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Aerobiological sampling using unmanned aerial vehicles (UAVs) is an exciting research field blending various scientific and engineering disciplines. The biological data collected using UAVs helps to better understand the atmospheric transport of microorganisms. Autopilot-equipped UAVs can accurately sample along pre-defined flight plans and precisely regulated altitudes. They can provide even greater utility when they are networked together in coordinated sampling missions: such measurements can yield further information about the aerial transport process. In this work flight vehicle path planning, control and coordination strategies are considered for unmanned autonomous aerial vehicles. A time-optimal path planning algorithm, that is simple enough to be solved in real time, is derived based on geometric concepts. The method yields closed-form solution for an important subset of candidate extremal paths; the rest of the paths are found using a simple numerical root-finding algorithm. A multi-UAV coordination framework is applied to a specific control-volume sampling problem that supports aerobiological data-collection efforts conducted in the lower atmosphere. The work is part of a larger effort that focuses on the validation of atmospheric dispersion models developed to predict the spread of plant diseases in the lower atmosphere. The developed concepts and methods are demonstrated by field experiments focusing on the spread of the plant pathogen <i>Phytophthora infestans</i>. / Ph. D.

[pt] O presente projeto de pesquisa objetiva desenvolver um procedimento para determinação de trajetórias ótimas em pistas de corrida baseado em técnicas de otimização, considerando os limites geométricos da pista e as características dinâmicas do veículo. O veículo será representado por meio de um modelo simplificado de partícula orientada, mas que inclui as capacidades de tração, frenagem e aceleração normal típicas de um veículo terrestre de competição. Primeiramente, é determinada a trajetória de tempo mínimo para uma curva de 90 graus por meio da análise geométrica do problema e em seguida, é obtida a solução analítica geral para aplicação a qualquer ângulo. Em seguida, técnicas de otimização com restrição são empregadas de forma a obter a curva de menor tempo que concatena as trajetórias ótimas individuais de cada curva, previamente determinadas. São estudadas, ainda, as características dinâmicas de algumas curvas polinomiais para inferir aquela que melhor pode ser aplicada no processo de concatenação. A trajetória de menor tempo da pista de corrida obtida pelo procedimento de concatenação é apresentada e é feita uma análise das vantagens e desvantagens do método proposto. Como alternativa, é apresentada uma visão geral do problema de controle ótimo e é formulada a modelagem completa do problema de trajetória de mínimo tempo utilizando esta abordagem, incluindo as restrições dinâmicas do veículo e as restrições geométricas da pista. Algumas técnicas possíveis para solução do problema de controle ótimo são sugeridas. / [en] This work proposes a new procedure to determine the optimal trajectory on race tracks based on constrained optimization techniques, where the constraints are defined by means of the dynamic characteristics of the vehicle and the geometrical limits of the track. The vehicle is represented by an oriented particle with the capabilities of traction, braking and normal acceleration, which are typical in a competition vehicle. First, the minimum-time trajectory for a 90-degree curve is obtained through a geometrical analysis of the problem. The solution is then expanded to be applied to all angles. Starting from the individual minimum-time trajectory for each curve of the track, constrained optimization techniques are employed in order to obtain the shorter curve that concatenates these individual optimal trajectories. The dynamic characteristics of some polynomial curves are analyzed to infer the one that can best be applied in the concatenation process. The minimum-time trajectory for the race track obtained by the concatenation procedure is presented and the advantages and disadvantages of the proposed method are discussed. Alternatively, an overview of the optimal control problem is presented and a complete model of the minimum-time trajectory problem is developed using this approach, including the dynamic constraints of the vehicle and the geometric constraints of the track. Some possible methods for the solution of the optimal control problem are suggested.

The aim of this master's thesis is the implementation of optimal trajectory planning for an airplane flying in lower altitudes, which has to avoid collision with obstacles. For the planning we assume static and fully known environment. There are described principals, optimality and complexity for some chosen methods of planning in this thesis. And based on the methods' characteristics it's chosen the best method for implementation.

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Applications such as urban surveillance, search and rescue, agricultural applications, military applications, etc., require miniature air vehicles (MAVs) to fly for a long time. But they have restricted flight duration due to their dependence on battery life, which necessitates optimal path planning. The generated optimal path should obey the curvature limits prescribed by the minimum turn radius/ maximum turn rate of the MAV. Further, in a dynamically changing environment, the final configuration that the MAV has to achieve may change en route, which demands the path to be replanned by an airborne processor in real-time. As MAVs are small in size and light in weight, wind has a very significant effect on the flight of MAVs and the computation of the minimum-time path in the presence of wind plays an important role. The thesis develops feasible trajectory generation algorithms which are fast, efficient, optimal and implementable in an onboard computer for rectilinear and circular path convergence problems and waypoint following problems both in the absence and in the presence of wind. The first part of the thesis addresses the problem of computation of optimal trajectories when MAVs fly on a two-dimensional (2D) plane maintaining a constant altitude. The shortest path is computed for MAVs from a given initial position and orientation to a given final path with a specified direction as required for a given mission. Unlike the classical Dubins problem where the shortest path was computed between two given configurations (position and orientation), the final point in this case is not specified. However, the final path, which can either be a rectilinear path or a circular path, and the direction to which the MAV should converge, is specified. The time-optimal path of MAVs is developed in the presence of wind mainly using the geometric approach although a few important properties are also obtained using optimal control theory, specifically, Pontryagin’s minimum principle (which provides only the necessary condition for optimality) for control-constrained systems. The complete optima l solution to this problem in all its generality is a major contribution of this thesis as existing methods in the literature that address this problem are either not optimal or do not give a complete solution. Further, the time-optimal path for specified initial and final configurations is generated in reasonably short time without computing all the path lengths of possible candidate paths, which is the method that exists in the literature for similar problems. Simulation results illustrate path generation for various cases, including the presence of steady and time-varying wind. Another problem in MAV path planning in 2D addressed in this thesis computes an extremal path that transitions between two consecutive waypoint segments (obtained by joining two way points in sequence) in a time-optimal fashion. This designed trajectory, named as γ-trajectory, is also used to track the maximum portion of waypoint segments in minimum time and the shortest distance between this trajectory and the associated waypoint can be set to a desired value. Another optimal path, called the loop trajectory, that goes through the way points as well as through the entire waypoint segments, is also proposed. Subsequently, the thesis proposes algorithms to generate trajectories in the presence of steady wind and compares these with the optimal trajectory generated using nonlinear programming based multiple shooting method to show that the generated paths are optimal in most cases. In three-dimensional (3D) space, if the initial and final configurations – in terms of (X,Y,Z) position, heading angle and flight path angle- of the vehicle are specified then shortest path computation is an interesting problem in literature. The proposed method in this thesis is based on 3D geometry and, unlike the existing iterative methods which yield suboptimal paths and are computationally more intensive, this method generates the shortest path in much less time. Due to its simplicity and low computational requirements, this approach can be implemented on a MAV in real-time. But, If the path demands very high pitch angle (as in the case of steep climbs), the generated path may not be flyable for an aerial vehicle with limited range of flight path angles. In such cases numerical methods, such as multiple shooting, coupled with nonlinear programming, are used to obtain the optimal solution. The time-optimal 3D path is also developed in the presence of wind which has a magnitude comparable to the speed of MAVs. The simulation results show path generation for a few sample cases to show the efficacy of the proposed approach as compared to the available approach in the literature. Next, the path convergence problem is studied in 3D for MAVs. The shortest path is generated to converge to a rectilinear path and a circular path starting from a known initial position and orientation. The method is also extended to compute the time-optimal path in the presence of wind. In simulation, optimal paths are generated for a variety of cases to show the efficacy of the algorithm. The other problem discussed in this thesis considers curvature-constrained trajectory generation technique for following a series of way points in 3D space. Extending the idea used in 2D, a γ-trajectory in 3D is generated to track the maximum portion of waypoint segments with a desired shortest distance between the trajectory and the associated waypoint. Considering the flyability issue of the plane a loop-trajectory is generated which is flyable by a MAV with constrained flight path angle. Simulation results are given for illustrative purposes. The path generation algorithms are all based on a kinematic model, considering the vehicle as a point in space. Implementing these results in a real MAV will require the dynamics of the MAV to be considered. So, a 6-DOF SIMULINK model of a MAV is used to demonstrate the tracking of the computed paths both in 2D plane and in 3D space using autopilots consisting of proportional-integral-derivative (PID )controllers .Achieving terminal condition accurately in real-time, if there is noisy measurement of wind data, is also addressed.

Linear Temporal Logic (LTL), as one of the temporal logic, can generate a fully automated correct-by-design controller synthesis approach for single or multiple autonomous vehicles, under much more complex missions than the traditional point-to-point navigation.In this master thesis, a framework which combines model- checking-based robot motion planning with action planning is proposed based on LTL for-mulas. The specifications implicitly require both sequential regions for multi-agent to visit and the desired actions to perform at these regions while avoid-ing collision with each other and fixed obstacles. The high level motion and task planning and low level navigation function based collision avoidance controller are verified by nontrivial simulation and implementation on real quadcopter in Smart Mobility Lab.

[pt] Em veículos de competição com velocidades elevadas, o principal objetivo é chegar em primeiro lugar, o que significa percorrer um determinado número de voltas em uma trajetória fechada fazendo algumas manobras para cumprir o circuito no menor tempo possível, dentro das limitações impostas pelas caracteristicas dinâmicas e de condução destes veículos. A otimização é uma metodologia que pode ser usada para reproduzir trajetórias e técnicas de condução usadas pelos pilotos de corrida, e também para investigar os efeitos de vários parâmetros nas condições limites da estabilidade veicular. Neste trabalho, inicialmente é apresentado o desenvolvimento de um modelo dinâmico do veículo considerando as caracterítiscas suficientes para análise da trajetória, influenciada por parâmetros geométricos e físicos pertinentes. Em seguida é definido o problema de obtenção da trajetória empregando procedimentos de otimização, de modo a determinar como um veículo irá percorrer um traçado, considerando como função objetivo o tempo de percurso, que deverá ser mínimo, e tendo como restrições as condições dinâmicas do veículo e geométricas da pista, implementando rotinas que são usadas em conjunto com os algoritmos existentes na Optimization Toolbox do Matlab. Finalmente apresenta-se o comportamento do veículo, representado pelo modelo desenvolvido anteriormente em uma malha de controle de trajetória, de modo a comparar o comportamento assim obtido com aquele previsto pelo procedimento de otimização. / [en] High speed competition vehicles are required to cover a determined number of laps in a closed trajectory circuit in a time that is the least possible, in the limits of the governing dynamic and driving characteristics of these vehicles. Optimization is a methodology that can be used in order to simulate trajectories and driving techniques of used by the competition pilots and to investigate the effects of several parameters in limit conditions of car stability. In this work it is first presented the development of the vehicle model considering the sufficient characteristics for trajectory analysis, influenced by pertinent geometric and physical parameters. In continuation, the problem of the optimal trajectory is defined using optimization procedures, in order to determine how a vehicle will follow the path, considering as an objective function the time to follow it, that must be the minimum, and having as constraints the vehicle dynamic conditions and the path geometry, implementing routines that are used with the Matlab´s Optimization Toolbox. Finally the behavior of the vehicle is presented, represented by the model developed previously in a trajectory control loop, in such a way to compare the resulting behavior with the one predicted by the optimization procedure.

Exploration is a fundamental problem in robotics that requires robots to navigate through unknown environments to autonomously gather information about their surroundings while executing collision-free paths. In this project, we propose a method for producing smooth paths during the exploration process in indoor environments using UAVs to improve battery efficiency and enhance the quality of pose estimation. The developed framework is built by merging two approaches that represent the state of the art in the field of autonomous exploration with UAVs. The overall exploration logic is given by GLocal, a paper that introduces a hybrid, i.e. both sampling-based and frontier-based, framework that is able to cope with the issue of odometry drift when exploring indoor environments due to the absence of absolute localization, e.g. through GNSS. The second approach is FUEL, which introduces a frontier-based exploration methodology which computes the ’drones path as an optimized non-uniform B-Spline. The framework described in this thesis borrows the optimized B-Spline trajectory generation from FUEL and implements it in GLocal. To do this, the original cost function defined by GLocal for each exploration viewpoint was modified and the resulting samples were used to select the initial control points of the B-Spline. Furthermore, we extended the underlying state machine governing the entire algorithm and we revisited the original re-planning logic. The presented system is evaluated in various simulated environments, showcasing the advantages and disadvantages of this method. These evaluations demonstrate its improved state estimation performance and absolute observed volume, albeit at the expense of longer traveled trajectories in big and complex environments. / Utforskning är ett grundläggande problem inom robotteknik som kräver att robotar navigerar genom okända miljöer för att autonomt samla in information om sin omgivning samtidigt som de utför kollisionsfria banor. I det här projektet föreslår vi en metod för att producera jämna banor under utforskningsprocessen i inomhusmiljöer med hjälp av UAV:er för att förbättra batterieffektiviteten och förbättra kvaliteten på posestimeringen. Det utvecklade ramverket bygger på en sammanslagning av två metoder som representerar den senaste tekniken inom autonom utforskning med UAV:er. Den övergripande utforskningslogiken ges av GLocal, en artikel som introducerar en hybrid, i.e. både samplingsbaserad och gränsbaserad, ram som kan hantera problemet med odometridrift vid utforskning av inomhusmiljöer på grund av frånvaron av absolut lokalisering, e.g. genom GNSS. Den andra metoden är FUEL, som introducerar en gränsbaserad utforskningsmetod som beräknar drönarens bana som en optimerad icke-uniform B-Spline. Ramverket som beskrivs i denna avhandling lånar den optimerade B-Spline-banegenereringen från FUEL och implementerar den i GLocal. För att göra detta modifierades den ursprungliga kostnadsfunktionen som definierades av GLocal för varje utforskningspunkt och de resulterande samplen användes för att välja de initiala kontrollpunkterna för B-Spline. Dessutom utökade vi den underliggande tillståndsmaskinen som styr hela algoritmen och vi reviderade den ursprungliga logiken för omplanering. Det presenterade systemet utvärderas i olika simulerade miljöer, vilket visar fördelarna och nackdelarna med denna metod. Dessa utvärderingar visar på förbättrad prestanda för tillståndsuppskattning och absolut observerad volym, om än på bekostnad av längre färdvägar i stora och komplexa miljöer.

In this work we present algorithms that are capable of calculating paths to destination for electric vehicles. These paths can be based on the simple metrics such as the distance, time or the paths can be based on more advanced metric such as the minimum energy demanding metric. This metric is parameterizable by the physical construction of the electrical vehicle. We also propose a new algorithm that computes energy optimal paths that are more acceptable by the driver, because it also takes into consideration the time metric while computing the path.