京都大学 / 新制・課程博士 / 博士(工学) / 甲第23887号 / 工博第4974号 / 新制||工||1777(附属図書館) / 京都大学大学院工学研究科航空宇宙工学専攻 / (主査)教授 藤本 健治, 教授 泉田 啓, 教授 大塚 敏之 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

The transition from a mathematical algorithm to a physical hardware implementation is non-trivial. This thesis discusses the issues involved in the transition from the theory of real-time trajectory generation all the way through a hardware experiment. Documentation of the validation process as well as modifications to the existing theory as a result of hardware testing are treated at length. The results of hardware experimentation show that trajectory generation can be done in real-time in a manner facilitating coordination of multiple small UAVs.

Nowadays, more and more flexible and efficient processes are required in modern industrial applications. In this field, robots are a key technoligy. In this paper a application is considered, where a 6-axis-industrial robot has to pick-and-place objects time efficiently in a constantly changing environment. Therefore, a concept for automated motion planning is presented, which is composed of two steps which are path planning and trajectory generation. In this paper suitable and established model-based methods are analyzed and chosen. Eventually, the suitability of the presented concept for the considered task is shown by implementing the concept in Matlab and applying it to a 6-axis articulated robot arm.

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Since the origin of the automotive at the end of the 19th century, the traffic flow is subject to a constant increase and, unfortunately, involves a constant augmentation of road accidents. Research studies such as the one performed by the World Health Organization, show alarming results about the number of injuries and fatalities due to these accidents. To reduce these figures, a solution lies in the development of Advanced Driver Assistance Systems (ADAS) which purpose is to help the Driver in his driving task. This research topic has been shown to be very dynamic and productive during the last decades. Indeed, several systems such as Anti-lock Braking System (ABS), Electronic Stability Program (ESP), Adaptive Cruise Control (ACC), Parking Manoeuvre Assistant (PMA), Dynamic Bending Light (DBL), etc. are yet market available and their benefits are now recognized by most of the drivers. This first generation of ADAS are usually designed to perform a specific task in the Controller/Vehicle/Environment framework and thus requires only microscopic information, so requires sensors which are only giving local information about an element of the Vehicle or of its Environment. On the opposite, the next ADAS generation will have to consider more aspects, i.e. information and constraints about of the Vehicle and its Environment. Indeed, as they are designed to perform more complex tasks, they need a global view about the road context and the Vehicle configuration. For example, longitudinal control requires information about the road configuration (straight line, bend, etc.) and about the eventual presence of other road users (vehicles, trucks, etc.) to determine the best reference speed. [...]

Ce travail est développé dans le contexte des projets SESAR et Next-Gen, où de nouvelles applications de la gestion du trafic aérien (ATM) comme le concept de gestion d'opérations en 4D, se sont focalisées sur les opérations basées sur la trajectoire (TBO - Trajectory Based Operations). Ces opérations sont en relation avec l'extension de la flexibilité de la séparation entre avions, et par conséquence, avec l'augmentation de la capacité du trafic aérien. En sachant qu'une évolution des routes fixes et autorisations émises par le contrôle du trafic aérien (ATC - Air Traffic Control) vers des trajectoires flexibles est imminente, en s'appuyant en même temps aux niveaux les plus élevés de l'automatique embarquée, ce travail de recherche s'intéresse aux sujets qui aideront à la transition des systèmes actuels vers les systèmes compatibles avec les nouveaux besoins des TBO. Les principaux axes de recherche de ce manuscrit s'articulent en trois points: La génération de trajectoires en 4D, le guidage en 4D, et l'estimation de la masse d'un avion pour l'optimisation des trajectoires. Concernant la génération des trajectoires, le besoin des utilisateurs d'espaces aériens de planifier leurs routes préférées à partir d'un point d'entrée dans l'espace aérien sans être limités par les configurations existantes est considéré. Une solution particulière pour la génération de trajectoires lisses en 4D à partir de points de contrôle prédéfinis est alors explorée. La méthode proposée s'appuie sur les courbes de Bézier, et elle permet de contrôler la distance euclidienne entre le point de contrôle donné et la trajectoire proposée. Ceci est fait en modifiant la trajectoire de telle façon qu'elle reste à l'intérieur des limites des facteurs de charge, en considérant un compromis entre la courbure de la trajectoire et la vitesse voulue de l'avion, ce qui représente une étape importante dans le chemin vers les TBO. Le guidage précis en 4D améliorera la sûreté en diminuant l'occurrence de quasi-collisions aériennes pour des trajectoires en 4D planifiées en avance. En conséquence, deux autopilotes et deux méthodes de guidage sont développées avec l'objectif de réduire la charge de travail des contrôleurs du trafic aérien associée à un vol. Les techniques de backstepping et feedback linearization sont utilisées pour le pilotage, alors que l'inversion non linéaire directe et indirecte sont adoptées pour le guidage. De plus, l'impact de la connaissance inexacte de la masse de l'avion dans le suivi de trajectoires, ses conséquences dans l'optimisation, la consommation de carburant, et la performance de l'avion, a conduit à l'implémentation d'une estimation embarquée de la masse de l'avion. L'approche créée est basée sur les moindres carrées, en fournissant des estimations de la masse initiale et la masse courante, toutes les deux avec une précision suffisante pour atteindre les objectifs liées aux TBO. Les méthodes proposées dans cette thèse sont examinées en utilisant un modèle à six degrés de liberté, dont les paramètres approchent un appareil du type B737-200 ou A320-200. La simulation est basée sur une modélisation complète et non linéaire de la dynamique des avions de transport incluant des perturbations liées au vent. Des réseaux de neurones sont utilisés pour obtenir les différents coefficients aérodynamiques correspondant aux forces et moments de l'avion. / This work is developed with the perspective of SESAR and Next-Gen projects, where new applications of Air Traffic Management (ATM) such as the Full 4D Management concept, are centered on Trajectory-Based Operations (TBO), deeply related with the extension of the flexibility in separation between aircraft, and hence, with the augmentation of air traffic capacity. Therefore, since a shift from fixed routes and Air Traffic Control (ATC) clearances to flexible trajectories is imminent, while relying on higher levels of onboard automation, the thesis hinges around topics that should enable or ease the transition from current systems to systems compliant with the new expectancies of Trajectory-Based Operations. The main axes of the manuscript can be summarized in three topics: 4D trajectory generation, 4D guidance, and mass estimation for trajectory optimization. Regarding the trajectory generation, the need of airspace users to plan their preferred route from an entry to an exit point of the airspace without being constrained by the existent configurations is considered. Thus, a particular solution for 4D smooth path generation from preexisting control points is explored. The method is based on Bezier curves, and is able to control the Euclidian distance between the given control points and the proposed trajectory. This is done by reshaping the path to remain within load factor limits, taking into account a tradeoff between path curvature and aircraft intended speed, representing a milestone in the road towards Trajectory-Based Operations. It is considered that accurate 4D guidance will improve safety by decreasing the occurrence of near mid-air collisions for planned conflict free 4D trajectories. In consequence, two autopilots and two guidance approaches are developed with the objective of diminishing the workload for air traffic controllers associated to a single flight. The backstepping and feedback linearization techniques are used for attitude control, while direct and indirect nonlinear inversion are adopted for guidance. Furthermore, the impact of inaccurate mass knowledge in trajectory guidance, with consequences in optimization, fuel consumption, and aircraft performance, has led to the implementation of an on-board aircraft mass estimation. The created approach is based on least squares, providing an initial mass estimation, and online computations of the current mass, both with enough accuracy to meet the objectives related to TBO. The methods proposed in this thesis are tested in a six degrees of freedom Matlab model with its parameters chosen similar to an aircraft type B737-200 or A320-200. The simulation is based on a full nonlinear modelling of transport aircraft dynamics under wind disturbances. Trained neural networks are used to obtain the aerodynamic coefficients corresponding the aircraft forces and moments.

This report presents optimal control methods integrated with hierarchical control framework to realize real-time collision-free optimal trajectories for motion control in kinematic chain manipulator (KCM) robot systems under dynamic environments. Recently, they have been increasingly used in applications where manipulators are required to interact with random objects and humans. As a result, more complex trajectory planning schemes are required. The main objective of this research is to develop new motion control strategies that can enable such robots to operate efficiently and optimally in such unknown and dynamic environments. Two direct optimal control methods: The direct collocation method and discrete mechanics for optimal control methods are investigated for solving the related constrained optimal control problem and the results are compared. Using the receding horizon control structure, open-loop sub-optimal trajectories are generated as real-time input to the controller as opposed to the predefined trajectory over the entire time duration. This, in essence, captures the dynamic nature of the obstacles. The closed-loop position controller is then engaged to span the robot end-effector along this desired optimal path by computing appropriate torque commands for the joint actuators. Employing a two-degree of freedom technique, collision-free trajectories and robot environment information are transmitted in real-time by the aid of a bidirectional connectionless datagram transfer. A hierarchical network control platform is designed to condition triggering of precedent activities between a dedicated machine computing the optimal trajectory and the real-time computer running a low-level controller. Experimental results on a 2-link planar robot are presented to validate the main ideas. Real-time implementation of collision-free workspace trajectory control is achieved for cases where obstacles are arbitrarily changing in the robot workspace.

This report presents optimal control methods integrated with hierarchical control framework to realize real-time collision-free optimal trajectories for motion control in kinematic chain manipulator (KCM) robot systems under dynamic environments. Recently, they have been increasingly used in applications where manipulators are required to interact with random objects and humans. As a result, more complex trajectory planning schemes are required. The main objective of this research is to develop new motion control strategies that can enable such robots to operate efficiently and optimally in such unknown and dynamic environments. Two direct optimal control methods: The direct collocation method and discrete mechanics for optimal control methods are investigated for solving the related constrained optimal control problem and the results are compared. Using the receding horizon control structure, open-loop sub-optimal trajectories are generated as real-time input to the controller as opposed to the predefined trajectory over the entire time duration. This, in essence, captures the dynamic nature of the obstacles. The closed-loop position controller is then engaged to span the robot end-effector along this desired optimal path by computing appropriate torque commands for the joint actuators. Employing a two-degree of freedom technique, collision-free trajectories and robot environment information are transmitted in real-time by the aid of a bidirectional connectionless datagram transfer. A hierarchical network control platform is designed to condition triggering of precedent activities between a dedicated machine computing the optimal trajectory and the real-time computer running a low-level controller. Experimental results on a 2-link planar robot are presented to validate the main ideas. Real-time implementation of collision-free workspace trajectory control is achieved for cases where obstacles are arbitrarily changing in the robot workspace.

Submitted by Clebson Anjos (clebson.leandro54@gmail.com) on 2016-02-15T20:03:45Z No. of bitstreams: 1 arquivototal.pdf: 2424729 bytes, checksum: 0b742be3286a70e0901c3d5a00813f6f (MD5) / Made available in DSpace on 2016-02-15T20:03:45Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2424729 bytes, checksum: 0b742be3286a70e0901c3d5a00813f6f (MD5) Previous issue date: 2015-08-21 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / One of the main issues related to the mobile robotics area is to find the most efficient way to perform the navigation from one point to another over environments, considering maximum safety and spending as less as possible time and computer resources. From this perspective, the aim of this work was to specify improved heuristics that could be applicable to cost functions of key A* based algorithms and use, more efficiently, the available computational resources. In this way, our approach aimed at minimizing the amount of collisions, the length of paths, and the processing time by minimizing the importance of g(n) term, which accounts for storing information from past steps of A* family algorithms. To show the effects of this modification, a survey of the best search strategies in dynamic and static environments was carried out and, after that, we analyzed the four best and latest algorithms, according to the specialized literature. Some comparisons have been made considering static and highly dynamic environments with different directions and search parameters to measure the quality of generated paths. Then, these algorithms were again analyzed with their cost functions modified according to our approach. The results of the comparison show that the R* algorithm, with forward search, is the most efficient for different spaces and searches. However, the change in their respective cost functions provided a significant improvement in the already excellent results achieved by the algorithms. In static environments, this modification showed up to be more effective for large and complex problems, which are commonly used for real robots. In highly dynamic environments, the cost function modification provided a considerable reduction in the time of planning and number of iterations to find the goal, as well as reductions in the memory utilization. / Uma das principais questões relacionados ao tema da robótica móvel é descobrir a maneira mais eficiente para realizar a navegação, de um ponto a outro no ambiente, com máxima segurança e despendendo a menor quantidade de tempo e de recursos computacionais possível. À vista disso, o presente trabalho se motiva a desenvolver uma melhoria heurística que possa ser aplicável às funções custo dos principais algoritmos baseados na família A* e que propõe utilizar, de forma mais eficiente, os recursos computacionais disponíveis, aperfeiçoando assim, os resultados obtidos através dos principais algoritmos de buscas aplicados à robótica móvel. A mesma tem o objetivo de minimizar a quantidade de colisões, a duração do trajeto, bem como o tempo de processamento através da minimização da importância da variável g(n) - responsável em armazenar informações subutilizadas do passado dos algoritmos. Para visualizar os efeitos dessa modificação, um levantamento das melhores estratégias de busca em ambiente estático e dinâmico foi realizado e, através deste, foram analisados os quatro melhores e mais atuais algoritmos destacados pela literatura técnica especializada. Algumas comparações foram efetuadas considerando ambientes estáticos e altamente dinâmico com diferentes direções de busca e parâmetros que visavam mensurar a qualidade das trajetórias geradas. Em seguida, esses foram novamente analisados com suas respectivas funções custo modificada. Os resultados da comparação demonstraram que o algoritmo R*, com direção de busca direta, é o mais eficiente para diferentes espaços e pesquisas. No entanto, a modificação em suas respectivas funções custo proporcionou uma melhora significativa nos resultados conquistados pelos algoritmos originais. Em ambientes estáticos, esta modificação se mostrou mais eficaz para problemas grandes e complexos, os que são efetivamente utilizados por robôs reais. Em ambientes altamente dinâmicos, a mesma apresentou uma redução considerável no tempo de planejamento e no número de iterações para localizar o objetivo, bem como reduziu a utilização de memória o que, consequentemente, tornou os robôs mais ágeis e habilidosos.