Existe um número considerável de áreas utilizadas como pontos de descarte de resíduos de construção civil na cidade de Palmas – TO que extrapolam o alcance das ferramentas de controle e fiscalização do município, principalmente em pontos dentro do perímetro urbano. A presente pesquisa tem como objetivo principal verificar a aplicabilidade da utilização de imagens obtidas pelo veículo aéreo não tripulado (VANT), no auxílio da identificação dos resíduos e de pontos de descarte de resíduos de construção civil (RCC) em áreas irregulares da cidade. De posse do instrumento, foram tomadas como unidade amostral áreas de descarte preliminarmente mapeadas por meio de imagens de satélite com resoluções espaciais menos refinadas. Com metodologia que permitisse atestar a potencialidade dos produtos gerados em termos de fotointerpretação, precisões geométricas e geográficas, foram feitos sobrevoos com diferentes sobreposições de imageamento e com a utilização de pontos de controle coletados por meio de GPS de precisão. Os resultados encontrados mostram que as imagens aéreas obtidas e os ortomosaicos gerados podem auxiliar de forma confiável, rápida, segura e com melhores informações na identificação de pontos de lançamento de RCC, atestando que as plataformas VANTs e os sensores passíveis de serem acoplados à elas podem servir adequadamente como tecnologia para sensoriamento remoto, como ênfase no estudo proposto. / There are a considerable number of areas used as waste disposal points for construction in the city of Palmas - TO, which extrapolate the reach of the control and inspection tools of the municipality, especially in points within the urban perimeter. The present research has as main objective to verify the applicability of the use of images obtained by the unmanned aerial vehicle (UAV), in the aid of the identification of the residues and points of disposal of construction waste (RCC) in irregular areas of the city. Once the instrument was in possession, preliminary sampling areas were mapped using satellite images with less refined spatial resolution. With a methodology that allowed to attest to the potential of the products generated in terms of photointerpretation, geometric and geographic precisions, overflight with different overlays of imaging and with the use of control points collected through precision GPS were made. The results obtained show that the aerial images obtained and the generated ortomosaic can help in a reliable, fast, safe and with better information in the identification of points of launch of RCC, attesting that the platforms VANTs and the sensors that can be coupled to them can Adequately serve as a technology for remote sensing, as an emphasis on the proposed study.

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This text presents the development of a four-degree-of-freedom (4DOF) quadcopter prototype that allows the vehicle to rotate around the three axes (yaw, pitch and roll) and linear movement along z-axis (altitude). The goal is to obtain a prototype bench that uses a good amount of components used in commercial quadcopters (sensors and actuators) and use it to apply attitude and altitude controllers, using techniques such as PID, LQR and Sliding-Mode. Starting from the system modeling, its specifications are shown followed by listing the components used, finishing with the development of the controllers and their simulations and applications. / Este texto apresenta o desenvolvimento de um protótipo de quadricóptero com quatro graus de liberdade (4DOF), o qual possibilita a rotação do veículo em torno dos três eixos (yaw, pitch e roll) e o deslocamento ao longo do eixo z (altitude). O objetivo é obter um protótipo de bancada que use a maior quantidade de componentes de um quadricóptero comercial (sensores e atuadores) e usá-lo para a aplicação de controladores de atitude e altitude, utilizando técnicas PID, LQR e Sliding-Mode. Partindo da modelagem do sistema, mostra-se as especificações do mesmo, os componentes utilizados e finaliza-se com o desenvolvimento dos controladores, simulação e aplicação deles.

Doctor of Philosophy / Genetics Interdepartmental Program / Jesse A. Poland / A central question in modern biology is to understand the genotype-to-phenotype (G2P) link, that is, how the genetics of an organism results in specific characteristics. However, prediction of phenotypes from genotypes is a difficult problem due to the complex nature of genomes, the environment, and their interactions. While the recent advancements in genome sequencing technologies have provided almost unlimited access to high-density genetic markers, large-scale rapid and accurate phenotyping of complex plant traits remains a major bottleneck. Here, we demonstrate field-based complex trait assessment approaches using a commercially available light-weight Unmanned Aerial Systems (UAS). By deploying novel data acquisition and processing pipelines, we quantified lodging, ground cover, and crop growth rate of 1745 advanced spring wheat lines at multiple time-points over the course of three field seasons at three field sites in South Asia. High correlations of digital measures to visual estimates and superior broad-sense heritability demonstrate these approaches are amenable for reproducible assessment of complex plant traits in large breeding nurseries. Using these validated high-throughput measurements, we applied genome-wide association and prediction models to assess the underlying genetic architecture and genetic control. Our results suggest a diffuse genetic architecture for lodging and ground cover in wheat, but heritable genetic variation for prediction and selection in breeding programs. The logistic regression-derived parameters of dynamic plant height exhibited strong physiological linkages with several developmental and agronomic traits, suggesting the potential targets of selection and the associated tradeoffs. Taken together, our highly reproducible approaches provide a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to understand the G2P and increase the rate of gain for complex traits in crop breeding.

Unmanned aerial systems (UASs) often require multiple human operators fulfilling diverse roles for safe and correct operation. Reliably designing the human interaction, autonomy, and decision making aspects of these systems requires the use of modeling. We propose a conceptual model that models human machine interaction systems as a group of actors connected by a network of communication channels. We present a simulation framework implemented in Java, with an optional XML model parser that can be analyzed using the Java Pathfinder (JPF) model checker. We propose two human workload metrics based on a taxonomy extracted from the relevant literature. Using the simulator to produce a workload profile over time for each human actor, we conducted a case study by modeling a UAS integrated into the National Airspace System. Additionally we adapted an existing cognitive workload metric to act as a baseline. The results of this case study were consistent with known workload events and the results of our baseline metric.

Precision agriculture requires detailed and timely information about field condition. In less than the short flight time a UAV (Unmanned Aerial Vehicle) can provide, an entire field can be scanned at the highest allowed altitude. The resulting NDVI (Normalized Difference Vegetation Index) imagery can then be used to classify each point in the field using a FIS (Fuzzy Inference System). This identifies areas that are expected to be similar, but only closer inspection can quantify and diagnose crop properties. In the remaining flight time, the goal is to scout a set of representative points maximizing the quality of actionable information about the field condition. This quality is defined by two new metrics: the average sampling probability (ASP) and the total scouting luminance (TSL). In simulations, the scouting flight plan created using a GA (Genetic Algorithm) significantly outperformed plans created by grid sampling or human experts, obtaining over 99% ASP while improving TSL by an average of 285%.

<p>A control system for autonomous landing of an unmanned aerial vehicle (UAV)with high precision has been developed. The UAV is a medium sized model he-licopter. Measurements from a GPS, a camera and a compass are fused with anextended Kalman filter for state estimation of the helicopter. Four PID-controllers,one for each control signal of the helicopter, are used for the helicopter control.During the final test flights fifteen landings were performed with an average land-ing accuracy of 35 cm.    A bias in the GPS measurements makes it impossible to land the helicopter withhigh precision using only the GPS. Therefore, a vision system using a camera anda pattern provided landing platform has been developed. The vision system givesaccurate measurement of the 6-DOF pose of the helicopter relative the platform.These measurements are used to guide the helicopter to the landing target. Inorder to use the vision system in real time, fast image processing algorithms havebeen developed. The vision system can easily match up the with the camera framerate of 30 Hz.</p> / <p>Ett kontrolsystem för att autonomt landa en modellhelikopter har utvecklats.Mätdata från en GPS, en kamera samt en kompass fusioneras med ett Extend-ed Kalman Filter för tillståndsestimering av helikoptern. Fyra PID-regulatorer,en för varje kontrolsignal på helikoptern, har används för regleringen. Under densista provflygningen gjordes tre landingar av vilken den minst lyckade slutade35 cm från målet.    På grund av en drift i GPS-mätningarna är det omöjligt att landa helikopternmed hög precision med bara en GPS. Därför har ett bildbehandlingssystem som an-vänder en kamera samt ett mönster på platformen utvecklats. Bidbehandlingssys-temet mäter positionen och orienteringen av helikoptern relativt platformen. Dessamätningar används kompensera för GPS-mätningarnas drift. Snabba bildbehan-dlingsalgoritmer har utvecklats för att kunna använda bildbehandlingssystemet irealtid. Systemet är mycket snabbare än 30 bilder per sekund vilket är kameranshastighet.</p>

A control system for autonomous landing of an unmanned aerial vehicle (UAV)with high precision has been developed. The UAV is a medium sized model he-licopter. Measurements from a GPS, a camera and a compass are fused with anextended Kalman filter for state estimation of the helicopter. Four PID-controllers,one for each control signal of the helicopter, are used for the helicopter control.During the final test flights fifteen landings were performed with an average land-ing accuracy of 35 cm.    A bias in the GPS measurements makes it impossible to land the helicopter withhigh precision using only the GPS. Therefore, a vision system using a camera anda pattern provided landing platform has been developed. The vision system givesaccurate measurement of the 6-DOF pose of the helicopter relative the platform.These measurements are used to guide the helicopter to the landing target. Inorder to use the vision system in real time, fast image processing algorithms havebeen developed. The vision system can easily match up the with the camera framerate of 30 Hz. / Ett kontrolsystem för att autonomt landa en modellhelikopter har utvecklats.Mätdata från en GPS, en kamera samt en kompass fusioneras med ett Extend-ed Kalman Filter för tillståndsestimering av helikoptern. Fyra PID-regulatorer,en för varje kontrolsignal på helikoptern, har används för regleringen. Under densista provflygningen gjordes tre landingar av vilken den minst lyckade slutade35 cm från målet.    På grund av en drift i GPS-mätningarna är det omöjligt att landa helikopternmed hög precision med bara en GPS. Därför har ett bildbehandlingssystem som an-vänder en kamera samt ett mönster på platformen utvecklats. Bidbehandlingssys-temet mäter positionen och orienteringen av helikoptern relativt platformen. Dessamätningar används kompensera för GPS-mätningarnas drift. Snabba bildbehan-dlingsalgoritmer har utvecklats för att kunna använda bildbehandlingssystemet irealtid. Systemet är mycket snabbare än 30 bilder per sekund vilket är kameranshastighet.

Inflatable-wing Unmanned Aerial Vehicles (UAVs) have the ability to be packed in a fraction of their deployed volume. This makes them ideal for many deployable UAV designs, but inflatable wings can be flexible and don’t have conventional control surfaces. This thesis will investigate the use of wing warping as a means of autonomous control for inflatable wings. Due to complexities associated with manufacturing inflatable structures a new method of rapid prototyping deformable wings is used in place of inflatables to decrease cost and design-cycle time. A UAV testbed was developed and integrated with the warping wings and flown in a series of flight tests. The warping wing flew both under manual control and autopilot stabilization.

The past few decades have witnessed a growing interest in the engineering communities to approach the handling of imperfect information from a quantitatively justifiable angle. In the aerospace engineering domain, the movement to develop creative avenues to nondeterministically solving engineering problems has emerged in the field of aerospace systems design. Inspired by statistical data modeling and numerical analysis techniques that used to be relatively foreign to the designers of aerospace systems, a variety of strategies leveraging upon the probabilistic treatment of uncertainty has been, and continue to be, reported. Although each method differs in the sequence in which probabilistic analysis and numerical optimization are performed, a common motif in all of them is the lack of any built-in provisions to compensate for infeasibilities that occur during optimization. Constraint violations are either strictly prohibited or striven to be held to an acceptable probability threshold, implying that most hitherto developed probabilistic design methods promote an avoid-failure approach to developing aerospace systems under uncertainty. It is the premise of this dissertation that such a dichotomous structure of addressing imperfections is hardly a realistic model of how product development unfolds in practice. From a time-phased view of engineering design, it is often observed that previously unknown parameters become known with the passing of each design milestone, and their effects on the system are realized. Should these impacts happen to be detrimental to critical system-level metrics, then a compensatory action is taken to remedy any unwanted deviations from the target or required bounds, rather than starting the process completely anew. Anecdotal accounts of numerous real-world design projects confirm that such remedial actions are commonly practiced means to ensure the successful fielding of aerospace systems. Therefore, formalizing the remedial aspect of engineering design into a new methodological capability would be the next logical step towards making uncertainty handling more pragmatic for this generation of engineers. In order to formulate a nondeterministic solution approach that capitalizes on the practice of compensatory design, this research introduces the notion of recourse. Within the context of engineering an aerospace system, recourse is defined as a set of corrective actions that can be implemented in stages later than the current design phase to keep critical system-level figures of merit within the desired target ranges, albeit at some penalty. The terminology is inspired by the concept of the same name in the field of statistical decision analysis, where it refers to an action taken by a decision maker to mitigate the unfavorable consequences caused by uncertainty realizations. Recourse programs also introduce the concept of stages to optimization formulations, and allow each stage to encompass as many sequences or events as determined necessary to solve the problem at hand. Together, these two major premises of classical stochastic programming provide a natural way to embody not only the remedial, but also the temporal and nondeterministic aspects of aerospace systems design. A two-part strategy, which partitions the design activities into stages, is proposed to model the bi-phasal nature of recourse. The first stage is defined as the time period in which an a priori design is identified before the exact values of the uncertain parameters are known. In contrast, the second stage is a period occurring some time after the first stage, when an a posteriori correction can be made to the first-stage design, should the realization of uncertainties impart infeasibilities. Penalizing costs are attached to the second-stage corrections to reflect the reality that getting it done right the first time is almost always less costly than fixing it after the fact. Consequently, the goal of the second stage becomes identifying an optimal solution with respect to the second-stage penalty, given the first-stage design, as well as a particular realization of the random parameters. This two-stage model is intended as an analogue of the traditional practice of monitoring and managing key Technical Performance Measures (TPMs) in aerospace systems development settings. Whenever an alarmingly significant discrepancy between the demonstrated and target TPM values is noted, it is generally the case that the most cost-effective recourse option is selected, given the available resources at the time, as well as scheduling and budget constraints. One obvious weakness of the two-stage strategy as presented above is its limited applicability as a forecasting tool. Not only cannot the second stage be invoked without a first-stage starting point, but also the second-stage solution differs from one specific outcome of uncertainties to another. On the contrary, what would be more valuable given the time-phased nature of engineering design is the capability to perform an anticipatory identification of an optimum that is also expected to incur the least costly recourse option in the future. It is argued that such a solution is in fact a more balanced alternative than robust, probabilistically maximized, or chance-constrained solutions, because it represents trading the design optimality in the present with the potential costs of future recourse. Therefore, it is further proposed that the original two-stage model be embedded inside a larger design loop, so that the realization of numerous recourse scenarios can be simulated for a given first-stage design. The repetitive procedure at the second stage is necessary for computing the expected cost of recourse, which is equivalent to its mathematical expectation as per the strong law of large numbers. The feedback loop then communicates this information to the aggregate-level optimizer, whose objective is to minimize the sum total of the first-stage metric and the expected cost of future corrective actions. The resulting stochastic solution is a design that is well-hedged against the uncertain consequences of later design phases, while at the same time being less conservative than a solution designed to more traditional deterministic standards. As a proof-of-concept demonstration, the recourse-based solution approach is presented as applied to a contemporary aerospace engineering problem of interest - the integration of fuel cell technology into uninhabited aerial systems. The creation of a simulation environment capable of designing three system alternatives based on Proton Exchange Membrane Fuel Cell (PEMFC) technology and another three systems leveraging upon Solid Oxide Fuel Cell (SOFC) technology is presented as the means to notionally emulate the development process of this revolutionary aeropropulsion method. Notable findings from the deterministic trade studies and algorithmic investigation include the incompatibility of the SOFC based architectures with the conceived maritime border patrol mission, as well as the thermodynamic scalability of the PEMFC based alternatives. It is the latter finding which justifies the usage of the more practical specific-parameter based approach in synthesizing the design results at the propulsion level into the overall aircraft sizing framework. The ensuing presentation on the stochastic portion of the implementation outlines how the selective applications of certain Design of Experiments, constrained optimization, Surrogate Modeling, and Monte Carlo sampling techniques enable the visualization of the objective function space. The particular formulations of the design stages, recourse, and uncertainties proposed in this research are shown to result in solutions that are well compromised between unfounded optimism and unwarranted conservatism. In all stochastic optimization cases, the Value of Stochastic Solution (VSS) proves to be an intuitively appealing measure of accounting for recourse-causing uncertainties in an aerospace systems design environment.