Unmanned aerial vehicles are becoming more popular in today’s society, which results in the rise of laws intended to maintain safety. To abide by these, while allowing the technology to expand, functioning path-planning algorithms are required.This also includes having methods for detecting and managing obstacles. This project aims to improve an existing path-planning algorithm that is based on A* and implemented in Python.The solution consisted of using functions for finding polytopeintersection,as well as optimizing the collision avoidance and the search algorithm. In addition to that, realistic constraints were implemented on the generated trajectory in order to reflect real-life limitations. The results demonstrated that the paths were always feasible, with respect to input and position constraints. The program’s computation time was also reduced up to 89% of the original run-time. There is, however, still room for improvement since the original code generated a shorter path for the three scenarios it was created for. On the other hand,the improved algorithm could handle a new scenario, which the original code failed to do. / Obemannade flygfarkoster blir alltmer vanliga i dagens samhälle, vilket resulterar i uppkomsten av nya lagar ämnade åt att upprätthålla säkerhet. För att förhålla sig till dessa, samtidigt som teknologin tillåts expandera, krävs fungerande vägplaneringsalgoritmer. Där ingår det även att ha metoder för att upptäcka och hantera hinder. Detta projekt syftar till att förbättra en befintlig vägplaneringsalgoritm som är baserad på A* och implenterad i Python. Lösningsmetoden bestod av att använda inbyggda Python-funktioner ämnade åt att finna skärningar mellan polytoper, samt optimera kollisionshantering och sökalgoritmen. Dessutom infördes realistiska krav på den framställda vägen i syfte om att reflektera verlighetens begränsningar. Resultatet visade att vägarna alltid var genomförbara, med avseende på inmatningsoch positionsrelaterade villkor. Programmets beräkningstid hade även reducerats upptill 89% av den ursprungliga körtiden. Det finns dock utrymme för förbättringar då den ursprungliga koden generar en kortare väg för de tre scenarion den tillverkades för. Däremot kinde den förbättrade algoritmen hantera ett nytt scenario, en ursprungliga koden misslyckades med. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

Though still in relative infancy, the field of flapping flight has potential to have a far-reaching impact on human life. Nature presents a myriad of examples of successful uses of this locomotion. Human efforts in flapping flight have seen substantial improvement in recent times. Wing kinematics are a key aspect of this study. This study summarizes previous wing trajectory generators and presents a new trajectory generation method built upon previous methods. This includes a novel means of commanding unequal half-stroke durations subject to robotic trajectory continuity requirements. Additionally, previous optimization methods are improved upon. Experimental optimization is performed using the new trajectory generation method and a more traditional means. Methods for quantifying and compensating for sensor time-dependence are also discussed. Results show that the Polar Fourier Series trajectory generator advanced rapidly through the optimization process, especially during the initial phase of experimentation. The Modified Berman and Wang trajectory generator moved through the design space more slowly due to the increased number of kinematic parameters. When optimizing lift only, the trajectory generators produced similar results and kinematic forms. The findings suggest that the objective statement should be modified to reward efficiency while maintaining a certain amount of lift. It is expected that the difference between the capabilities of the two trajectory generators will become more apparent under such conditions.

As an attempt to steer away from developing an autonomous robot with complex centralised intelligence, this thesis proposes an intelligent environment infrastructure where intelligences are distributed in the environment through collaborative vision sensors mounted in a physical architecture, forming a wireless sensor network, to enable the navigation of unintelligent robots within that physical architecture. The aim is to avoid the bottleneck of centralised robot intelligence that hinders the application and exploitation of autonomous robot. A bio-mimetic snake algorithm is proposed to coordinate the distributed vision sensors for the generation of a collision free Reference-snake (R-snake) path during the path planning process. By following the R-snake path, a novel Accompanied snake (A-snake) method that complies with the robot's nonholonomic constraints for trajectory generation and motion control is introduced to generate real time robot motion commands to navigate the robot from its current position to the target position. A rolling window optimisation mechanism subject to control input saturation constraints is carried out for time-optimal control along the A-snake. A comprehensive simulation software and a practical distributed intelligent environment with vision sensors mounted on a building ceiling are developed. All the algorithms proposed in this thesis are first verified by the simulation and then implemented in the practical intelligent environment. A model car with less on-board intelligence is successfully controlled by the distributed vision sensors and demonstrated superior mobility.

The development of autonomous driving in recent years has been in full swing and one of the aspects that Autonomous Vehicles (AVs) should always focus on is safety. Although the corresponding technology has gradually matured, and AVs have performed well in a large number of tests, people are still uncertain whether AVs can cope with all possible situations. This world is complex and ever-changing, experiencing countless disturbances every moment, and according to The Butterfly Effect, even the most insignificant small disturbance may set off a huge storm in the near future. If AVs really enter people’s daily lives, they will inevitably encounter many unexpected situations that have never been experienced before. Thus how to ensure that AVs can handle these well has become the most important issue at the moment. It is necessary to give the Automated Driving System (ADS) sufficient challenges during training and testing for acceptable safety and stability. However, dangerous and extreme driving scenarios in the real world are very rare, and it is also very expensive for such a test to be carried out in reality. Therefore, artificially creating a series of critical driving scenarios then training and testing the ADS in a simulation environment has become the current mainstream solution. This thesis project builds a complete framework for the automatic generation, simulation, and analysis of safety-critical driving scenarios. First, the specified scenarios and features are sequentially extracted from the naturalistic driving dataset through pre-defined rules; then a Density Estimation Model is adopted to learn the features, trying to find the distribution of the specified scenarios; after the distribution is obtained, synthetic driving scenarios can be obtained by sampling. Finally, visualize these synthetic scenarios via simulation for safety assessment and data analysis. / Utvecklingen av självkörande fordon har varit i full gång de senaste åre och en av aspekterna som självkörande alltid bör fokusera på är säkerheten. Även om motsvarande teknik gradvis har mognat, och självkörande har presterat bra i ett stort antal tester, är människor fortfarande osäkra på om självkörande klarar av alla möjliga situationer. Den här världen är komplex och ständigt föränderlig, upplever otaliga störningar varje ögonblick, och enligt The Butterfly Effect kan även den mest obetydliga lilla störningen sätta igång en enorm storm inom en snar framtid. Om självkörande verkligen kommer in i människors dagliga liv kommer de oundvikligen att möta många oväntade situationer som aldrig har upplevts tidigare. Så hur man säkerställer att självkörande kan hantera dessa väl har blivit den viktigaste frågan för tillfället. Det är nödvändigt att ge självkörande tillräckliga utmaningar underträning och testning för acceptabel säkerhet och stabilitet. Men farliga och extrema körscenarier i den verkliga världen är mycket sällsynta, och det är också mycket dyrt att genomföra ett sådant test i verkligheten. Att på konstgjord väg skapa en serie kritiska körscenarier och sedan träna och testa det automatiserade körsystemet i en simuleringsmiljö har därför blivit den nuvarande vanliga lösningen. Detta examensarbete bygger ett komplett ramverk för automatisk generering, simulering och analys av säkerhetskritiska körscenarier. Först extraheras de specificerade scenarierna och funktionerna sekventiellt från den naturalistiska kördatauppsättningen genom fördefinierade regler; sedan antas en densitetsuppskattningsmodell för att lära sig funktionerna och försöka hitta fördelningen av de specificerade scenarierna; efter att fördelningen erhållits kan syntetiska körscenarier erhållas genom provtagning. Slutligen, visualisera dessa syntetiska scenarier via simulering för säkerhetsbedömning och dataanalys.

Dans ces travaux, nous proposons un système de guidage automatique pour la navigation sûre d'un robot mobile dans un monde ouvert. Le principe est de contrôler la direction et la vitesse du véhicule afin de préserver son intégrité physique et celle de son environnement. Cela se traduit par la généralisation du concept d'obstacle permettant d'estimer l'espace de vitesses admissibles par le véhicule en fonction de la surface de navigation, des capacités du véhicule et de son état. Afin d'atteindre cet objectif, le système doit pour chaque itération : 1) fournir à la tâche de perception une zone sur laquelle elle devra focaliser son attention pour la reconstruction de l'environnement ; 2) générer des trajectoires admissibles par le véhicule ; 3) estimer le profil de vitesse admissible pour chacune d'entre elles ; 4) pour finir, sélectionner la plus optimale par rapport à un critère prédéfini. Des résultats simulés et obtenus sur un démonstrateur réel permettent d'analyser les performances obtenues du système face à des scénarios divers et en démontre la pertinence. / In this work, we propose an automatic guidance system for safe navigation of a mobile robot in an open environment. The principle is to control the direction and the speed oh the vehicle in order to preserve its physical integrity and that of its environment. That results in the generalization of obstacle's concept to estimate the admissible speeds of the vehicle taking into account the surface navigation, the capabilities of the vehicle and its state. To accomplish this objective, th system has to ; 1) provide to the perception task an area on witch it can focus its attention to build the environment, 2) generate acceptable trajectories by th vehicles ; 3) estimate the admissible speed profile for each of them, 4) finally, select the most optimal with respect to a predefined criterion. Simulated and real results show the performance of the system obtained against various scenarios.

A contribuição e objetivo desta tese é desenvolver um modelo de redes neurais artificiais, baseado em princípios de memória associativa, capaz de resolver o problema de planejamento e controle ponto a ponto de trajetória de um braço mecânico imerso em um ambiente parcialmente conhecido e/ou sujeito a ruídos. O modelo proposto é formado por dois planos: plano seqüência temporal e plano ângulo. Para o plano seqüência temporal, o novo modelo proposto chamado de Memória Associativa Multidirecional Temporal (TMAM) é capaz de armazenar e recuperar n-tuplas de informações, lidar com informações ruidosas e/ou incompletas e aprender seqüências temporais. TMAM utiliza representação contínua e realimentação autoassociativa. O plano ângulo é formado pelo modelo RBF que é responsável por produzir as informações de ângulos das juntas do braço mecânico. A composição dos dois planos forma o sistema completo que é responsável pelo planejamento e controle ponto a ponto de trajetória. Em resumo, o sistema recebe informações do ponto origem e do ponto alvo, estabelece uma trajetória para atingir o ponto alvo a partir do ponto de origem e transforma os pontos espaciais da trajetória em valores de ângulos das juntas. Os resultados obtidos mostram que o modelo TMAM é capaz de recuperar, interpelar e extrapolar pontos nas seqüências, é capaz de gerar trajetórias, de memorizar seqüências de diferentes tamanhos e de lidar com duas trajetórias ao mesmo tempo. O modelo apresenta também rápido treinamento. O modelo RBF é capaz de recuperar as saídas desejadas apresentando um erro pequeno e é capaz de receber um padrão que apresenta um ponto final inatingível e gerar um conjunto de ângulos que representa um ponto final atingível. / The aim of this project is to develop an artificial neural networks model based on principles of associative memory. This neural network model must be able to solve the problem of trajectory planning and point to point control of a robot arm, which is located in a partially known and/or noisy environment. The proposed model is composed by two surfaces: the temporal sequence surface and the angle surface. For the temporal sequence surface the new propose model Temporal Multidirectional Associative Memmy (TMAM) is able to store and recall n-tuplas of information, to deal with noisy and/or incomplete information and to learn temporal sequences. TMAM uses a continuas representation and autoassociative feedback. A RBF model is used to implement the angle surface, which is liable for producing the angle information for the joint of the robot arm. The two surfaces compose the whole system which is liable for the trajectory planning and system control. Hence, the system receives information about the initial point and the target point, constructs the trajectory to reach the target point from the initial point and converts the spatial points which compose the trajectory, in values of joint angles. The obtained results show that TMAM model can recall, interpolate and extrapolate points in the sequences. The model has the ability of generating new trajectories and memorizing different size of sequences at the same time. This model also shows fast learning. The RBF model can recall the desired outputs with a small error and can receive a pattern which is formed by an unreachable final point and generate a set of angles which, in turn, represent a reachable final point.

Depuis l'invention de l'automobile à la fin du 19eme siècle, la taille du parc ainsi que l'importance du trafic routier n'ont cessées d'augmenter. Ceci a malheureusement été suivi par l'augmentation constante du Nombre d'accidents routiers. Un grand nombre d'études et notamment un rapport fourni par l'Organisation Mondiale de la Santé, a présenté un état alarmant du nombre de blessés et de décès liés aux accidents routiers. Afin de réduire ces chiffres, une solution réside dans le Développement de systèmes d'aide à la conduite qui ont pour but d'assister le conducteur dans sa tâche de conduite. La recherche dans le domaine des aides à la conduite s'est montrée très dynamique et productive durant les vingt dernières années puisque des systèmes tels que l'antiblocage de sécurité (ABS), le programme de stabilité électronique (ESP), le régulateur de vitesse intelligent (ACC), l'assistant aux manœuvres de parking (PMA), les phares orientables (DBL), etc. sont maintenant commercialisés et acceptés par la majorité des conducteurs. Cependant, si ces systèmes ont permis d'améliorer la sécurité des conducteurs, de nombreuses pistes sont encore à explorer. En effet, les systèmes d'aide à la conduite existants ont un comportement microscopique, en d'autres termes ils se focalisent uniquement sur la tâche qu'ils ont à effectuer. Partant du principe que la collaboration entre toutes ces aides à la conduite est plus efficace que leur utilisation en parallèle, une approche globale d'aide à la conduite devient nécessaire. Ceci se traduit par la nécessité de développer une nouvelle génération d'aide à la conduite, prenant en compte d'avantage d'informations et de contraintes liées au véhicule, au conducteur et à son environnement. [...] / 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. [...]

A contribuição e objetivo desta tese é desenvolver um modelo de redes neurais artificiais, baseado em princípios de memória associativa, capaz de resolver o problema de planejamento e controle ponto a ponto de trajetória de um braço mecânico imerso em um ambiente parcialmente conhecido e/ou sujeito a ruídos. O modelo proposto é formado por dois planos: plano seqüência temporal e plano ângulo. Para o plano seqüência temporal, o novo modelo proposto chamado de Memória Associativa Multidirecional Temporal (TMAM) é capaz de armazenar e recuperar n-tuplas de informações, lidar com informações ruidosas e/ou incompletas e aprender seqüências temporais. TMAM utiliza representação contínua e realimentação autoassociativa. O plano ângulo é formado pelo modelo RBF que é responsável por produzir as informações de ângulos das juntas do braço mecânico. A composição dos dois planos forma o sistema completo que é responsável pelo planejamento e controle ponto a ponto de trajetória. Em resumo, o sistema recebe informações do ponto origem e do ponto alvo, estabelece uma trajetória para atingir o ponto alvo a partir do ponto de origem e transforma os pontos espaciais da trajetória em valores de ângulos das juntas. Os resultados obtidos mostram que o modelo TMAM é capaz de recuperar, interpelar e extrapolar pontos nas seqüências, é capaz de gerar trajetórias, de memorizar seqüências de diferentes tamanhos e de lidar com duas trajetórias ao mesmo tempo. O modelo apresenta também rápido treinamento. O modelo RBF é capaz de recuperar as saídas desejadas apresentando um erro pequeno e é capaz de receber um padrão que apresenta um ponto final inatingível e gerar um conjunto de ângulos que representa um ponto final atingível. / The aim of this project is to develop an artificial neural networks model based on principles of associative memory. This neural network model must be able to solve the problem of trajectory planning and point to point control of a robot arm, which is located in a partially known and/or noisy environment. The proposed model is composed by two surfaces: the temporal sequence surface and the angle surface. For the temporal sequence surface the new propose model Temporal Multidirectional Associative Memmy (TMAM) is able to store and recall n-tuplas of information, to deal with noisy and/or incomplete information and to learn temporal sequences. TMAM uses a continuas representation and autoassociative feedback. A RBF model is used to implement the angle surface, which is liable for producing the angle information for the joint of the robot arm. The two surfaces compose the whole system which is liable for the trajectory planning and system control. Hence, the system receives information about the initial point and the target point, constructs the trajectory to reach the target point from the initial point and converts the spatial points which compose the trajectory, in values of joint angles. The obtained results show that TMAM model can recall, interpolate and extrapolate points in the sequences. The model has the ability of generating new trajectories and memorizing different size of sequences at the same time. This model also shows fast learning. The RBF model can recall the desired outputs with a small error and can receive a pattern which is formed by an unreachable final point and generate a set of angles which, in turn, represent a reachable final point.

Un bras de chargement de gaz est une structure articulée dans laquelle du méthane peut s’écouler à température cryogénique. En haute mer, ces bras sont installés sur le pont de navires-usines et se connectent à des méthaniers pour leur transférer du gaz. En raison de problèmes de sécurité et de performances, il est souhaité que le bras de chargement soit robotisé pour qu’il se connecte automatiquement. Cette thèse a pour objectif l‘automatisation de la connexion. Cette opération nécessite un pilotage de grande précision vis à vie de la taille du bras. Pour cette raison le bras est d’abord étalonné pour augmenter sa précision statique. Ensuite, des analyses modales expérimentales mettent en évidence l’importante souplesse de la structure des bras de chargement. Pour cette raison un générateur de trajectoires « douces », à jerk limité, est développé afin de piloter le bras sans le faire vibrer. Enfin, un système de compensation actif visant à compenser les mouvements relatifs des deux navires est mis en place. Cette compensation combine la génération de trajectoires douces avec une composante prédictive basée sur des réseaux de neurones. Cette dernière permet de prédire et d’anticiper les mouvements des navires sur l’océan, afin d’annuler tout retard dans la compensation. Finalement, cette thèse présente la première connexion automatique d’un bras de chargement, et démontre la validité de cette approche. / Marine loading arms are articulated structures that transfer liquefied gas between two vessels. The flanging operation of the loading arm to the receiving tanker is very sensitive. This thesis aims to robotize a loading arm so it can flange automatically. The required accuracy for the connection is very high. A calibration procedure is thus proposed to increase the accuracy of loading arms. Moreover a jerk-limited trajectory generator is developed to smoothly drive the arm without inducing oscillation. This element is important because the structures of loading arms have a very low stiffness and easily oscillate, as highlighted by modal analyses.A predictive active compensation algorithm is developed to track without delay the relative motion between the two vessels. This algorithm relies on an artificial neural network able to predict the evolution of this relative motion. Finally this thesis presents the first automatic connection of an offshore loading arm. The success of the final tests validate the feasibility the automatic connection and the validity of this approach.

L'objet de cette thèse est de proposer un cadre complet, du haut niveau au bas niveau, de génération de trajectoires pour un groupe de systèmes dynamiques indépendants. Ce cadre, basé sur la résolution de l'équation de Burgers pour la génération de trajectoires, est appliqué à un modèle original de drone trirotor et utilise la platitude des deux systèmes différentiels considérés. La première partie du manuscrit est consacrée à la génération de trajectoires. Celle-ci est effectuée en créant formellement, par le biais de la platitude du système considéré, des solutions à l'équation de la chaleur. Ces solutions sont transformées en solution de l'équation de Burgers par la transformation de Hopf-Cole pour correspondre aux formations voulues. Elles sont optimisées pour répondre à des contraintes spécifiques. Plusieurs exemples de trajectoires sont donnés.La deuxième partie est consacrée au suivi autonome de trajectoire par un drone trirotor. Ce drone est totalement actionné et un contrôleur en boucle fermée non-linéaire est proposé. Celui-ci est testé en suivant, en roulant, des trajectoires au sol et en vol. Un modèle est présenté et une démarche pour le contrôle est proposée pour transporter une charge pendulaire. / This thesis is dedicated to the creation of a complete framework, from high-level to low-level, of trajectory generation for a group of independent dynamical systems. This framework, based for the trajectory generation, on the resolution of Burgers equation, is applied to a novel model of trirotor UAV and uses the flatness of the two levels of dynamical systems.The first part of this thesis is dedicated to the generation of trajectories. Formal solutions to the heat equation are created using the differential flatness of this equation. These solutions are transformed into solutions to Burgers' equation through Hopf-Cole transformation to match the desired formations. They are optimized to match specific requirements. Several examples of trajectories are given.The second part is dedicated to the autonomous trajectory tracking by a trirotor UAV. This UAV is totally actuated and a nonlinear closed-loop controller is suggested. This controller is tested on the ground and in flight by tracking, rolling or flying, a trajectory. A model is presented and a control approach is suggested to transport a pendulum load.