Sistemas de Transportes Inteligentes (STIs) englobam um conjuntos de tecnologias (Sensoriamento Remoto, Tecnologia da Informação, Eletrônica, Sistemas de Comunicação de Dados entre outros) que visam oferecer serviços e gerenciamento de tráfego avançado para meios de transporte rodoviário, aéreo e outros. A obtenção de informações a respeito das características e das condições do pavimento das estradas constitui uma parte importante dentro do sensoriamento nesses STIs. Investigar novas técnicas, metodologias e meios de automatizar a obtenção dessas informações é parte deste trabalho. Uma vez que existem diferentes tipos de defeitos em vias pavimentadas, esta tese apresenta a proposta de uma metodologia que permite a obtenção, de forma automática, das condições dos pavimentos asfálticos. A obtenção dos dados foi realizada por meio do Sensoriamento Remoto com uso de Veículos Aéreos Não Tripulados. A utilização de técnicas de Aprendizado de Máquina na detecção automática possibilitou alcançar uma acurácia de 99% na detecção de pavimentos asfálticos flexíveis e 92% na identificação de defeitos em alguns experimentos. Como resultado obteve-se o diagnóstico automático, não só das condições da via, mas de diferentes tipos de defeitos presentes em pavimentos. / Intelligent Transport Systems (ITS) is a set of integrated technologies (Remote Sensing, Information Technology, Electronics, Data Communication Systems among others) that aims to provide services and advanced traffic management for road, air, rail and others transportation systems. Obtaining information about characteristics and road pavement conditions is an important part within the sensing these ITS. Investigating new techniques, methods and means to optimize and automate obtaining these information are part of this work, since there are different types of defects on paved roads. Thus, this thesis proposes a methodology that allows automatically obtain information about the condition of the pavement. Data collection was performed with remote sensing technology using Unmanned Aerial Vehicles. Automatic detection was possible through the use of Machine Learning techniques with 99% of accuracy in pavements and 92% in distress identification. As a result we obtained the self-diagnosis, not just the pavement, but different types of distress present in the pavement.

A aplicação de defensivos químicos em áreas agrícolas é de primordial importância para o rendimento de lavouras. O uso de aeronaves é cada vez mais comum em tal tarefa, principalmente pelo fato de aumentar a agilidade na operação. Entretanto, características climáticas, como intensidade e orientação do vento, podem causar prejuízo aos produtores (e.g. aplicação sobreposta dos defensivos e multas por aplicação de defensivos na borda exterior da lavoura). Essa lacuna motivou este trabalho de mestrado, que tem por objetivo avaliar um sistema integrado de veículos aéreos não tripulados (VANTs) e redes de sensores sem fio (RSSFs) para aumentar a eficiência da aplicação de defensivos químicos em campos agrícolas. Um VANT é utilizado para percorrer o campo enquanto aplica defensivos químicos. Uma RSSF com nós capazes de sensoriar a concentração de defensivos químicos é instalada no solo do campo. Os nós da RSSF e o VANT são equipados com módulos de rádio, possibilitando a troca de informação entre eles. Desse modo, o VANT consegue obter a distribuição de defensivos químicos aplicados no campo, podendo então tomar decisões para aumentar a eficiência da aplicação. Para análise desse sistema, dois experimentos foram realizados utilizando o simulador OMNeT++ e o framework MiXiM. Em um primeiro experimento é comparada a eficiência do sistema proposto com o sistema tradicional de pulverização agrícola, mostrando que é possível aumentar, em média, 16% a quantidade de defensivos químicos aplicados dentro do campo agrícola. E, em alguns casos, é possível obter um aumento de até 118,25%. No segundo experimento são analisados cinco diferentes protocolos de comunicação, mostrando que é possível otimizar o uso da bateria nos nós sensores sem fio em até 10 vezes, sem diminuir a eficiência do sistema / The use of chemical defensives in crop fields is important for the agricultural production. Often airplanes are used for spraying these products, because it adds agility in such task. But climatic characteristics, such as wind speed, may cause loss to farmers (e.g. superimposed application and fines for environment contamination). This text details an integrated system of unmanned aerial vehicle (UAV) and wireless sensor network (WSN) with the purpose of letting the aerial spraying process more precise. An UAV is used to spray chemicals in the crop field. A WSN composed of sensor nodes capable of sensoring quantities of chemical defensives is installed in the crop. Both UAV and sensor nodes are equipped with radio modules, this way they can communicate each other. Thus, the UAV may obtain the information of where the chemicals are being applied, then the UAV may take some action to performs a better spraying. This system were developed in the simulator OMNET++ using the framework MiXiM and two experiments were executed. In the first experiment the objective is to compare the efficiency of this system with the tradicional system, without the WSN. Results shown that the proposed system can improve the spraying process in 16% on average, and 118.25% on some cases. In the second experiment, five different WSN protocols were evaluated in means of battery usage of the sensor nodes. Results shown that its possible to reduce the battery usage in 10 times without intervene in the system efficiency

Esta dissertação visa a modelagem, simulação e controle de um veículo aéreo não tripulado (VANT) do tipo quadricóptero, utilizando-se as técnicas de controle ótimo e controle robusto no espaço de estados. O quadricóptero deve realizar as funções de decolagem, voo em cruzeiro e pouso de maneira autônoma. A dissertação inclui a síntese e análise comparativa entre um observador de estados de ordem plena de Luenberger e um filtro de Kalman. Além disso, um controlador linear quadrático gaussiano e um controlador robusto serão sintetizados e avaliados, procurando-se avaliar qual tem o melhor desempenho nas diversas tarefas do VANT. / This dissertation includes the modeling, simulation and control of a quadrotor unmanned aerial vehicle by using optimum control and robust control techniques on the space state. The quadrotor must perform the takeoff, cruise flight and landing in an autonomous way. This report also presents the synthesis and comparative analysis between a Luenberger full order state observer and a Kalman filter. A linear quadratic gaussian controller and a robust controller will be also synthetized and analyzed, to compare which one exhibits the best performance on the UAV tasks.

Esta dissertação visa a modelagem, simulação e controle de um veículo aéreo não tripulado (VANT) do tipo quadricóptero, utilizando-se as técnicas de controle ótimo e controle robusto no espaço de estados. O quadricóptero deve realizar as funções de decolagem, voo em cruzeiro e pouso de maneira autônoma. A dissertação inclui a síntese e análise comparativa entre um observador de estados de ordem plena de Luenberger e um filtro de Kalman. Além disso, um controlador linear quadrático gaussiano e um controlador robusto serão sintetizados e avaliados, procurando-se avaliar qual tem o melhor desempenho nas diversas tarefas do VANT. / This dissertation includes the modeling, simulation and control of a quadrotor unmanned aerial vehicle by using optimum control and robust control techniques on the space state. The quadrotor must perform the takeoff, cruise flight and landing in an autonomous way. This report also presents the synthesis and comparative analysis between a Luenberger full order state observer and a Kalman filter. A linear quadratic gaussian controller and a robust controller will be also synthetized and analyzed, to compare which one exhibits the best performance on the UAV tasks.

Fixed-wing unmanned aerial vehicles (UAVs) with the ability to hover combine the speed and endurance of traditional fixed-wing fight with the stable hovering and vertical takeoff and landing (VTOL) capabilities of helicopters and quadrotors. This combination of abilities can provide strategic advantages for UAV operators, especially when operating in urban environments where the airspace may be crowded with obstacles. Traditionally, fixed-wing UAVs with hovering capabilities had to be custom designed for specific payloads and missions, often requiring custom autopilots and unconventional airframe configurations. With recent government spending cuts, UAV operators like the military and law enforcement agencies have been urging UAV developers to make their aircraft cheaper, more versatile, and easier to repair. This thesis discusses the use of the commercially available ArduPilot open source autopilot, to autonomously transition a fixed-wing UAV to and from hover flight. Software modifications were made to the ArduPilot firmware to add hover flight modes using both Proportional, Integral, Derivative (PID) Control and Model Reference Adaptive Control (MRAC) with the goal of making the controllers robust enough so that anyone in the ArduPilot community could use their own ArduPilot board and their own fixed-wing airframe (as long as it has enough power to maintain stable hover) to achieve autonomous hover after some simple gain tuning. Three new hover flight modes were developed and tested first in simulation and then in flight using an E-Flight Carbon Z Yak 54 RC aircraft model, which was equipped with an ArduPilot 2.5 autopilot board. Results from both the simulations and flight test experiments where the airplane transitions both to and from autonomous hover flight are presented.

The United States increasingly has resorted to the use of Unmanned Aerial Vehicles (UAVs) for targeted killings of terrorists as a counterterrorism strategy. More states and terrorist organizations also are acquiring UAVs and this development can lead to indiscriminate and unregulated use of UAVs. Previous researchers have indicated the surveillance ability and precise weapon delivery capacity of UAVs make them a weapon of choice for U.S. counterterrorism efforts. Although the U.S. government estimated the collateral damage involved in the use of UAVs at 3-5%, nongovernmental sources put it at 25-40%. A gap exists in the current literature regarding public perception of the use of UAVs as a counterterrorism measure and how international humanitarian law (IHL) may interpret employment of UAVs. The purpose of this quantitative, cross-sectional study is to determine if a relationship exists among public support of the use of UAVs for targeted killing, attitudes towards counterterrorism, and public perceptions of IHL. An online survey was used to collect data from 104 adult participants using the convenience sampling method. Logistic regression, ANOVA, and correlational analyses helped to determine the relationships. The outcomes contributed to the existing literature by providing important data related to public perception of the use of UAVs with the potential to enhance global peace and security. The results contributed to social change initiatives through the potential to facilitate the establishment of international and domestic legal frameworks to regulate the future employment of UAVs for targeted killing.

The thesis has been developed as part of the requirements for a PhD degree at the Artificial Intelligence and Integrated Computer System division (AIICS) in the Department of Computer and Information Sciences at Linköping University.The work focuses on issues related to Unmanned Aerial Vehicle (UAV) navigation, in particular in the areas of guidance and vision-based autonomous flight in situations of short and long term GPS outage.The thesis is divided into two parts. The first part presents a helicopter simulator and a path following control mode developed and implemented on an experimental helicopter platform. The second part presents an approach to the problem of vision-based state estimation for autonomous aerial platforms which makes use of geo-referenced images for localization purposes. The problem of vision-based landing is also addressed with emphasis on fusion between inertial sensors and video camera using an artificial landing pad as reference pattern. In the last chapter, a solution to a vision-based ground object geo-location problem using a fixed-wing micro aerial vehicle platform is presented.The helicopter guidance and vision-based navigation methods developed in the thesis have been implemented and tested in real flight-tests using a Yamaha Rmax helicopter. Extensive experimental flight-test results are presented. / WITAS

This master's thesis deals with the control design method called Non-linear Dynamic Inversion (NDI) and how it can be applied to Unmanned Aerial Vehicles (UAVs). In this thesis, simulations are conducted using a model for the unmanned aerial vehicle SHARC (Swedish Highly Advanced Research Configuration), which Saab AB is developing. The idea with NDI is to cancel the non-linear dynamics and then the system can be controlled as a linear system. This design method needs much information about the system, or the output will not be as desired. Since it is impossible to know the exact mathematical model of a system, some kind of robust control theory is needed. In this thesis integral action is used. A problem with NDI is that the mathematical model of a system is often very complex, which means that the controller also will be complex. Therefore, a controller that uses pure NDI is only discussed, and the simulations are instead based on approximations that use a cascaded NDI. Two such methods are investigated. One that uses much information from aerodata tables, and one that uses the derivatives of some measured outputs. Both methods generate satisfying results. The outputs from the second method are more oscillatory but the method is found to be more robust. If the signals are noisy, indications are that method one will be better.

One of the basic problems which have to be solved by Unmanned Automated Vehicles (UAV) involves the computation of a motion plan that would enable the system to reach a target given a set of initial conditions in presence of uncertainties on the vehicle dynamics and in the environment. Recent research efforts in this area have relied on deterministic models. To address the problem of inevitable uncertainties, a low-level control layer is typically used to ensure proper robust trajectory tracking. Such decision-tracking algorithms correct model disturbances a posteriori, while the whole movement planning is done in a purely deterministic fashion. We argue that the decision making process that takes place during movement planning, as performed by experienced human pilots, is not a purely deterministic operation, but is heavily influenced by the presence of uncertainties and reflects a risk-management policy. This research aims at addressing these uncertainties and developing an optimal control strategy that would account for the presence of system uncertainties. The underlying description of UAV trajectories will be based on a modeling language, the Maneuver Automaton, that takes into full account the vehicle dynamics, and hence guarantees flyable and trackable paths and results in a discretized solution space. Two optimal control problems, a nominal problem omitting uncertainties and a robust problem addressing the presence of uncertainties, will be defined and compared throughout this work. The incorporation of uncertainties, will ensure that the generated motion planning policies will maximize the probability to meet mission goals, weighing risks against performance.

Recently there has been a tremendous growth of research emphasizing control of unmanned aerial vehicles (UAVs) either in isolation or in teams. As a matter of fact, UAVs increasingly find their way to applications, especially in military and law enforcement (e.g., reconnaissance, remote delivery of urgent equipment/material, resource assessment, environmental monitoring, battlefield monitoring, ordnance delivery, etc.). This trend will continue in the future, as UAVs are poised to replace the human-in-the-loop during dangerous missions. Civilian applications of UAVs are also envisioned such as crop dusting, geological surveying, search and rescue operations, etc. In this thesis we propose a new online multiresolution path planning algorithm for a small UAV with limited on-board computational resources. The proposed approach assumes that the UAV has detailed information of the environment and the obstacles only in its vicinity. Information about far-away obstacles is also available, albeit less accurately. The proposed algorithm uses the fast lifting wavelet transform (FLWT) to get a multiresolution cell decomposition of the environment, whose dimension is commensurate to the on-board computational resources. A topological graph representation of the multiresolution cell decomposition is constructed efficiently, directly from the approximation and detail wavelet coefficients. Dynamic path planning is sequentially executed for an optimal path using the A* algorithm over the resulting graph. The proposed path planning algorithm is implemented on-line on a small autopilot. Comparisons with the standard D*-lite algorithm are also presented. We also investigate the problem of generating a smooth, planar reference path from a discrete optimal path. Upon the optimal path being represented as a sequence of cells in square geometry, we derive a smooth B-spline path that is constrained inside a channel that is induced by the geometry of the cells. To this end, a constrained optimization problem is formulated by setting up geometric linear constraints as well as boundary conditions. Subsequently, we construct B-spline path templates by solving a set of distinct optimization problems. For an application to the UAV motion planning, the path templates are incorporated to replace parts of the entire path by the smooth B-spline paths. Each path segment is stitched together while preserving continuity to obtain a final smooth reference path to be used for path following control. The path following control for a small fixed-wing UAV to track the prescribed smooth reference path is also addressed. Assuming the UAV is equipped with an autopilot for low level control, we adopt a kinematic error model with respect to the moving Serret-Frenet frame attached to a path for tracking controller design. A kinematic path following control law that commands heading rate is presented. Backstepping is applied to derive the roll angle command by taking into account the approximate closed-loop roll dynamics. A parameter adaptation technique is employed to account for the inaccurate time constant of the closed-loop roll dynamics during actual implementation. Finally, we implement the proposed hierarchical path control of a small UAV on the actual hardware platform, which is based on an 1/5 scale R/C model airframe (Decathlon) and the autopilot hardware and software. Based on the hardware-in-the-loop (HIL) simulation environment, the proposed hierarchical path control algorithm has been validated through the on-line, real-time implementation on a small micro-controller. By a seamless integration of the control algorithms for path planning, path smoothing, and path following, it has been demonstrated that the UAV equipped with a small autopilot having limited computational resources manages to accomplish the path control objective to reach the goal while avoiding obstacles with minimal human intervention.