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
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
With the expansion of offshore infrastructure, the necessity for efficient Operation and Maintenance (O&M) procedures intensifies. This article introduces DADDI, a multimodal dataset obtained from a real offshore floating structure, aimed at facilitating comprehensive inspections and 3D model creation. Leveraging Unmanned Aerial Vehicles (UAVs) equipped with advanced sensors, DADDI provides synchronized data, including visual images, thermal images, point clouds, GNSS, IMU, and odometry data. The dataset, gathered during a campaign at the ATLANTIS Coastal Testbed, offers over 2500 samples of each data type, along with intrinsic and extrinsic sensor calibrations. DADDI serves as a vital resource for the development and evaluation of algorithms, models, and technologies tailored to the inspection, monitoring, and maintenance of complex maritime structures. © 2024 IEEE.

Abstract
Over the last few years, Man-Machine collaborative systems have been increasingly present in daily routines. In these systems, one operator usually controls the machine through explicit commands and assesses the information through a graphical user interface. Direct & implicit interaction between the machine and the user does not exist. This work presents a man-machine symbiotic concept & system where such implicit interaction is possible targeting search and rescue scenarios. Based on measuring physiological variables (e.g. body movement or electrocardiogram) through wearable devices, this system is capable of computing the psycho-physiological state of the human and autonomously identify abnormal situations (e.g. fall or stress). This information is injected into the control loop of the machine that can alter its behavior according to it, enabling an implicit man-machine communication mechanism. A proof of concept of this system was tested at the ARTEX (ARmy Technological EXperimentation) exercise organized by the Portuguese Army involving a military agent and a drone. During this event the soldier was equipped with a kit of wearables that could monitor several physiological variables and automatically detect a fall during a mission. This information was continuously sent to the drone that successfully identified this abnormal situation triggering the take-off and a situation awareness fly-by flight pattern, delivering a first-aid kit to the soldier in case he did not recover after a pre-determined time period. The results were very positive, proving the possibility and feasibility of a symbiotic system between humans and machines.

Abstract
Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images.

Abstract
The deployment of offshore wind turbines (WTs) has emerged as a pivotal strategy in the transition to renewable energy, offering significant potential for clean electricity generation. However, these structures' operation and maintenance (O&M) present unique challenges due to their remote locations and harsh marine environments. For these reasons, it is fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper explores the application of Unmanned Aerial Vehicles (UAVs) in the inspection and maintenance of offshore wind turbines, introducing a new strategy for autonomous wind turbine inspection and a simulation environment for testing and training autonomous inspection techniques under a more realistic offshore scenario. Instead of relying on visual information to detect the WT parts during the inspection, this method proposes a three-dimensional (3D) light detection and ranging (LiDAR) method that estimates the wind turbine pose (position, orientation, and blade configuration) and autonomously controls the UAV for a close inspection maneuver. The first tests were carried out mainly in a simulation framework, combining different WT poses, including different orientations, blade positions, and wind turbine movements, and finally, a mixed reality test, where a real vehicle performed a full inspection of a virtual wind turbine.