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Autonomous Vehicles are complex modular systems with various inter-dependent safety critical modules, the failure of which leads to failure of the overall system. The Localization system, which estimates the pose of the vehicle in the global coordinate frame with respect to a map, has a drift in error, when operated only based on data from proprioceptive sensors. Current solutions to the problem are computationally heavy SLAM algorithms. An alternate system is proposed in the thesis which eliminates the drift by resetting the global coordinate frame to the local frame at every motion planning update. The system replaces the mission planner with a user interface(UI) onto which the User provides local navigation inputs, thus eliminating the need for maintenance of a Global frame. The User Input is considered in the decision framework of the behavioral planner, which selects a safe and legal maneuver for the vehicle to follow. The path and trajectory planners generate a trajectory to accomplish the maneuver and the controller follows the trajectory until the next motion planning update. A prototype of the system has been built on a wheeled robot and tested for the feasibility of continuous operation in Autonomous Vehicles. / Master of Science / Autonomous Vehicles are complex modular systems with various inter-dependent safety critical modules, the failure of which leads to failure of the overall system. One such module is the Localization system, that is responsible for estimating the pose of the vehicle in the global coordinate frame, with respect to a map. Based on the pose, the vehicle navigates to the goal waypoints, which are points in the global coordinate frame specified in the map by the route or mission planner of the planning module. The Localization system, however, consists of a drift in position error, due to poor GPS signals and high noise in the inertial sensors. This has been tackled by applying computationally heavy Simultaneous Localization and Mapping based methods, which identify landmarks in the environment at every time step and correct the vehicle position, based on the relative change in position of landmarks. An alternate solution is proposed in this thesis, which delegates navigation to the passenger. This system replaces the mission planner from the planning module with a User Interface onto which the passenger provides local Navigation input, which is followed by the vehicle. The system resets the global coordinate frame to the vehicle frame at every motion planning update, thus eliminating the error accumulated between the two updates. The system is also designed to perform default actions in the absence of user Navigation commands, to reduce the number of commands to be provided by the passenger in the journey towards the goal. A prototype of the system is built and tested for feasibility.

This thesis starts with an introduction to Simulataneous Localization and Mapping (SLAM) and more background on Visual SLAM (VSLAM). The goal of VSLAM is to map the world with a camera, and at the same time localize the camera in that world. One important step is to optimize the acquired map, which can be done in several different ways. In this thesis, two state-of-the-art optimization algorithms are identified and compared, namely the g2o package and the SBA package. The results show that SBA is better on smaller datasets, and g2o on larger. It is also discovered that there is an error in the implementation of the pinhole camera model in the SBA package.

This degree project essay examines the literary culture phenomenon known as poetry slam, a competitive format for performance poetry, with the purpose of discussing whether poetry slam could contribute to upper secondary school Swedish teaching or not. The essay consists of two parts, one where poetry slam is presented and one where poetry slam is discussed from a didactic perspective. In the first part, a brief history of poetry slam and its origins in the U.S. is presented, followed by a discussion of the ideology within the slam movement. Furthermore, the set of rules that regulate poetry slams are presented and finally the first part of the essay is ended with a discussion of poetry slam as a poetic genre of its own. The second part starts by presenting and discussing the concept of aesthetic learning processes and how poetry slam might enable such processes. Poetry slam is then related to the Swedish subject syllabus for upper secondary school in order to analyse whether there is any rationale for poetry slam in Swedish teaching or not. Furthermore, poetry slam is related to literacy development, first by discussing creative writing and then by discussing oral performance. The results of the analysis shows that poetry slam is foremost motivated in Swedish teaching as a literary historical content, partly as an opportunity to work with oral performance, and also to cultivate students’ linguistic subjectivity. Poetry slam could possibly enable aesthetic learning processes, although it may be questioned whether poetry slam qualifies as an artistic community professional enough to provide artistic working methods. The very concept of a school poetry slam could also be seen as a contradiction, when considering the slam movement’s self image of counter culture, as schools themselves could be interpreted as dominant literary establishments. Possible pitfalls related to poetry slam in Swedish teaching are the competitive format’s effect on students socio-emotional safety and the scoring system that may be hazardous for students’ linguistic subjectivity. Also, poetry slam may not meet the curricular demands on rhetoric performances which are mandatory in the upper secondary school Swedish courses, and thus questions may be raised if oral poetic performances can be prioritised at the expense of oral rhetoric performances. Moreover, the free, creative space provided to students within creative writing processes puts high demands on the teacher balancing students’ creative freedom, in order to possibly articulate hidden knowledge, with making sure that students do not spend time procrastinating.

L'un des défis fondamentaux de la robotique d'aujourd'hui est d'obtenir des cartes robustes en utilisant des mécanismes efficaces pour l'exploration et la modélisation des environnements toujours plus complexes. Ce problème est connu comme celui de la planification, de la localisation et de la cartographie simultanée (SPLAM).Dans cette thèse nous avons développé des outils pour obtenir une stratégie de SPLAM. D'abord, l'exploration est faite par le graphe d'exploration aléatoire (REG) basé sur la création d'une structure de graphe et sur un contrôle de frontières. Ensuite, le problème de localisation et de cartographie simultanée (SLAM) est résolu avec une stratégie topologique basée sur des B-Splines. Pour valider notre stratégie, nous avons créé une autre approche de SPLAM basée sur des outils connus comme le Filtre de Kalman étendu pour le SLAM et sur l'arbre aléatoire (SRT) pour l'exploration. Ces résultats sont comparés avec les résultats de notre stratégie. / One of the fundamental challenges of today's robotics is to obtain robust maps using efficient mechanisms for exploring and modeling increasingly complex environments. This is known as simultaneous planning, localization and mapping (SPLAM) problem.Considering this problem, in this thesis we have developed some tools to obtain a SPLAM strategy. First, the exploration is made by the Random Exploration Graph approach (REG) which is based on the creation of a graph structure and on a frontier control. Next, the simultaneous localization and mapping (SLAM) problem is solved using a B-Spline based topologic strategy. To validate our strategy, we have created another SPLAM approach based on well known tools as the Extended Kalman Filter for SLAM and on the Sensor based Random tree (SRT) for the exploration problem. Its results are confronted with the results obtained by our strategy.

"This work presents a novel method to efficiently factorize the combination of multiple factor graphs having common variables of estimation. The fast-paced innovation in the algebraic graph theory has enabled new tools of state estimation like factor graphs. Recent factor graph formulation for Simultaneous Localization and Mapping (SLAM) like Incremental Smoothing and Mapping using the Bayes tree (ISAM2) has been very successful and garnered much attention. Variable ordering, a well-known technique in linear algebra is employed for solving the factor graph. Our primary contribution in this work is to reuse the variable ordering of the graphs being combined to find the ordering of the fused graph. In the case of mapping, multiple robots provide a great advantage over single robot by providing a faster map coverage and better estimation quality. This coupled with an inevitable increase in the number of robots around us produce a demand for faster algorithms. For example, a city full of self-driving cars could pool their observation measurements rapidly to plan a traffic free navigation. By reusing the variable ordering of the parent graphs we were able to produce an order-of-magnitude difference in the time required for solving the fused graph. We also provide a formal verification to show that the proposed strategy does not violate any of the relevant standards. A common problem in multi-robot SLAM is relative pose graph initialization to produce a globally consistent map. The other contribution addresses this by minimizing a specially formulated error function as a part of solving the factor graph. The performance is illustrated on a publicly available SuiteSparse dataset and the multi-robot AP Hill dataset."

Robôs verdadeiramente autônomos devem conhecer o ambiente para executar tarefas complexas. Em ambientes desconhecidos o robô deve concorrentemente construir o mapa do ambiente e se localizar usando sensores proprioceptivos e exteroceptivos imprecisos. Isto é problemático, uma vez que o mapa parcial e possivelmente incorreto do ambiente será usado para corrigir erros de localização. Este problema importante da robótica móvel é conhecido como Localização e Mapeamento Simultâneos (SLAM). Quando um robô autonomamente executa o algoritmo de SLAM concorrentemente com uma estratégia de exploração, o problema passa a se chamar SLAM Ativo ou Exploração Integrada. Um dos principais desafios por trás destes problemas é o tratamento de fechamento de ciclos. Ao atravessar regiões desconhecidas ou ambientes esparsos, a pose do robô e o mapa podem não ser propriamente corrigidos por falta de informação. Quando isto acontece, as incertezas da posição do robô e do mapa aumentam, podendo levar a erros irrecuperáveis. Por outro lado, quando o ciclo é fechado corretamente, estas incertezas diminuem consideravelmente. Portanto, a escolha do caminho para explorar o ambiente pode drasticamente melhorar ou degradar a qualidade do mapeamento e da localização. Uma técnica bem conhecida de exploração de ambientes é a adaptação do problema de valor de contorno (BVP) para a equação de Laplace e condições de contorno de Dirichlet. Apesar de ser fácil de implementar, resultando em trajetórias de exploração suaves, esta técnica não endereça cuidadosamente erros de SLAM, uma vez que ela segue a descida do gradiente, o que pode não possibilitar revisitas, uma limitação crucial para o SLAM Ativo. Mesmo sendo uma técnica de exploração gulosa e direcionada a fronteiras, consideramos que a flexibilidade do BVP e condições de contorno de Dirichlet ainda são pouco exploradas. Nossa proposta é modificar o algoritmo de Exploração por BVP para executar comportamentos complexos, tais como revisitas e, em particular, fechamentos de ciclo. Apresentamos duas novas abordagens: a primeira faz uso de uma condição de contorno direcionada pelo tempo combinada a distorções de potencial para gerar comportamentos de fechamento de ciclo, além de um potencial que nunca cessa de existir, mesmo após o ambiente ter sido completamente explorado; a segunda, propicia o fechamento de ciclos aproveitando a propagação do potencial em regiões desconhecidas, através de um par dinâmico de condições de contorno que funcionam como obstáculos e objetivos virtuais. Ambas abordagens aproveitam o Esqueleto de Voronoi do ambiente para reduzir o custo computacional do algoritmo. Testes em ambientes reais e simulados usando o robô Pioneer 3DX mostram que as técnicas apresentadas apresetam melhores resultados quando comparadas a técnicas concorrentes. / Truly autonomous robots must know the environment in order to execute complex tasks. In unknown environments, the robot must construct a map and localize itself using noisy proprioceptive and exteroceptive sensors. This is problematic, since the partial and possibly inaccurate map of the environment will be used to correct localization errors. This important problem of mobile robotics is known as Simultaneous Localization and Mapping (SLAM). When a robot autonomously execute a SLAM algorithm concurrently with an exploration strategy, this problem is called Active SLAM or Integrated Exploration. One of the main challenges behind both these problems is the treatment of loop closures. While the robot traverses unknown regions or sparse environments, the robot pose and the map may not be properly corrected due to lack of information. When this happens, the uncertainties about the map and the robot pose increase, which may lead to unrecoverable SLAM errors. On the other hand, when a loop is closed successfully, these uncertainties drastically decrease. Therefore, path chosen to explore the environment can considerably improve or degrade the quality of both localization and mapping. One well known way to explore the environment is the adaptation of the Boundary Value Problem (BVP) for the Laplace Equation and Dirichlet boundary conditions. Even though it is easy to implement, resulting in smooth exploration trajectories, it does not carefully address SLAM errors, since it follows a gradient decent which not always allows revisits, a crucial limitation for Active SLAM. Despite being a greedy frontier driven exploration strategy, we consider the flexibility of the BVP and Dirichlet boundary conditions still under-explored for Active SLAM. Our proposal is to modify the BVP Exploration algorithm to execute complex exploration behaviors, such as revisits and, in particular, loop-closures. We present two new approaches: the first makes use of a time driven boundary value condition together with potential distortions to generate loop closing behaviors and a potential field that never ceases to exist, even after the exploration ends; the second enables loop closure behaviors with BVP by taking advantage of potential propagation in unknown space generated by a pair of dynamic boundary conditions functioning as virtual walls and goals. Both approaches take advantage of a local optimization that uses the Voronoi Skeleton to reduce the computational cost of the algorithm. Tests in real and simulated environments using a Pioneer 3DX show that the proposed approaches present better results when compared with competing approaches.

Morbillivirus est un genre de virus à ARN simple brin de polarité négative de la famille des Paramyxoviridae. Actuellement, il est composé de sept espèces responsables de maladies hautement contagieuses. Les infections par morbillivirus causent une forte mortalité chez l’Homme, les petits ruminants, les carnivores et chez certains mammifères marins. Les vaccins disponibles contre les morbillivirus exigent généralement 7-10 jours pour développer une immunité protectrice. Après la Peste Bovine, première maladie animale éradiquée avec succès, la peste des petits ruminants (PPR) est la prochaine cible pour l'éradication au niveau mondial d’ici 2030. Le vaccin actuel basé sur la souche PPR Nigeria 75/1 est adéquat pour la campagne d'éradication. Cependant, certaines améliorations peuvent être envisagées pour accroître son efficacité, raccourcir le temps nécessaire pour l’éradication et réduire les coûts. Par exemple, l’introduction d’un vaccin marqué positif/négatif permettrait la différenciation entre animaux infectés et vaccinés (DIVA stratégie), permettant ainsi en temps réel la surveillance de l'infection, la vaccination et l’élimination rapide des animaux infectés. Une autre amélioration pourrait être la modification du vaccin actuel par génétique inverse pour insérer une cassette exprimant des ARN interférents capables de cibler les souches virulentes PPRV. Ce vaccin thérapeutique serait utile en situation d'urgence pour contrôler l’infection le temps que l’immunité protectrice se mette en place après vaccination. Afin de développer et tester ces nouveaux vaccins et outils thérapeutiques, il est nécessaire d’utiliser des modèles petit animal et cellulaire pour limiter les expérimentations avec les animaux d'élevage Dans ce travail, nous avons contribué au développement d’un modèle cellulaire in vitro et d’un modèle murin in vivo pour étudier la pathogenèse des morbillivirus. Le présent document est divisé en trois principaux chapitres : « Identification d’un modèle cellulaire pour les études de pathogenèse du PPRV » ; « Construction d’un clone PPR marqué le gène luciférase rapporteur » ; et « Évaluation in vivo d’un petit ARN interférent (siRNA) contre les morbillivirus ». Dans le premier chapitre, la ligne cellulaire murine 10T1/2 a été éprouvée avec des souches atténuées et virulentes du PPRV dans des conditions différentes d’expression du récepteur SLAM de la chèvre et de la réponse interféron type I. Les résultats ont montré une permissivité des cellules 10T1/2 limitée aux souches virulentes du PPRV, laquelle est indépendante du récepteur SLAM de la chèvre et de la réponse interféron type I. Le deuxième chapitre visait à développer un PPRV recombinant capable d’exprimer une luciférase par la génétique inverse. Diverses stratégies d’assemblage du génome entier du PPRV ont été établies pour l’obtention du clone d’ADNc avec le génome complet du PPRV. Cependant, le rescue a été impossible à réaliser et les raisons en sont discutées. Le dernier chapitre englobait l’évaluation des siRNA comme outils thérapeutiques contre un autre morbillivirus recombinant capable d’exprimer la luciférase, le virus de la rougeole (MV). Alors que sur les lignées cellulaires nous avons observé 100% d’activité antivirale des siRNA contre le rMV-luc, la validation in vivo, utilisant le modèle souris transgénique CD46 humain sensible à la rougeole, a échoué. Pour conclure, ce travail apporte des avancées sur le développement d’outils pour étudier la pathogenèse non seulement du PPRV mais aussi d’autres morbillivirus. / Morbillivirus genus, a non-segmented negative single-strand RNA (ssRNA) group of viruses, belongs to the Paramyxoviridae family and is currently composed of seven species responsible to highly contagious diseases. Morbillivirus infections cause significant mortality in humans, small ruminants, carnivores and in some marine mammals. The available vaccines against morbillivirus infections require usually 7-10 days to induce a protective immunity. After Rinderpest, the first animal disease successfully eradicated, peste des petits ruminants (PPR) is the next target for global eradication by 2030.The current vaccine based on the Nigeria 75/1 is fit for purpose for the eradication campaign. However, some improvements can be envisaged to increase efficacy, shorten the time to complete the eradication and reduce costs. For instance, the introduction of a positive/negative marker in the vaccine could allow the Discrimination between Infected and Vaccinated Animals (DIVA strategy), thus enabling the real-time parallel monitoring of infection and vaccination, and rapid elimination of infected animals. Another improvement could be the modification of the current vaccine by reverse genetics to insert a cassette expressing interfering RNA targeting virulent strains of PPR. This therapeutic vaccine would be useful in emergency situations to control the infection over the delay necessary for the acquisition of an efficient vaccine protection. In order to develop and test these new vaccine tools, there is a need for new cell or small animal models to limit experiments with farm animals. In this work, we contributed in the development of in vitro and in vivo murine models to study morbillivirus pathogenesis. The present document is divided in three main chapters: “Identification of a cell model to PPRV pathogenesis studies”; “Construction of a full-length cDNA clone of PPRV with a luciferase reporter gene” and “In vivo evaluation of small interfering RNA (siRNA) against morbilliviruses”. In the first chapter the mouse cell line 10T1/2 was challenged with attenuated and wild type PPRV strains using different conditions of goat SLAM expression and type I IFN response. The results showed a restricted permissiveness of 10T1/2 to wild type PPRV, which was independent of goat SLAM receptor and type I IFN response. The second chapter aimed to develop a recombinant PPRV expressing luciferase through reverse genetics methodology. Various strategies to assembling the PPRV genome were established reaching up to the full-length cDNA PPRV-luciferase construction. However, the rescue could not be achieved and the reasons for that are discussed. The last chapter encompassed the evaluation of siRNA as a prophylactic tool against another luciferase recombinant morbillivirus, the measles virus. In vitro and in vivo studies were performed with the recombinant MV (rMV-luc). Whereas in cell lines siRNA showed 100% of antiviral activity against rMV-luc, the validation in vivo, using a human CD46 transgenic mouse model susceptible to measles, failed. In conclusion, this work provides advancements in the development of tools for the study of the pathogenesis of PPRV and other morbilliviruses.

Localização e Mapeamento são problemas fundamentais da robótica que vêm sendo estudados exaustivamente pela comunidade científica para a navegação de robôs móveis. A maior parte das pesquisas estão concentradas em implementações para computadores pessoais, mas pouco se tem feito na área de computação embarcada. Este trabalho mostra a análise e implementação em FPGA de um algoritmo de localização para ambientes dinâmicos composto por um filtro de partículas, e também de um algoritmo de mapeamento baseado na técnica de scan matching. Os algoritmos originais desenvolvidos em linguagem de programação C foram analisados e modificados para uma abordagem embarcada (embedded) em robôs reconfiguráveis utilizando-se o processador Nios II da Altera. Os algoritmos são comparados quanto ao desempenho, no intuito de servir como referência no futuro desenvolvimento da ferramenta de codesign autom´atico ARCHITECT+ / Localization and Mapping are fundamental robot navigation problems wich currently has been exaustily studied by scientific comunity. Most of research is concentrated on implementation for personal computers, and the robot navigation is done on static environment. But, these algorithms can not be directly applied for embedded solutions on dynamic environments. This work shows an analysis and implementation on FPGA of a localization algorithm for dynamic environments composed of a particle filter, and by an mapping algorthm known as scan matching. The original algorithm devoloped on C programming language for PCs are analised and modified for an embedded approach to mobile robots using Altera Nios II processor. Both C and embedded algorithms are compared within performance, to serve as reference on a future developement of automatic codesign tool ARCHITECT+