Pedro Nuno Almeida

Abstract
This work presents a bio-inspired Autonomous Underwater Vehicle (AUV) concept called Raya that enables high manoeuvrability required for close-range inspection and intervention tasks, while fostering endurance for long-range operations by enabling efficient navigation. The AUV has an estimated terminal velocity of 0.82 m/s in an optimal environment, and a capacity to acquire visual data and sonar measurements in all directions. Raya was designed with the potential to incorporate an electric manipulator arm of 6 degrees of freedom (DoF) for free-floating underwater intervention. Smart and biologically inspired principles applied to morphology and a strategic thruster configuration assure that Raya is capable of manoeuvring in all 6 DoFs even when equipped with a manipulator with a 5 kg payload. Extensive experiments were conducted using simulation tools and real-life environments to validate Raya's requirements and functionalities. The stresses and displacements of the rigid bodies were analysed using finite element analysis (FEA), and an estimation of the terminal forward velocity was achieved using a dynamic model. To assess the accuracy of the perception system, a reconstruction task took place in an indoor pool, resulting in a 3D reconstruction with average length, width, and depth errors below 1. 5%. The deployment of Raya in the ATLANTIS Coastal Testbed and Porto de Leix & otilde;es allowed the validation of the propulsion system and the gathering of valuable 2D and 3D data, thus proving the suitability of the vehicle for operation and maintenance (O&M) activities of underwater structures.

Abstract
Due to the difficult access to the maritime environment, cooperation between different robotic platforms operating in different domains provides numerous advantages when considering Operations and Maintenance (O&M) missions. The nest Uncrewed Surface Vehicle (USV) is equipped with a parallel platform, serving as a landing pad for Uncrewed Aerial Vehicle (UAV) landings in dynamic sea states. This work proposes a methodology for short term forecasting of wave-behaviour using Fast Fourier Transforms (FFT) and a low-pass Butterworth filter to filter out noise readings from the Inertial Measurement Unit (IMU) and applying an Auto-Regressive (AR) model for the forecast, showing good results within an almost 10-second window. These predictions are then used in a Model Predictive Control (MPC) approach to optimize trajectory planning of the landing pad roll and pitch, in order to increase horizontality, consistently mitigating around 80% of the wave induced motion.

Abstract
Offshore wind farms face harsh maritime conditions, prompting the use of sacrificial anodes to prevent rapid structural degradation. Regular maintenance and replacement of these elements are vital to ensure ongoing corrosion protection, maintain structural integrity, and optimize efficiency. This article details the design and validation of the MARESye hybrid underwater imaging system, capable of retrieving heterogeneous tri-dimensional information with millimetric precision for the close-range inspection of submerged critical structures. The optical prowess of the system is first validated during low turbidity trials where the volumetric properties of a decommissioned anode are reconstructed with absolute errors down to 0.0008 m, and its spatial dimensions are depicted with sub-millimeter precision accounting for relative errors as low as 0.31%. MARESye is later equipped as payload in a commercial ROV during areal environment inspection mission at the ATLANTIS Coastal Test Center. This experiment sees the sensor provide live reconstructions of a sacrificial anode, revealing a biofouling layer of approximately 0.0130 m thickness. The assessment of the high-fidelity 2D/3D information obtained from the MARESye sensor demonstrates its potential to enhance the situational awareness of underwater vehicles, fostering reliable O&M procedures.

Abstract
Accurate dynamics modelling of Unmanned Under-water Vehicles (UUV s) is critical for optimizing mission planning, minimizing collision risks, and ensuring the successful execution of tasks in diverse underwater environments. This paper presents a structured approach to estimating the hydrodynamic coeffi-cients of UUV s. Initially, it follows a detailed methodology for estimating hydrodynamic coefficients using simple geometries, a sphere and a spheroid, using the Computational Fluid Dy-namics (CFD) software OpenFoam, and comparing the results to analytical solutions, enabling the validation of the simulations approach. Following this, the paper provides an in-depth analysis of the damping and added mass coefficients for the Raya UUV, offering valuable insights into its hydrodynamic behaviour. © 2024 IEEE.

Abstract
The underwater domain presents a myriad of challenges for perception systems that must be overcome to achieve accurate object detection and recognition. To augment the performance and safety of existing solutions for intricate O&M (Operations and Maintenance) procedures, AUVs must perceive the surroundings and locate potential objects of interest based on the perceived information. A depth gradient methodology is employed to survey the seabed using a multibeam sonar to perform a coarse reconstruction of the scenario that it later used to locate and identify foreign objects. This could include rocks, debris, wreckage, or other objects that may pose potential exploratory interest. First results show that the proposed method was able to detect 100 % of the objects present in the scenario with an average chamfer distance error of 0.0238m between models and respective reconstruction. © 2024 IEEE.