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.

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
The integration of precise landing capabilities into unmanned aerial vehicles (UAVs) is crucial for enabling autonomous operations, particularly in challenging environments such as the offshore scenarios. This work proposes a heterogeneous perception system that incorporates a multimodal fiducial marker, designed to improve the accuracy and robustness of autonomous landing of UAVs in both daytime and nighttime operations. This work presents ViTAL-TAPE, a visual transformer-based model, that enhance the detection reliability of the landing target and overcomes the changes in the illumination conditions and viewpoint positions, where traditional methods fail. VITAL-TAPE is an end-to-end model that combines multimodal perceptual information, including photometric and radiometric data, to detect landing targets defined by a fiducial marker with 6 degrees-of-freedom. Extensive experiments have proved the ability of VITAL-TAPE to detect fiducial markers with an error of 0.01 m. Moreover, experiments using the RAVEN UAV, designed to endure the challenging weather conditions of offshore scenarios, demonstrated that the autonomous landing technology proposed in this work achieved an accuracy up to 0.1 m. This research also presents the first successful autonomous operation of a UAV in a commercial offshore wind farm with floating foundations installed in the Atlantic Ocean. These experiments showcased the system's accuracy, resilience and robustness, resulting in a precise landing technology that extends mission capabilities of UAVs, enabling autonomous and Beyond Visual Line of Sight offshore operations.

Abstract
This chapter presents the use of Robotics in the Inspection and Maintenance of Offshore Wind as another highly challenging environment where autonomous robotics systems and digital transformations are proving high value. © 2024 Andry Maykol Pinto | Aníbal Matos | João V. Amorim Marques | Daniel Filipe Campos | Maria Inês Pereira | Rafael Claro | Eeva Mikola | João Formiga | Mohammed El Mobachi | Jaap-Jan Stoker | Jonathan Prevosto | Shashank Govindaraj | David Ribas | Pere Ridao | Luca Aceto.

Abstract

Abstract
This article presents a real-time trajectory optimizer for shore-to-ship operations using Unmanned Aerial Vehicles (UAVs). This concept aims to improve the efficiency of the transportation system by using UAVs to carry out parcel deliveries to offshore ships. During these operations, UAVs would fly relatively close to manned vessels, posing significant risks to the crew in the event of any incident. Additionally, in these areas, UAVs are exposed to meteorological phenomena such as wind gusts, which may compromise the stability of the flight and lead to potential collisions. Furthermore, this is a phenomenon difficult to predict, which poses a risk that must be considered in the operations. For these reasons, this work proposes a gust-aware multi-objective optimization solution for calculating fast and safe trajectories, considering the risk of flying in areas prone to the formation of intense gusts. Moreover, the system establishes a risk buffer with respect to all vessels to ensure compliance with EASA (European Union Aviation Safety Agency) regulations. For this purpose, Automatic Identification System (AIS) data are used to determine the position and velocity of the different vessels, and trajectory calculations are periodically updated based on their motion. The system computes the minimum-cost trajectory between the ground base and a moving destination ship while keeping these risk buffer constraints. The problem was solved through an Optimal Control formulation discretized on a dynamic graph with time-dependent costs and constraints. The solution was obtained using a Reaching Method that allowed efficient and real-time computations.