Abstract
The automation of typically intelligent and decision-making processes in the maritime industry leads to fewer accidents and more cost-effective operations. However, there are still lots of challenges to solve until fully autonomous systems can be employed. Artificial Intelligence (AI) has played a major role in this paradigm shift and shows great potential for solving some of these challenges, such as the docking process of an autonomous vessel. This work proposes a lightweight volumetric Convolutional Neural Network (vCNN) capable of recognizing different docking-based structures using 3D data in real-time. A synthetic-to-real domain adaptation approach is also proposed to accelerate the training process of the vCNN. This approach makes it possible to greatly decrease the cost of data acquisition and the need for advanced computational resources. Extensive experiments demonstrate an accuracy of over 90% in the recognition of different docking structures, using low resolution sensors. The inference time of the system was about 120ms on average. Results obtained using a real Autonomous Surface Vehicle (ASV) demonstrated that the vCNN trained with the synthetic-to-real domain adaptation approach is suitable for maritime mobile robots. This novel AI recognition method, combined with the utilization of 3D data, contributes to an increased robustness of the docking process regarding environmental constraints, such as rain and fog, as well as insufficient lighting in nighttime operations.

Abstract
This paper presents an algorithm for mapping monopiles from Offshore Wind Farms (OWF). The ASV (Autonomous Surface Vehicle) surveys the environment, detects and localizes monopiles using situational awareness system based on LiDAR, GPS and IMU (Inertial Measurement Unit) data. The position of the monopile is obtained based on the relative localization between the extrapolated center of the structure that was detected and the ASV. A positive detection of a monopile is referenced to a global positioning frame based on the GPS. Results in a simulator environment demonstrate the ability of this situational awareness system to identify monopiles with a precision of 0.005 m, which is relevant for detecting structural disalignments over time that might be caused by the appearance of scour in the structure's foundation.

Abstract
For Vertical Take-Off and Landing Unmanned Aerial Vehicles (VTOL UAVs) to operate autonomously and effectively, it is mandatory to endow them with precise landing abilities. The UAV has to be able to detect the landing target and to perform the landing maneuver without compromising its own safety and the integrity of its surroundings. However, current UAVs do not present the required robustness and reliability for precise landing in highly demanding scenarios, particularly due to their inadequacy to perform accordingly under challenging lighting and weather conditions, including in day and night operations.This work proposes a multimodal fiducial marker, named ArTuga (Augmented Reality Tag for Unmanned vision-Guided Aircraft), capable of being detected by an heterogeneous perception system for accurate and precise landing in challenging environments and daylight conditions. This research combines photometric and radiometric information by proposing a real-time multimodal fusion technique that ensures a robust and reliable detection of the landing target in severe environments.Experimental results using a real multicopter UAV show that the system was able to detect the proposed marker in adverse conditions (such as at different heights, with intense sunlight and in dark environments). The obtained average accuracy for position estimation at 1 m height was of 0.0060 m with a standard deviation of 0.0003 m. Precise landing tests obtained an average deviation of 0.027 m from the proposed marker, with a standard deviation of 0.026 m. These results demonstrate the relevance of the proposed system for the precise landing in adverse conditions, such as in day and night operations with harsh weather conditions.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Abstract
A perception module is a vital component of a modern robotic system. Vision, radar, thermal, and LiDAR are the most common choices of sensors for environmental awareness. Relying on singular sources of information is prone to be affected by specific environmental conditions (e.g., visual cameras are affected by glary or dark environments). Thus, relying on different sensors is an essential step to introduce robustness against various environmental conditions. Hence, a perception system with sensor fusion capabilities produces the desired redundant and reliable awareness critical for real-world systems. This paper proposes a novel early fusion module that is reliable against individual cases of sensor failure when detecting an offshore maritime platform for UAV landing. The model explores the early fusion of a still unexplored combination of visual, infrared, and LiDAR modalities. The contribution is described by suggesting a simple methodology that intends to facilitate the training and inference of a lightweight state-of-the-art object detector. The early fusion based detector achieves solid detection recalls up to 99% for all cases of sensor failure and extreme weather conditions such as glary, dark, and foggy scenarios in fair real-time inference duration below 6 ms.

Abstract
The use of Unmanned Aerial Vehicles (UAVs) in different inspection tasks is increasing. This technology reduces inspection costs and collects high quality data of distinct structures, including areas that are not easily accessible by human operators. However, the reduced energy available on the UAVs limits their flight endurance. To increase the autonomy of a single flight, it is important to optimize the path to be performed by the UAV, in terms of energy loss. Therefore, this work presents a novel formulation of the Travelling Salesman Problem (TSP) and a path planning algorithm that uses a UAV energy model to solve this optimization problem. The novel TSP formulation is defined as Asymmetric Travelling Salesman Problem with Precedence Loss (ATSP-PL), where the cost of moving the UAV depends on the previous position. The energy model relates each UAV movement with its energy consumption, while the path planning algorithm is focused on minimizing the energy loss of the UAV, ensuring that the structure is fully covered. The developed algorithm was tested in both simulated and real scenarios. The simulated experiments were performed with realistic models of wind turbines and a UAV, whereas the real experiments were performed with a real UAV and an illumination tower. The inspection paths generated presented improvements over 24% and 8%, when compared with other methods, for the simulated and real experiments, respectively, optimizing the energy consumption of the UAV.

Abstract
Given the increased interest in offshore wind energy, there is a greater need for advancements in operation and maintenance technology. As a result, robotic solutions are required to avoid human risky behavior and reduce associated operational costs. In order to accommodate the need for inspecting multiple domains, multiple robotic vehicles are utilized, which requires the deployment of control stations that can effectively monitor, facilitate communication among different vehicles, and ensure successful completion of the overall mission. A shore control centre (SCC) is a communication software infrastructure capable of monitoring, localizing and planning missions for a group of multi-domain heterogeneous robots within a local network. This paper proposes an SCC as: (i) an active monitor by continuously observing the local behaviour of each robot and the global progress of the mission and its safety; (ii) a mission planner that provides and supervises its execution while constantly checking for critical failures and intervening in the case of unexpected events. Also, The control centre is able to connect to multiple vehicles from various domains and monitor real-time data. Accordingly, validation procedures were carried out in real conditions.