[pt] No campo em crescimento da robótica subaquática, Veículos Subaquáticos Automatizados (AUVs) estão se tornando cada vez mais importantes para uma variedade de usos, como exploração, mapeamento e inspeção. Esta dissertação foca em estudar os principais desafios da acoplagem automática de AUVs, considerando um ambiente 3D simulado personalizado. A pesquisa divide essa tarefa em duas partes principais: estimativa da pose da garagem e estratégia de controle do AUV. Utilizando uma mistura de métodos tradicionais e novos, incluindo sistemas baseados em marcos fiduciais, Redes Neurais Convolucionais (CNN) e Aprendizado por Reforço (RL), o estudo realiza experimentos para verificar o desempenho e as limitações do sistema. Um aspecto significativo desta dissertação é o uso de um ambiente 3D simulado para facilitar o desenvolvimento e o teste de algoritmos de acoplagem automática para AUVs. Este ambiente simula dinâmicas subaquáticas, sensores robóticos e atuadores, permitindo experimentar diferentes técnicas de estimativa de pose e estratégias de controle. Além disso, o estabelecimento de um ambiente 3D simulado amigável para RL representa uma contribuição relevante, oferecendo uma plataforma reutilizável que não apenas valida os algoritmos de acoplagem automática desenvolvidos neste estudo, mas também serve como base para futuras aplicações subaquáticas baseadas em RL. Em resumo, a dissertação explora uma série de cenários para avaliar a eficácia de várias técnicas de acoplagem automática. Inicialmente, ela utiliza servo-visualização junto com um controlador PID tradicional, seguido pela introdução de métodos mais avançados, como estimadores de pose baseados em CNN e controladores de Aprendizado por Reforço. Esses métodos são avaliados tanto individualmente quanto em combinações híbridas para medir sua adequação e limitações para entender os principais desafios por trás da acoplagem automática de AUVs. / [en] In the growing field of underwater robotics, Automated Underwater Vehicles (AUVs) are becoming more important for a range of uses, such as exploration, mapping, and inspection. This dissertation focuses on studying the main challenges of AUV auto-docking, considering a customized 3D simulated environment. The research breaks down this challenging task into two main parts: cage pose estimation and AUV control strategy. Using a mix of traditional and new methods, including fiducial-based systems, Convolutional Neural Networks (CNN), and Reinforcement Learning (RL), the study carries out experiments to check system performance and limitations. A significant aspect of this dissertation is using a 3D simulated environment to facilitate the development and testing of auto-docking algorithms for AUVs. This environment simulates crucial underwater dynamics, robotic sensors, and actuators, allowing for experimenting with different pose estimation techniques and control strategies. Additionally, the establishment of an RL-friendly 3D simulated environment stands as a relevant contribution, offering a reusable platform that not only validates the auto-docking algorithms developed in this study but also serves as a foundation for future RL-based underwater applications. In summary, the dissertation explores a range of scenarios to evaluate the efficacy of various auto-docking techniques. It initially utilizes visual servoing along with a traditional PID controller, followed by the introduction of more advanced methods like CNN-based pose estimators and Reinforcement Learning controllers. These methods are assessed both individually and in hybrid combinations to gauge their suitability and limitations for understanding the main challenges behind the AUV auto-docking.

The cardiac sodium ion channel (NaV1.5) is a protein with four domains (DI-DIV), each with six transmembrane segments. Its opening and subsequent inactivation results in the brief rapid influx of Na+ ions resulting in the depolarization of cardiomyocytes. The neurotoxin veratridine (VTD) inhibits NaV1.5 inactivation resulting in longer channel opening times, and potentially fatal action potential prolongation. VTD is predicted to bind at the channel pore, but alternative binding sites have not been ruled out. To determine the binding site of VTD on NaV1.5, we perform docking calculations and high-throughput electrophysiology experiments in the present study. The docking calculations identified two distinct binding regions. The first site was in the pore, close to the binding site of NaV1.4 and NaV1.5 blocking drugs in experimental structures. The second site was at the “mouth” of the pore at the cytosolic side, partly solvent-exposed. Mutations at this site (L409, E417, and I1466) had large effects on VTD binding, while residues deeper in the pore had no effect, consistent with VTD binding at the mouth site. Overall, our results suggest a VTD binding site close to the cytoplasmic mouth of the channel pore. Binding at this alternative site might indicate an allosteric inactivation mechanism for VTD at NaV1.5

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In recreational boating, docking is one of the most stressful and accident prone situations. Due to the loss of maneuverability at low speeds, it is a procedure that requires experience. There are mainly two problems when it comes to autonomous docking of a boat, these are identifying a berth’s position as well as keeping the boat on its intended path and correcting any deviations. Autonomous docking in recreational boating is still quite uncommon, with companies still exploring different solutions. This thesis proposes a Model Predictive Control (MPC) system combined with Pulsed Coherent Radar technology, equipped on an under-actuated boat model, to achieve autonomous docking. A major part of this thesis was to evaluate the amount and placement of radar sensors, as well as determining whether these are suitable in a water environment. In order to test this, the sensors were placed alongside the hull of the boat. It was found that the placement of sensors had a bigger impact than the amount when it came to correctly detecting the position of a berth. Once the placement of sensors and the berth position algorithmhad been done, a closed-loop MPC was used. This controller got constant feedback of the boat’s position relative the berth, in order to calculate the thruster control inputs for the next time step. The developed autonomous docking system was then implemented on the boat which was tested in a swimming pool. The optimal radar configuration combined withMPC, made it possible to successfully dock a boat autonomously without any modification to the berth. / För fritidsbåtlivet är tilläggning en av demest stressfulla och olycksbenägna situationerna. På grund av förlust av manövrering vid låga hastigheter är det en procedur som kräver erfarenhet. Det finns främst två problem när det kommer till autonom tilläggning, det är att identifiera positionen av en brygga såväl som att hålla båten på den avsedda kursen och rätta till små avvikelser. Autonom tilläggning för fritidsbåtlivet är fortfarande rätt ovanligt och företag utforskar fortfarande olika lösningar. Denna avhandling föreslår ett Modellprediktivt Reglersystem (MPC) kombinerat med Pulserad Koherent Radarteknik som är utrustad på en underaktuerad båtmodell för att uppnå autonom tilläggning. En stor del av avhandlingen var att utvärdera antalet och placeringen av radarsensorer, såväl som att fastställa om dessa är lämpliga att användas i en vattenmiljö. För att undersöka detta placerades sensorerna längs med båtens skrov. Det konstaterades att placeringen av sensorer hade en större påverkan än mängden när det kom till att läsa av positionen av bryggan korrekt. När placeringen av sensorer och bryggpositionsalgoritmen var klar användes MPC med återkoppling. Denna regulator fick konstant återkoppling av båtens position relativt bryggan för att räkna ut styrsignal till motorerna för nästa tidssteg. Den utvecklade autonoma tilläggningen var sedan implementerad på båten som testades i en pool. Den optimala radarplaceringen kombinerat med MPC gjorde det möjligt att med framgång kunna lägga till båten autonomt utan modifiering av bryggan.