Abstract
A major effort is put into the production of green energy as a countermeasure to climatic changes and sustainability. Thus, the energy industry is currently betting on offshore wind energy, using wind turbines with fixed and floating platforms. This technology can benefit greatly from interventive autonomous underwater vehicles (AUVs) to assist in the maintenance and control of underwater structures. A wireless charger system can extend the time the AUV remains underwater, by allowing it to charge its batteries through a docking station. The present work details the development process of a housing component for a wireless charging system to be implemented in an AUV, addressed as wireless charger housing (WCH), from the concept stage to the final physical verification and operation stage. The wireless charger system prepared in this research aims to improve the longevity of the vehicle mission, without having to return to the surface, by enabling battery charging at a docking station. This product was designed following a design for excellence (DfX) and modular design philosophy, implementing visual scorecards to measure the success of certain design aspects. For an adequate choice of materials, the Ashby method was implemented. The structural performance of the prototypes was validated via a linear static finite element analysis (FEA). These prototypes were further physically verified in a hyperbaric chamber. Results showed that the application of FEA, together with well-defined design goals, enable the WCH optimisation while ensuring up to 75% power efficiency. This methodology produced a system capable of transmitting energy for underwater robotic applications.

Abstract

Abstract
The expansion of autonomous driving operations requires the research and development of accurate and reliable self-localization approaches. These include visual odometry methods, in which accuracy is potentially superior to GNSS-based techniques while also working in signal-denied areas. This paper presents an in-depth review of state-of-the-art visual and point cloud odometry methods, along with a direct performance comparison of some of these techniques in the autonomous driving context. The evaluated methods include camera, LiDAR, and multi-modal approaches, featuring knowledge and learning-based algorithms, which are compared from a common perspective. This set is subject to a series of tests on road driving public datasets, from which the performance of these techniques is benchmarked and quantitatively measured. Furthermore, we closely discuss their effectiveness against challenging conditions such as pronounced lighting variations, open spaces, and the presence of dynamic objects in the scene. The research demonstrates increased accuracy in point cloud-based methods by surpassing visual techniques by roughly 33.14% in trajectory error. This survey also identifies a performance stagnation in state-of-the-art methodologies, especially in complex conditions. We also examine how multi-modal architectures can circumvent individual sensor limitations. This aligns with the benchmarking results, where the multi-modal algorithms exhibit greater consistency across all scenarios, outperforming the best LiDAR method (CT-ICP) by 5.68% in translational drift. Additionally, we address how current AI advances constitute a way to overcome the current development plateau.

Abstract
AbstractThe offshore wind power industry is an emerging and exponentially growing sector, which calls to a necessity for a cyclical monitoring and inspection to ensure the safety and efficiency of the wind farm facilities. Thus, the emersed (aerial) and immersed (underwater) scenarios must be reconstructed to create a more complete and reliable map that maximizes the observability of all the offshore structures from the wind turbines to the cable arrays, presenting a multi domain scenario.This work proposes the use of an Autonomous Surface Vehicle (ASV) to map both domains simultaneously. As such, it will produce a multi-domain map through the fusion of navigational sensors, GPS and IMU, to localize the vehicle and aid the registration process for the perception sensors, 3D Lidar and Multibeam echosounder sonar. The performed experiments demonstrate the ability of the multi-domain mapping architecture to provide an accurate reconstruction of both scenarios into a single representation using the odometry system as the initial seed to further improve the map with data filtering and registration processes. An error of 0.049 m for the odometry estimation is observed with the GPS/IMU fusion for simulated data and 0.07 m for real field tests. The multi-domain map methodology requires an average of 300 ms per iteration to reconstruct the environment, with an error of at most 0.042 m in simulation.

Abstract
In recent years, autonomous underwater vehicles (AUVs) have gained prominence in the most varied fields of application of underwater missions. The most common solution for recharging their batteries still implies removing them from the water, which is exceptionally costly. The use of Inductive Power Transfer (IPT) technologies allows to mitigate the associated costs and to extend the vehicles' operation time. In consequence, a prototype has been developed, whose objective is to integrate commercially available IPT technologies, while allowing the employment by most of the AUVs. The prototype is a funnel structure and its counterpart aimed to be fixed to a docking station and the AUV respectively. When coupled, it enables the batteries to recharge by electromagnetic induction. Energy transmission has been tested, resulting in encouraging results. This particular solution achieved over 90% efficiency during underwater experiments. The next objective will be to develop a commercial version of the prototype, that allows a direct, practical and effective use of wireless charging technologies within this particular scenario.