Com o quadro de escassez hídrica apresentado nos últimos anos no estado de São Paulo, cada vez é mais evidente a necessidade do planejamento e gerenciamento dos recursos hídricos de forma racional, compartilhada e sustentável. Além do aumento da demanda por água devido ao contínuo crescimento populacional, somam-se, ao desafio da gestão integrada dos recursos hídricos, as alterações climáticas que vêm sendo observadas ao longo das últimas décadas. Diante a situação apresentada, essa pesquisa teve como objetivo avaliar, de forma quantitativa, as possíveis alterações no regime hidrológico no conjunto de reservatórios do Sistema Cantareira, em diferentes cenários de mudanças climáticas futuras. O simulador climático MAGICC/SCENGEN foi utilizado para estimar as alterações climáticas previstas para o ano de 2100 em dois diferentes cenários de emissões de gases de efeito estufa, A1FIMI e B2MES. As variações de temperatura e precipitação foram incorporadas às séries climatológicas observadas na região em estudo. Para simular o comportamento das bacias contribuintes ao Sistema Cantareira, foi selecionado o modelo hidrológico SMAP (Soil Moisture Accounting Procedure). As séries alteradas de temperatura e precipitação serviram como dados de entrada do modelo hidrológico calibrado. As séries de vazões originadas desse processo, referentes aos dois cenários futuros A1FIMI e B2MES, foram comparadas às séries de vazões simuladas pelo modelo hidrológico no período de 1990 a 2010. Os resultados indicam queda no regime de vazões para os dois cenários futuros analisados, com diminuição mais acentuada para o cenário A1FIMI, de alta emissão de gases de efeito estufa. A diminuição nas vazões mostra-se preocupante, principalmente por se tratar do principal sistema de abastecimento da Região Metropolitana de São Paulo, o que traz à tona a necessidade de repensar as medidas adotadas para a exploração dos recursos hídricos no Sistema Cantareira. / The recent water shortages in the state of São Paulo, highlight the increasingly need for rational and sustainable water resources management. The increase in water demand due to continuous populational growth associated with climate changes observed in the past decades challenge the integrated management of water resources. Given this context, this research aimed to evaluate possible changes in the hydrological cycle of the Cantareira System reservoirs associated to different future climate change scenarios. A climate scenario generator (MAGICC/SCENGEN) was used to estimate the climate for the year 2100 with two different greenhouse gas emissions scenarios, A1FIMI and B2MES. Variations in temperature and precipitation were incorporated into climatological series observed in the study area. The SMAP (Soil Moisture Accounting Procedure) hydrologic model was used to simulate the behavior of the Cantareira System catchment basins. The altered temperature and precipitation series were used as input data in the calibrated hydrologic model. The discharge series of the two future scenarios obtained were compared to discharges simulated in recent climate conditions (series from 1990 to 2010) with the same hydrologic model. The results show a drop in the discharge series for the two future scenarios analyzed, with more marked decrease for A1FIMI, a scenario with higher greenhouse gases emissions. The decrease in discharge rates is a concern, especially because the Cantareira System is the main water system to supply São Paulo´s metropolitan area. This highlights a need to rethink the water use and management in the Cantareira System.

Com o quadro de escassez hídrica apresentado nos últimos anos no estado de São Paulo, cada vez é mais evidente a necessidade do planejamento e gerenciamento dos recursos hídricos de forma racional, compartilhada e sustentável. Além do aumento da demanda por água devido ao contínuo crescimento populacional, somam-se, ao desafio da gestão integrada dos recursos hídricos, as alterações climáticas que vêm sendo observadas ao longo das últimas décadas. Diante a situação apresentada, essa pesquisa teve como objetivo avaliar, de forma quantitativa, as possíveis alterações no regime hidrológico no conjunto de reservatórios do Sistema Cantareira, em diferentes cenários de mudanças climáticas futuras. O simulador climático MAGICC/SCENGEN foi utilizado para estimar as alterações climáticas previstas para o ano de 2100 em dois diferentes cenários de emissões de gases de efeito estufa, A1FIMI e B2MES. As variações de temperatura e precipitação foram incorporadas às séries climatológicas observadas na região em estudo. Para simular o comportamento das bacias contribuintes ao Sistema Cantareira, foi selecionado o modelo hidrológico SMAP (Soil Moisture Accounting Procedure). As séries alteradas de temperatura e precipitação serviram como dados de entrada do modelo hidrológico calibrado. As séries de vazões originadas desse processo, referentes aos dois cenários futuros A1FIMI e B2MES, foram comparadas às séries de vazões simuladas pelo modelo hidrológico no período de 1990 a 2010. Os resultados indicam queda no regime de vazões para os dois cenários futuros analisados, com diminuição mais acentuada para o cenário A1FIMI, de alta emissão de gases de efeito estufa. A diminuição nas vazões mostra-se preocupante, principalmente por se tratar do principal sistema de abastecimento da Região Metropolitana de São Paulo, o que traz à tona a necessidade de repensar as medidas adotadas para a exploração dos recursos hídricos no Sistema Cantareira. / The recent water shortages in the state of São Paulo, highlight the increasingly need for rational and sustainable water resources management. The increase in water demand due to continuous populational growth associated with climate changes observed in the past decades challenge the integrated management of water resources. Given this context, this research aimed to evaluate possible changes in the hydrological cycle of the Cantareira System reservoirs associated to different future climate change scenarios. A climate scenario generator (MAGICC/SCENGEN) was used to estimate the climate for the year 2100 with two different greenhouse gas emissions scenarios, A1FIMI and B2MES. Variations in temperature and precipitation were incorporated into climatological series observed in the study area. The SMAP (Soil Moisture Accounting Procedure) hydrologic model was used to simulate the behavior of the Cantareira System catchment basins. The altered temperature and precipitation series were used as input data in the calibrated hydrologic model. The discharge series of the two future scenarios obtained were compared to discharges simulated in recent climate conditions (series from 1990 to 2010) with the same hydrologic model. The results show a drop in the discharge series for the two future scenarios analyzed, with more marked decrease for A1FIMI, a scenario with higher greenhouse gases emissions. The decrease in discharge rates is a concern, especially because the Cantareira System is the main water system to supply São Paulo´s metropolitan area. This highlights a need to rethink the water use and management in the Cantareira System.

This thesis presents the design of an autopilot capable of flying small unmanned aerial vehicles with wingspans less then 21 inches. The autopilot is extremely small and lightweight allowing it to fit in aircraft of this size. The autopilot features an advanced, highly autonomous flight control system with auto-launch and auto-landing algorithms. These features allow the autopilot to be operated by a wide spectrum of skilled and unskilled users. Innovative control techniques implemented in software, coupled with light weight, robust, and inexpensive hardware components were used in the design of the autopilot.

The quadrotor helicopter is a unique flying vehicle which uses the thrust from four motors to provide hover flight capability. The uncoupled nature of the longitudinal and lateral axes and its ability to support large payloads with respect to its size make it an attractive vehicle for autonomous vehicle research. In this thesis, the quadrotor is modeled based on first principles and a proportional-derivative control method is applied for attitude stabilization and position control. A unique means of using an optic flow sensor for velocity and position estimation in an indoor setting is presented with flight results. Reliable hover flight and hallway following capabilities are exhibited in GPS-denied indoor flight using only onboard sensors. Attitude angles can be reliably estimated in the short run by integrating the angular rates from MEMS gyros, but noise on the signal leads to drift which renders the measurement unsuitable to attitude estimation. Typical methods of providing vector attitude corrections such as accelerometers and magnetometers have inherent weaknesses on hovering vehicles. Thus, an additional vector measurement is necessary to correct attitude readings for long-term flights. Two methods of using image processing to determine vanishing points in a hallway are demonstrated. The more promising of the two uses a Hough transform to detect lines in the image and forms a histogram of the intersections to detect likely vanishing point candidates. Once the vanishing point is detected, it acts as a vector measurement to correct attitude estimates on the quadrotor vehicle. Results using onboard vision to estimate heading are demonstrated on a test stand. Together, these capabilities improve the utility of the quadrotor platform for flight without the need of any external sensing capability.

Vertical Take-Off and Land (VTOL) Unmanned Air Vehicles (UAVs) possess several desirable characteristics, such as being able to hover and take-off or land in confined areas. One type of VTOL airframe, the tailsitter, has all of these advantages, as well as being able to fly in the more energy-efficient level flight mode. The tailsitter can track trajectories that successfully transition between hover and level flight modes. Three methods for performing transitions are described: a simple controller, a feedback linearization controller, and an adaptive controller. An autopilot navigational state machine with appropriate transitioning between level and hover waypoints is also presented. The simple controller is useful for performing a immediate transition. It is very quick to react and maintains altitude during the maneuver, but tracking is not performed in the lateral direction. The feedback linearization controller and adaptive controller both perform equally well at tracking transition trajectories in lateral and longitudinal directions, but the adaptive controller requires knowledge of far fewer parameters.

Miniature air vehicles (MAVs) are particularly well suited for short-distance, over-the-horizon, low-altitude surveillance and reconnaissance tasks. New camera and battery technologies have greatly increased a MAVs potential for these tasks. This thesis focuses on aerial surveillance of borders and roads, where a strap-down camera is used in-the-loop to track a border or road pathway. It is assumed that quality tracking requires that the pathway always remain in the footprint of the camera. The objective of this thesis is to explore roll-angle and altitude-above-ground-level constraints imposed on a bank-to-turn MAV due to the requirement to keep the pathway in the footprint of a downward-looking strap-down camera. This thesis derives the required altitude to maintain the pathway in the footprint of the camera and associated bank-angle constraints. Constraints are derived for both roads whose geometry is unknown a priori and roads with known geometry obtained from digital elevation map (DEM) data. MAV geometry and camera localization are used to derive these constraints. The thesis also discusses simple computer vision techniques for pathway following and a corresponding guidance law. The pixels of the captured color video are statistically classified into road and non-road components. Standard computer vision functions are used to eliminate classification noise and obtain a road heading direction. The effectiveness of the result is explored using a high fidelity simulator. Flight test results on small UAVs demonstrate the practicality of the road-following method.

Utilizing the Euler-Rodrigues symmetric parameters (attitude quaternion) to describe vehicle orientation, we develop a multiplicative, nonlinear (extended) variation of the Kalman filter (MEKF) to fuse data from low-cost sensors. The sensor suite is comprised of gyroscopes, accelerometers, and a GPS receiver. In contrast to the common approach of using the complete vehicle attitude as the quantities to be estimated, our filter states consist of the three components of an attitude error vector. In parallel with the time update of the attitude error estimate, we utilize the gyroscope measurements for the time propagation of the attitude quaternion. The accelerometer and the GPS sensors are used independently for the measurement update portion of the Kalman filter. For both sensors, a vector arithmetic approach is used to determine the attitude error vector. Following each measurement update, a multiplicative reset operation moves the attitude error information from the filter state into the attitude estimate. This reset operation utilizes quaternion algebra to implicitly maintain the unity-norm constraint. We demonstrate the effectiveness of our attitude estimation algorithm through flight simulations and flight tests of aggressive maneuvers such as loops and small-radius circles. We implement an approach to aerobatic maneuvering for miniature air vehicles (MAVs) using time-parameterized attitude trajectory generation and an associated attitude tracking control law. We designed two methodologies, polynomial and trigonometric, for creating functions that specify pitch and roll angles as a function of time. For both approaches, the functions are constrained by the maneuver boundary conditions of aircraft position and velocity. We construct a trajectory tracking feedback control law to regulate aircraft orientation throughout the maneuvers. The trajectory generation algorithm was used to construct several maneuvers and trajectory tracking control law successfully executed the maneuvers in the flight simulator. In addition to the simulation results, MAV flight tests verified the performance of the maneuver generation and control. To achieve obstacle avoidance maneuvering, the time parameterized trajectories were converted to spatially parameterized paths, which allowed for inertial reference frame position error to be included in the control law feedback loop. We develop a novel method to achieve the spatial parameterization using a prediction and correction approach. Additionally, the first derivative of position of the desired path is modified using a corrective parameter scheme prior to being used in the control. Using the path position error and the corrected derivative, we utilize a unit-norm quaternion framework to implement a proportional-derivative (PD) control law. This control law was demonstrated in simulation and hardware on maneuvers designed specifically to avoid obstacles, namely the Immelmann and the Close-Q, as well as a basic loop.

The viability of small Unmanned Aerial Vehicles (UAVs) as a stable platform for specific application use has been significantly advanced in recent years. Initial focus of lightweight UAV development was to create a craft capable of stable and controllable flight. This is largely a solved problem. Currently, the field has progressed to the point that unmanned aircraft can be carried in a backpack, launched by hand, weigh only a few pounds and be capable of navigating through unrestricted airspace. The most basic use of a UAV is to visually observe the environment and use that information to influence decision making. Previous attempts at using visual information to control a small UAV used an off-board approach where the video stream from an onboard camera was transmitted down to a ground station for processing and decision making. These attempts achieved limited results as the two-way transmission time introduced unacceptable amounts of latency into time-sensitive control algorithms. Onboard image processing offers a low-latency solution that will avoid the negative effects of two-way communication to a ground station. The first part of this thesis will show that onboard visual processing is capable of meeting the real-time control demands of an autonomous vehicle, which will also include the evaluation of potential onboard computing platforms. FPGA-based image processing will be shown to be the ideal technology for lightweight unmanned aircraft. The second part of this thesis will focus on the exact onboard vision system implementation for two proof-of-concept applications. The first application describes the use of machine vision algorithms to locate and track a target landing site for a UAV. GPS guidance was insufficient for this task. A vision system was utilized to localize the target site during approach and provide course correction updates to the UAV. The second application describes a feature detection and tracking sub-system that can be used in higher level application algorithms.

Human-controlled robots are utilized in many situations and such use is becoming widespread. This thesis details research that allows a single human to interact with a team of robots performing tasks that require cooperation. The research provides insight into effective interaction design methods and appropriate interface techniques. The use of team-level autonomy is shown to decrease human workload while simultaneously improving individual robot efficiency and robot-team cooperation. An indoor human-robot interaction testbed was developed at the BYU MAGICC Lab to facilitate experimentation. The testbed consists of eight robots equipped with wireless modems, a field on which the robots move, an overhead camera and image processing software which tracks robot position and heading, a simulator which allows development and testing without hardware utilization and a graphical user interface which enables human control of either simulated or hardware robots. The image processing system was essential for effective robot hardware operation and is described in detail. The system produced accurate robot position and heading information 30 times per second for a maximum of 12 robots, was relatively insensitive to lighting conditions and was easily reconfigurable. The completed testbed was utilized to create a game for testing human-robot interaction schemes. The game required a human controlling three robots to find and tag three robot opponents in a maze. Finding an opponent could be accomplished by individual robots, but tagging an opponent required cooperation between at least two robots. The game was played by 11 subjects in five different autonomy modes ranging from limited robot autonomy to advanced individual autonomy with basic team-level autonomy. Participants were interrupted during the game by a secondary spatial reasoning task which prevented them from interacting with the robots for short periods of time. Robot performance during that interruption provided a measure of both individual and team neglect tolerance. Individual robot neglect tolerance and performance did not directly correspond to those quantities at the team level. The interaction mode with the highest levels of individual and team autonomy was most effective; it minimized game time and human workload and maximized team neglect tolerance.

Using autonomous miniature air vehicles (MAVs) is a cost-effective, simple method for collecting data about the size, shape, and location characteristics of a forest fire. However, noise in measurements used to compute pose (location and attitude) of the on-board camera leads to significant errors in the processing of collected video data. Typical methods using MAVs to track fires attempt to find single geolocation estimates and filter that estimate with subsequent observations. While this is an effective method of resolving the noise to achieve a better geolocation estimate, it reduces a fire to a single point or small set of points. A georeferenced mosaic is a more effective method for presenting information about a fire to fire fighters. It provides a means of presenting size, shape, and geolocation information simultaneously. We describe a novel technique to account for uncertainty in pose estimation of the camera by converting it to the image domain. We also introduce a new concept, a Georeferenced Uncertainty Mosaic (GUM), in which we utilize a Sequential Monte Carlo method (a particle filter) to resolve that uncertainty and construct a georeferenced mosaic that simultaneously shows size, shape, geolocation, and uncertainty information about the fire.