quad-rotor helicopter in different weather conditions, paving way for future real time applications.

Approved for public release; distribution in unlimited. / This study analyzes the effective use of multiple unmanned aerial vehicles (UAVs) for the Navy's Surface Search and Control mission. In the future, the Navy hopes to leverage the capabilities of a family of UAVs to provide increased situational awareness in the maritime environment. This family of UAVs includes a Broad Area Maritime Surveillance (BAMS) UAV and Vertical Take-Off UAVs (VTUAVs). The concepts of operations for how these UAVs work together have yet to be determined. Questions exist about the best number of UAVs, types of UAVs, and tactics that will provide increased capabilities. Through modeling and agent-based simulation, this study explores the validity of future UAV requirements and provides insights into the effectiveness of different UAV combinations. For the scenarios modeled, the best UAV combination is BAMS plus two or three VTUAVs. However, analysis shows that small numbers of VTUAVs can perform as well without BAMS as they do with BAMS. For combinations with multiple UAVs, BAMS proves to be a valuable asset that not only reduces the number of missed classifications, but greatly improves the amount of coverage on all contacts in the maritime environment. BAMS tactics have less effect than the mere presence of BAMS itself. / Lieutenant, United States Navy

Approved for public release, distribution is unlimited / This thesis is part of an ongoing research conducted at the Naval Postgraduate School to achieve the autonomous shipboard landing of Unmanned Aerial Vehicles (UAV). Two main problems are addressed in this thesis. The first is to establish communication between the UAV's ground station and the Autonomous Landing Flight Control Computer effectively. The second addresses the design and implementation of an autonomous landing controller using classical control techniques. Device drivers for the sensors and the communications protocol were developed in ANSI C. The overall system was implemented in a PC104 computer running a real-time operating system developed by The Mathworks, Inc. Computer and hardware in the loop (HIL) simulation, as well as ground test results show the feasibility of the algorithm proposed here. Flight tests are scheduled to be performed in the near future. / Lieutenant Junior Grade, Mexican Navy

Approved for public release; distribution is unlimited / An increasing number of unmanned vehicles (UV) are being incorporated into maritime operations as organic elements of Expeditionary and Carrier Strike Groups for development of the recognized maritime picture. This thesis develops an analytically-based planning aid for allocating UVs to missions. Inputs include the inventory of UVs, sensors, their performance parameters, and operational scenarios. Operations are broken into mission critical functions: detection, identification, and collection. The model output assigns aggregated packages of UVs and sensors to one of the three functions within named areas of interest. A spreadsheet model uses conservative time-speed-distance calculations, and simplified mathematical models from search theory and queuing theory, to calculate measures of performance for possible assignments of UVs to missions. The spreadsheet model generates a matrix as input to a linear integer program assignment model which finds the best assignment of UVs to missions based on the user inputs and simplified models. The results provide the mission planner with quantitatively-based recommendations for unmanned vehicle mission tasking in challenging scenarios. / Lieutenant, United States Navy

Approved for public release, distribution is unlimited / In urbanized terrain, radiowave propagation is subjected to fading on large-scales and smallscales that would impede on the quality and reliability of data link transmission. This would have implications in many military applications. One example is the performance of unmanned aerial vehicle (UAV) data and communications links in complex urban environments. The purpose of this research is to study the effectiveness of diversity techniques on the performance of urban UAV data and communications links. The techniques investigated were spatial, polarization, and angle diversities. The ray tracing software, Urbana Wireless Toolset, was used in the modeling and simulation process. The various combinations of diversity techniques were simulated using a realistic urban city model. For the few transmit-receive geometries examined, it was found that angle diversity with a directive antenna provided the greatest increase in signal strength relative to the no diversity case. / Civilian, Singapore DOD

Cette thèse se concentre sur les robots aériens autonomes qui interagissent avec l’environnement et en particulier sur la conception de nouvelles méthodes de commande et de planification de mouvement pour tels systèmes. De nos jours, les véhicules aériens autonomes sont de plus en plus utilisés dans des nombreux domaines d’application, mais ils viennent utilisés surtout comme des simples capteurs. Au vu de ça, les défis majeurs dans le domaine de l’interaction physique aérienne, est aujourd’hui d’aller au-delà de cette application limitée, et d’exploiter entièrement les capacités des robots aériens afin d’interagir avec l’environnement. Dans le but de réaliser cet objectif, cette thèse considère l’analyse d’une classe spécifique de systèmes aériens interagissant avec l’environnement : les véhicules aériens attachés avec des câbles ou des bars. Ce travail se concentre sur l’analyse formelle et minutieuse de véhicules aériens attachés, en allant du contrôle et l’évaluation d’état à la planification du mouvement. Nous avons examiné notamment la platitude différentielle du système, trouvant deux sorties plate possibles qui révèlent des nouvelles capacités de tel système pour l’interaction physiques. En plus, poussé par l’intérêt pour l’interaction physique aérienne d’A à Z, nous avons abordés des problèmes supplémentaires liés à la conception, au contrôle et à la planification du mouvement pour des manipulateurs aériens. / This thesis focuses on the study of autonomous aerial robots interacting with the surrounding environment, and in particular on the design of new control and motion planning methods for such systems. Nowadays, autonomous aerial vehicles are extensively employed in many fields of application but mostly as autonomously moving sensors. On the other hand, in the recent field of aerial physical interaction, the goal is to go beyond sensing-only applications and fully exploit the aerial robots capabilities in order to interact with the environment. With the aim of achieving this goal, this thesis considers the analysis of a particular class of aerial robots interacting with the environment: tethered aerial vehicles. This work focuses on the thorough formal analysis of tethered aerial vehicles ranging from control and state estimation to motion planning. In particular, the differential flatness property of the system is investigated, finding two possible flat outputs that reveal new capabilities of such system for the physical interaction. The theoretical results were finally employed to solve the challenging problem of landing and takeoff on/from a sloped surface. In addition, moved by the interest on aerial physical interaction from A to Z, we addressed supplementary problems related to the design, control and motion planning for aerial manipulators.

Os veículos aéreos não tripulados (VANTs) vêm apresentando destaque crescente no setor aeronáutico mundial, tanto no desenvolvimento dos mesmos quanto nas diferentes aplicações desses veículos, devido ao seu grande potencial de utilização. Em muitas aplicações, a utilização de múltiplos VANTs apresenta várias vantagens sobre um VANT sozinho. No entanto, o agrupamento pode experimentar problemas inerentes à comunicação sem fio, podendo originar novos problemas como inviabilizar a coordenação e a execução cooperativa de uma missão, por exemplo. Em um cenário não colaborativo, VANTs com algum nível de autonomia e que partilhem do mesmo espaço aéreo também deverão ser capazes de trocar informações entre si, principalmente informações sobre posicionamento e rota de voo, e também estarão suscetíveis aos distúrbios da comunicação sem fio. Para balancear as vantagens e as desvantagens, esses robôs precisarão se comunicar cientes das restrições e utilizando a rede de modo otimizado, por meio da aplicação de algoritmos que equilibrem adequadamente técnicas de difusão de informações e técnicas de mitigação de retransmissões. Neste projeto de doutorado foi feita a investigação sobre o método de disseminação de dados, por meio de broadcasting, em uma rede móvel ad hoc entre VANTs, denominada Flying Ad hoc Networks (FANETs). FANETs são um novo paradigma que pode superar as restrições de missões de um único VANT. As FANETs são compostas por vários VANTs que cooperam para realizar alguma missão crítica (por exemplo, uma missão de busca e resgate). Para manter a coordenação, todos os VANTs devem continuamente enviar ou retransmitir mensagens através do canal sem fio para garantir que todos os membros da rede saibam o estado da rede. Geralmente, a troca de dados necessários para manter a sincronização da missão exige o uso de broadcast para que todos os membros da rede possam recebê-los. No entanto, quando essa troca de mensagens é feita arbitrariamente, isso pode causar o problema da tempestade de broadcast (BSP), levando o meio sem fio a um estado inoperante. Apesar de alguns esforços relatados na literatura para o provimento de técnicas gerais de mitigação do problema BSP, o desafio de agregar novas informações ou conhecimento a receptores que estejam voando, ao invés de apenas espalhar os dados na rede, tem recebido menos atenção. Nesta tese, além de demonstrar que o problema BSP intensifica a contenção de rede à medida que o número de VANTs aumenta, também foi criado um método que se prova mais eficiente que os existentes até então. Tal técnica, denominada de Algoritmo Baseado em Vizinhança Dinâmica para o Problema da Tempestade de Broadcast (DNA-BSP), foi desenvolvida e validada com base em experimentos de mundo real e em simulações computacionais. Ele pode mitigar o problema BSP, que é um desafio real nas FANETs, reduzindo a redundância de mensagens em mais de 98% e tornando a entrega de mensagens 99,5% mais rápida do que no cenário de flooding, superando as técnicas gerais de mitigação do BSP quando aplicado em FANETs. Os resultados detalhados neste texto também poderão orientar trabalhos futuros ao fornecer informações úteis para o planejamento e otimização de redes ad hoc móveis para VANTs. / Unmanned aerial vehicles (UAVs) have been showing increasing notoriety in the global aviation scene, both on their development and on the different applications for these vehicles, due to their high potential of use. In many applications, using multiple UAVs has several advantages over a single UAV. However, a cluster of UAVs may experience issues inherent to wireless communication, which may lead to new complications such as making mission coordination and cooperative execution impractible. In a non-collaborative scenario, UAVs with some level of autonomy which share the same airspace should also be able to exchange information among themselves, especially positioning and flight path information, and may also be susceptible to wireless communication disturbances. These robots have to use the network fairly and should communicate under restrictions, appropriately adjusting techniques that disseminate information and that mitigate broadcasts, in order to balance the advantages and disadvantages of being a group. This Ph.D. research investigates how broadcasting is used to disseminate data throughout ad hoc mobile networks between UAVs called Flying Ad hoc Networks (FANETs). FANETs are a new paradigm that can overcome the mission constraints of single UAVs. FANETs are composed of several UAVs that cooperate to accomplish a critical mission (e.g., hazardous area monitoring). Aiming to maintain UAVs coordination, all aircraft must continuously retransmit or relay messages through the wireless channel to assure that every member knows the FANET status. However, when this message exchange is done blindly, it may cause the broadcast storm problem (BSP), leading the wireless medium to a dysfunctional state. Despite some efforts reported in the literature for providing general techniques to mitigate the broadcast storm problem, the challenge of aggregating new information or knowledge to receivers, instead of just spreading the information in the network, has received less attention. In this research, it has been proved that the broadcast storm problem causes network contention as the number of UAVs increases, and the innovative Dynamic Neighborhood-based Algorithm for the Broadcast Storm Problem (DNA-BSP) has been provided as a countermeasure, which was developed and validated based on computer simulations and outdoor experiments. It can mitigate the broadcast storm problem, which is a real challenge in FANETs, reducing message redundancy in more than 98%, and making message delivery 99,5% faster than in flooding scenario, outperforming classical broadcast storm mitigation techniques when applied in FANETs. Our detailed results can also guide future researches and provide useful insights for engineers planning and optimizing mission-critical mobile ad hoc network with support of UAVs.

Made available in DSpace on 2017-07-21T14:19:30Z (GMT). No. of bitstreams: 1 Victor Schnepper Lacerda.pdf: 2047594 bytes, checksum: f0234089904caa6e03e22d3efba8394c (MD5) Previous issue date: 2017-02-02 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Soybean cultivation is of great importance to the Brazilian economy, and one of the major obstacles to its high productivity is the Asian soybean rust, a disease caused by the fungus Phakopsora pachyrhizi. The main measure to control the damage caused by this disease is the application of fungicides at the appropriate time, but the biggest obstacle to its implementation is the difficult detection of Asian rust in its early stages. In this sense, remote sensing combined with the use of unmanned aerial vehicles (UAVs) has potential for disease detection, especially for providing information that is hard to assess by traditional means, and for the advantages of quality and cost of this technology. The present work explores the use of unmanned aerial vehicles to detect and predict the severity of Asian soybean rust by use of digital image processing and data mining techniques for retrieval of predictive models of severity in different development stages. The models obtained showed satisfactory potential for Asian rust detection, and a high correlation between disease severity and the visible spectrum (RGB camera), as it was possible to obtain correlation coefficients greater than 93% after the R5 development stage of the soybean crop. / O cultivo da soja (Glycine max) é importante para a economia brasileira, sendo que um dos principais obstáculos à alta produtividade na lavoura é a ferrugem asiática, causada pelo fungo Phakopsora pachyrhizi. O principal fator para o controle de danos causados por essa doença é a aplicação de fungicidas em momento apropriado, porém o maior obstáculo para uso dessa medida é a difícil detecção da ferrugem asiática em estágios iniciais. Nesse sentido, o sensoriamento remoto aliado ao uso de veículos aéreos remotamente pilotados apresenta potencial para detecção da doença, principalmente por fornecer informação de difícil acesso aos meios tradicionais e pelas vantagens de qualidade e custo dessa tecnologia. O presente trabalho explora o uso de veículos aéreos remotamente pilotados para detecção e predição de severidade da ferrugem asiática da soja, associados a técnicas de processamento digital de imagens e de mineração de dados, visando a obtenção de modelos preditivos de severidade nos diferentes estágios de desenvolvimento da soja. Os modelos obtidos demonstraram potencial para a detecção da ferrugem asiática, e uma boa correlação da severidade da doença com o espectro visível (câmera RGB), ao passo que foi possível obter coeficientes de correlação maiores que 93% utilizando o algoritmo SMOREG após o estádio R5 de desenvolvimento da cultura da soja.

The role of vision as an additional sensing mechanism has received a lot of attention in recent years in the context of autonomous flight applications. Modern Unmanned Aerial Vehicles (UAVs) are equipped with vision sensors because of their light-weight, low-cost characteristics and also their ability to provide a rich variety of information of the environment in which the UAVs are navigating in. The problem of vision based autonomous flight is very difficult and challenging since it requires bringing together concepts from image processing and computer vision, target tracking and state estimation, and flight guidance and control. This thesis focuses on the adaptive state estimation, guidance and control problems involved in vision-based formation flight. Specifically, the thesis presents a composite adaptation approach to the partial state estimation of a class of nonlinear systems with unmodeled dynamics. In this approach, a linear time-varying Kalman filter is the nominal state estimator which is augmented by the output of an adaptive neural network (NN) that is trained with two error signals. The benefit of the proposed approach is in its faster and more accurate adaptation to the modeling errors over a conventional approach. The thesis also presents two approaches to the design of adaptive guidance and control (G&C) laws for line-of-sight formation flight. In the first approach, the guidance and autopilot systems are designed separately and then combined together by assuming time-scale separation. The second approach is based on integrating the guidance and autopilot design process. The developed G&C laws using both approaches are adaptive to unmodeled leader aircraft acceleration and to own aircraft aerodynamic uncertainties. The thesis also presents theoretical justification based on Lyapunov-like stability analysis for integrating the adaptive state estimation and adaptive G&C designs. All the developed designs are validated in nonlinear, 6DOF fixed-wing aircraft simulations. Finally, the thesis presents a decentralized coordination strategy for vision-based multiple-aircraft formation control. In this approach, each aircraft in formation regulates range from up to two nearest neighboring aircraft while simultaneously tracking nominal desired trajectories common to all aircraft and avoiding static obstacles.