This thesis presents a greedy search algorithm for maneuver-based motion planning of agile vehicles. In maneuver-based motion planning, vehicle maneuvers are solved offline and saved in a library to be used during motion planning. From this library, a tree of possible vehicle states can be generated through the search space. A depth-first, library-based algorithm called AD-Lib is developed and used to quickly provide feasible trajectories along the tree. AD-Lib combines greedy search techniques with hill climbing and effective backtracking to guide the search process rapidly towards the goal. Using simulations of a four-thruster hovercraft, AD-Lib is compared to existing suboptimal search algorithms in both known and unknown environments with static obstacles. AD-Lib is shown to be faster than existing techniques, at the expense of increased path cost. The motion planning strategy of AD-Lib along with a switching controller is also tested in an environment with dynamic obstacles. / Master of Science

<p>U tezi je prikazan novi metod za sintezu i realizaciju dvonožnog<br />veštačkog hoda koji se zasniva na upotrebi jednostavnih pokreta čijim<br />je kombinovanjem moguće realizovati kompleksne pokrete kao što je<br />hod, a čiji se parametri mogu menjati tokom kretanja. Time je omogućeno<br />da se na osnovu informacija o nameravanom kretanju i stanja okoline<br />izvrši sinteza kretanja izborom i kombinacijom jednostavnih<br />bazičnih pokreta koje se nazivaju primitivi. Takođe je omogućeno da se,<br />tokom izvršavanja hoda bez njegovog prekida, menjaju parametri<br />kretanja kao što su brzina hoda, dužina koraka, pravac kretanja i<br />visina podizanja noge tokom prenosne faze. Potvrda je data kroz<br />eksperimentalne rezultate koji su sprovedeni simulacijom na<br />dinamičkom modelu humanoidnog robota.</p> / <p>This dissertation presents new method for the synthesis and realization of<br />biped artificial walk based on the use of simple movements that can be<br />combined in order to achieve complex movements such as walk, whereas it<br />is possible to change the motion parameters at any time. It means that,<br />based on the information about intended movement and current state of the<br />environment, it is possible to synthesize motion by selecting and tying simple<br />movements, i.e. motion primitives. It also enables the robot to change<br />walking parameters online such as walking speed, direction of walk, foot<br />length during swing phase and step length. Proof of this method is given by<br />experimental results obtained during the simulation on a dynamic model of<br />humanoid robot.</p>

Robotic motion planning aspires to match the ease and efficiency with which humans move through and interact with their environment. Yet state of the art robotic planners fall short of human abilities; they are slower in computation, and the results are often of lower quality. One stumbling block in traditional motion planning is that points and paths are often considered in isolation. Many planners fail to recognize that substantial shared information exists among path alternatives. Exploitation of the geometric and topological relationships among path alternatives can therefore lead to increased efficiency and competency. These benefits include: better-informed path sampling, dramatically faster collision checking, and a deeper understanding of the trade-offs in path selection. In path sampling, the principle of locality is introduced as a basis for constructing an adaptive, probabilistic, geometric model to influence the selection of paths for collision test. Recognizing that collision testing consumes a sizable majority of planning time and that only collision-free paths provide value in selecting a path to execute on the robot, this model provides a significant increase in efficiency by circumventing collision testing paths that can be predicted to collide with obstacles. In the area of collision testing, an equivalence relation termed local path equivalence, is employed to discover when the work of testing a path has been previously performed. The swept volumes of adjoining path alternatives frequently overlap, implying that a continuum of intermediate paths exists as well. By recognizing such neighboring paths with related shapes and outcomes, up to 90% of paths may be tested implicitly in experiments, bypassing the traditional, expensive collision test and delivering a net 300% boost in collision test performance. Local path equivalence may also be applied to the path selection problem in order to recognize higher-level navigation options and make smarter choices. This thesis presents theoretical and experimental results in each of these three areas, as well as inspiration on the connections to how humans reason about moving through spaces.

This dissertation addresses motion planning, modeling, and feedback control for autonomous vehicle systems. A hierarchical approach for motion planning and control of nonlinear systems operating in obstacle environments is presented. To reduce computation time during the motion planning process, dynamically feasible trajectories are generated in real-time through concatenation of pre-specified motion primitives. The motion planning task is posed as a search over a directed graph, and the applicability of informed graph search techniques is investigated. Specifically, a locally greedy algorithm with effective backtracking ability is developed and compared to weighted A* search. The greedy algorithm shows an advantage with respect to solution cost and computation time when larger motion primitive libraries that do not operate on a regular state lattice are utilized. Linearization of the nonlinear system equations about the motion primitive library results in a hybrid linear time-varying model, and an optimal control algorithm using the L2-induced norm as the performance measure is applied to ensure that the system tracks the desired trajectory. The ability of the resulting controller to closely track the trajectory obtained from the motion planner, despite various disturbances and uncertainties, is demonstrated through simulation. Additionally, an approach for obtaining dynamically feasible reference trajectories and feedback controllers for a small unmanned aerial vehicle (UAV) based on an aerodynamic model derived from flight tests is presented. The modeling approach utilizes the two step method (TSM) with stepwise multiple regression to determine relevant explanatory terms for the aerodynamic models. Dynamically feasible trajectories are then obtained through the solution of an optimal control problem using pseudospectral optimal control software. Discrete-time feedback controllers are then obtained to regulate the vehicle along the desired reference trajectory. Simulations in a realistic operational environment as well as flight testing with the feedback controller demonstrate the capabilities of the approach. The TSM is also applied for system identification of an aircraft using motion capture data. In this application, time domain system identification techniques are used to identify both linear and nonlinear aerodynamic models of large-amplitude pitching motions driven by control surface deflections. The resulting models are assessed based on both their predictive capabilities as well as simulation results. / Ph. D.

Research about unmanned ground vehicles (UGVs) has received an increased amount of attention in recent years, partly due to the many applications of UGVs in areas where it is inconvenient or impossible to have human operators, such as in mines or urban search and rescue. Two closely linked problems that arise when developing such vehicles are motion planning and control of the UGV. This thesis explores these subjects for a UGV with an unknown, and possibly time-variant, dynamical model. A framework is developed that includes three components: a machine learning algorithm to estimate the unknown dynamical model of the UGV, a motion planner that plans a feasible path for the vehicle and a controller making the UGV follow the planned path. The motion planner used in the framework is a lattice-based planner based on input sampling. It uses a dynamical model of the UGV together with motion primitives, defined as a sequence of states and control signals, which are concatenated online in order to plan a feasible path between states. Furthermore, the controller that makes the vehicle follow this path is a model predictive control (MPC) controller, capable of taking the time-varying dynamics of the UGV into account as well as imposing constraints on the states and control signals. Since the dynamical model is unknown, the machine learning algorithm Bayesian linear regression (BLR) is used to continuously estimate the model parameters online during a run. The parameter estimates are then used by the MPC controller and the motion planner in order to improve the performance of the UGV. The performance of the proposed motion planning and control framework is evaluated by conducting a series of experiments in a simulation study. Two different simulation environments, containing obstacles, are used in the framework to simulate the UGV, where the performance measures considered are the deviation from the planned path, the average velocity of the UGV and the time to plan the path. The simulations are either performed with a time-invariant model, or a model where the parameters change during the run. The results show that the performance is improved when combining the motion planner and the MPC controller with the estimated model parameters from the BLR algorithm. With an improved model, the vehicle is capable of maintaining a higher average velocity, meaning that the plan can be executed faster. Furthermore, it can also track the path more precisely compared to when using a less accurate model, which is crucial in an environment with many obstacles. Finally, the use of the BLR algorithm to continuously estimate the model parameters allows the vehicle to adapt to changes in its model. This makes it possible for the UGV to stay operational in cases of, e.g., actuator malfunctions.

A aplicação de técnicas baseadas em amostragem em algoritmos que envolvem o planejamento de trajetórias de robôs tem se tornado cada vez mais difundida. Deste grupo, um dos algoritmos mais utilizados é chamado Rapidly-exploring Random Tree (RRT), que se baseia na amostragem incremental para calcular de forma eficiente os planos de trajetória do robô evitando colisões com obstáculos. Vários esforços tem sido realizados a fim de reduzir o custo computacional do algoritmo RRT, visando aplicações que necessitem de respostas mais rápidas do algoritmo, como, por exemplo, em ambientes dinâmicos. Um dos dilemas relacionados ao RRT está na etapa de geração de primitivas de movimento. Se várias primitivas são geradas, permitindo o robô executar vários movimentos básicos diferentes, um grande custo computacional é gasto. Por outro lado, quando poucas primitivas são geradas e, consequentemente, poucos movimentos básicos são permitidos, o robô pode não ser capaz de encontrar uma solução para o problema, mesmo que esta exista. Motivados por este problema, um método de geração de primitivas de movimento foi proposto. Tal método é comparado com os métodos tradicional e aleatório de geração de primitivas, considerando não apenas o custo computacional de cada um, mas também a qualidade da solução obtida. O método proposto é aplicado ao algoritmo RRT, que depois é aplicado em um caso de estudo em um ambiente dinâmico. No estudo de caso, o algoritmo RRT otimizado é avaliado em termos de seus custos computacionais durante planejamentos e replanejamento de trajetória. As simulações são realizadas em dois simuladores: um desenvolvido em linguagem Python e outro em Matlab. / The application of sample-based techniques in path-planning algorithms has become year-by-year more widespread. In this group, one of the most widely used algorithms is the Rapidly-exploring Random Tree (RRT), which is based on an incremental sampling of configurations to efficiently compute the robot\'s path while avoiding obstacles. Many efforts have been made to reduce RRT computational costs, targeting, in particular, applications in which quick responses are required, e.g., in dynamic environments. One of the dilemmas posed by the RRT arises from its motion primitives generation. If many primitives are generated to enable the robot to perform a broad range of basic movements, a signicant computational cost is required. On the other hand, when only a few primitives are generated, thus, enabling a limited number of basic movements, the robot may be unable to find a solution to the problem, even if one exists. To address this quandary, an optimized method for primitive generation is proposed. This method is compared with the traditional and random primitive generation methods, considering not only computational cost, but also the quality of local and global solutions that may be attained. The optimized method is applied to the RRT algorithm, which is then used in a case study in dynamic environments. In the study, the modied RRT is evaluated in terms of the computational costs of its planning and replanning. The simulations were developed to access the effectiveness and efficiency of the proposed algorithm.

A aplicação de técnicas baseadas em amostragem em algoritmos que envolvem o planejamento de trajetórias de robôs tem se tornado cada vez mais difundida. Deste grupo, um dos algoritmos mais utilizados é chamado Rapidly-exploring Random Tree (RRT), que se baseia na amostragem incremental para calcular de forma eficiente os planos de trajetória do robô evitando colisões com obstáculos. Vários esforços tem sido realizados a fim de reduzir o custo computacional do algoritmo RRT, visando aplicações que necessitem de respostas mais rápidas do algoritmo, como, por exemplo, em ambientes dinâmicos. Um dos dilemas relacionados ao RRT está na etapa de geração de primitivas de movimento. Se várias primitivas são geradas, permitindo o robô executar vários movimentos básicos diferentes, um grande custo computacional é gasto. Por outro lado, quando poucas primitivas são geradas e, consequentemente, poucos movimentos básicos são permitidos, o robô pode não ser capaz de encontrar uma solução para o problema, mesmo que esta exista. Motivados por este problema, um método de geração de primitivas de movimento foi proposto. Tal método é comparado com os métodos tradicional e aleatório de geração de primitivas, considerando não apenas o custo computacional de cada um, mas também a qualidade da solução obtida. O método proposto é aplicado ao algoritmo RRT, que depois é aplicado em um caso de estudo em um ambiente dinâmico. No estudo de caso, o algoritmo RRT otimizado é avaliado em termos de seus custos computacionais durante planejamentos e replanejamento de trajetória. As simulações são realizadas em dois simuladores: um desenvolvido em linguagem Python e outro em Matlab. / The application of sample-based techniques in path-planning algorithms has become year-by-year more widespread. In this group, one of the most widely used algorithms is the Rapidly-exploring Random Tree (RRT), which is based on an incremental sampling of configurations to efficiently compute the robot\'s path while avoiding obstacles. Many efforts have been made to reduce RRT computational costs, targeting, in particular, applications in which quick responses are required, e.g., in dynamic environments. One of the dilemmas posed by the RRT arises from its motion primitives generation. If many primitives are generated to enable the robot to perform a broad range of basic movements, a signicant computational cost is required. On the other hand, when only a few primitives are generated, thus, enabling a limited number of basic movements, the robot may be unable to find a solution to the problem, even if one exists. To address this quandary, an optimized method for primitive generation is proposed. This method is compared with the traditional and random primitive generation methods, considering not only computational cost, but also the quality of local and global solutions that may be attained. The optimized method is applied to the RRT algorithm, which is then used in a case study in dynamic environments. In the study, the modied RRT is evaluated in terms of the computational costs of its planning and replanning. The simulations were developed to access the effectiveness and efficiency of the proposed algorithm.

Dans un contexte industriel aéronautique où les problématiques de sécurité constituent un facteur différentiateur clé, l’objectif de cette thèse est de répondre à la problématique ambitieuse de la réduction des accidents de type opérationnel. Les travaux de recherche s’inscrivent dans le domaine des systèmes d’alarmes pour l’évitement de collision qui ne font pas une analyse approfondie des solutions d’évitement par rapport à la situation de danger. En effet, les situations d’urgence en vol ne bénéficient pas à ce jour d’une représentation et d’un guide des solutions associées formels. Bien que certains systèmes d’assistance existent et qu’une partie de la connaissance associée aux situations d’urgence ait pu être identifiée, la génération dynamique d’une séquence de manœuvres sous fortes contraintes de temps et dans un environnement non connu à l’avance représente une voie d’exploration nouvelle. Afin de répondre à cette question et de rendre objective la notion de danger, les travaux de recherche présentés dans cette thèse mettent en confrontation la capacité d’évolution d’un aéronef dans son environnement immédiat avec une enveloppe physique devenant contraignante. Afin de mesurer ce danger, les travaux de recherche ont conduit à construire un module de trajectoires capable d’explorer l’espace en 3D. Cela a permis de tirer des enseignements en terme de flexibilité des manœuvres d’évitement possibles à l’approche du sol. De plus l’elicitation des connaissances des pilotes et des experts d’Airbus Helicopters (ancien Eurocopter) mis en situation d’urgence dans le cas d’accidents reconstitués en simulation a conduit à un ensemble de paramètres pour l’utilisation de la méthode multicritère PROMETHEE II dans le processus de prise de décision relatif au choix de la meilleure trajectoire d’évitement et par conséquent à la génération d’alarmes anti-collision. / In the aeronautics industrial context, the issues related to the safety constitute a highly differentiating factor. This PhD thesis addresses the challenge of operational type accident reduction. The research works are positioned and considered within the context of existing alerting equipments for collision avoidance, who don’t report a thorough analysis of the avoidance manoeuvres with respect to a possible threat. Indeed, in-flight emergency situations are various and do not all have a formal representation of escape procedures to fall back on. Much of operational accident scenarios are related to human mistakes. Even if systems providing assistance already exist, the dynamic generation of a sequence of manoeuvres under high constraints in an unknown environment remain a news research axis, and a key development perspective. In order to address this problematic and make the notion of danger objective, the research works presented in this thesis confront the capabilities of evolution of an aircraft in its immediate environment with possible physical constraints. For that purpose, the study has conducted to generate a module for trajectory generation in the 3D space frame, capable of partitioning and exploring the space ahead and around the aircraft. This has allowed to draw conclusions in terms of flexibility of escape manoeuvres on approach to the terrain. Besides, the elicitation of the Airbus Helicopters (former Eurocopter) experts knowledge put in emergency situations, for reconstituted accident scenarios in simulation, have permitted to derive a certain number of criteria and rules for parametrising the multicriteria method PROMETHEE II in the process for the relative decision-making of the best avoidance trajectory solution. This has given clues for the generation of new alerting rules to prevent the collisions.