<p dir="ltr">The technological advancements in marine robotics led to the expansion of the autonomous underwater vehicle (AUV) fleet. Depending on the applications, the type of the AUV ranges across various shapes and sizes. It seeks a solution for the issue of limited power capacity, often in terms of underwater docking systems. Underwater docking poses a significant challenge for AUVs, especially when considering the diverse shapes and sizes of these vehicles. Existing solutions usually are task specific, and do not address the idea of scalable underwater docking system design.<br>This thesis investigates the adaptability of the specific docking system design, previously validated for torpedo-shaped AUVs, to boxed-shaped AUVs in a nonlinear open water environment. In order to achieve this goal, the scalability of the docking system design of choice was tested in an open water non-linear underwater environment and validated. The scalability of the robust docking system was adapted to the box-shaped AUV, encompassing path planning, path following, and docking maneuver. The adapted docking system was based on the optic methods for docking station detection and subsequent docking. Additionally, the simulated environment was developed for the AUV model, for testing and debugging purposes. In the simulation, a custom PID controller was developed along with integrating the navigation and guidance package, to fully simulate the real life behavior of the AUV. </p><p dir="ltr">Furthermore, this work introduces a recurrent neural network-based architecture for investigating temporal dependencies of the sequential data input. The proposed architecture is based on CNN for spatial feature extraction and LSTM/GRU for temporal feature detection. The dataset collection is based on the simulation environment, by enhancing the artificial images with imposed realism. The dataset was gathered on different levels of turbidity and the collection process was automated.</p>

<div>Exploring and surveilling the marine environment away from shore is critical for scientific, economic, and military purposes as we progress through the 21st century. Until recently, these missions far from shore were only possible using manned surface vehicles. Over the past decade, advances in energy density, actuators, electronics, and controls have enabled great improvements in vehicle endurance, yet, no solution is capable of supporting persistent operation especially when considering power hungry scientific surveys. This dissertation summarizes contributions related to the development of an adaptable underwater docking station and associated navigation solutions to allow applications in the wide range of maritime missions. The adaptable docking system is a novel approach to the standard funnel shaped docking station design that enables the dock to be collapsible, portable, and support a wide range of vehicles. It has been optimized and tested extensively in simulation. Field experiments in both pool and open water validate the simulation results. The associated control strategies for approach and terminal homing are also introduced and studied in simulation and field trials. These strategies are computationally efficient and enable operation in a variety of scenarios and conditions. Combined, the adaptable docking system and associated navigation strategies can form a baseline for future extended endurance missions away from manned support.</div>

<p dir="ltr">The growing movement toward sustainable use of ocean resources is driven by the pressing need to alleviate environmental and human stressors on the planet and its oceans. From monitoring the food web to supporting sustainable fisheries and observing environmental shifts to protect against the effects of climate change, ocean observations significantly impact the Blue Economy. Acknowledging the critical role of Autonomous Underwater Vehicles (AUVs) in achieving persistent ocean exploration, this research addresses challenges focusing on the limited energy and storage capacity of AUVs, introducing a comprehensive underwater docking solution with a specific emphasis on enhancing the terminal homing phase through innovative vision algorithms leveraging neural networks.</p><p dir="ltr">The primary goal of this work is to establish a docking procedure that is failure-tolerant, scalable, and systematically validated across diverse environmental conditions. To fulfill this objective, a robust dock detection mechanism has been developed that ensures the resilience of the docking procedure through \comment{an} improved detection in different challenging environmental conditions. Additionally, the study addresses the prevalent issue of data sparsity in the marine domain by artificially generating data using CycleGAN and Artistic Style Transfer. These approaches effectively provide sufficient data for the docking detection algorithm, improving the localization of the docking station.</p><p dir="ltr">Furthermore, this work introduces methods to compress the learned docking detection model without compromising performance, enhancing the efficiency of the overall system. Alongside these advancements, a station-keeping algorithm is presented, enabling the mobile docking station to maintain position and heading while awaiting the arrival of the AUV. To leverage the sensors onboard and to take advantage of the computational resources to their fullest extent, this research has demonstrated the feasibility of simultaneously learning docking detection and marine wildlife classification through multi-task and transfer learning. This multifaceted approach not only tackles the limitations of AUVs' energy and storage capacity but also contributes to the robustness, scalability, and systematic validation of underwater docking procedures, aligning with the broader goals of sustainable ocean exploration and the blue economy.</p>