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.

Abstract
The demonstration of robotic technologies in real environments is essential for technology developers and end-users to fully showcase the benefits of theirs solutions, and contributes to the promotion of the transition of inspection and maintenance methodologies towards automated robotic strategies. However, before allowing technologies to be demonstrated in real, operating offshore wind-farms, there is a need to de-risk the technology, to ensure its safe operation offshore. As part of the ATLANTIS project, a pioneer pilot infrastructure, the ATLANTIS Test Centre, was installed in Viana do Castelo, Portugal. This infrastructure will allow the demonstration of key enabling robotic technologies for offshore inspection and maintenance. The Test Centre is composed of two distinct testbeds, and a supervisory control centre, enabling the de-risking, testing, validation and demonstration of technologies, in both near-real and real environments. This paper presents the details of the Coastal Testbed of the ATLANTIS Test Centre, from implementation to available resources and infrastructures and environment details.

Abstract
Abstract Recently, research concerning the navigation of autonomous surface vehicles (ASVs) has been increasing. However, a large-scale implementation of these vessels is still held back by several challenges such as multi-object tracking. Attaining accurate object detection plays a big role in achieving successful tracking. This article presents the development of a detection model with an image-based Convolutional Neural Network trained through transfer learning, a deep learning technique. To train, test, and validate the detector module, data were collected with the SENSE ASV by sailing through two nearby ports, Leixões and Viana do Castelo, and recording video frames through its on-board cameras, along with a Light Detection And Ranging, GPS, and Inertial Measurement Unit data. Images were extracted from the collected data, composing a manually annotated dataset with nine classes of different vessels, along with data from other open-source maritime datasets. The developed model achieved a class mAP@[.5 .95] (mean average precision) of 89.5% and a clear improvement in boat detection compared to a multi-purposed state-of-the-art detector, YOLO-v4, with a 22.9% and 44.3% increase in the mAP with an Intersection over Union threshold of 50% and the mAP@[.5 .95], respectively. It was integrated in a detection and tracking system, being able to continuously detect nearby vessels and provide sufficient information for simple navigation tasks.

Abstract
Precise construction progress monitoring has been shown to be an essential step towards the successful management of a building project. However, the methods for automated construction progress monitoring proposed in previous work have certain limitations because of inefficient and unrobust point cloud processing. The main objective of this research was to develop an accurate automated method for construction progress monitoring using a 4D BIM together with a 3D point cloud obtained using a terrestrial laser scanner. The proposed method consists of four phases: point cloud simplification, alignment of the as-built data with the as-planned model, classification of the as-built data according to the BIM elements, and estimation of the progress. The accuracy and robustness of the proposed methodology was validated using a known dataset. The developed application can be used for construction progress visualization and analysis. © 2023 ITMA.

Abstract
The Reinforcement Learning (RL) paradigm is showing promising results as a generic purpose framework for solving decision-making problems (e.g., robotics, games, finance). The aim of this work is to reduce the learning barriers and inspire young students, researchers and educators to use RL as an obvious tool to solve robotics problems. This paper provides an intelligible step-by-step RL problem formulation and the availability of an easy-to-use interactive simulator for students at various levels (e.g., undergraduate, bachelor, master, doctorate), researchers and educators. The interactive tool facilitates the familiarization with the key concepts of RL, its problem formulation and implementation. In this work, RL is used for solving a robotics 2D navigational problem where the robot needs to avoid collisions with obstacles while aiming to reach a goal point. A navigational problem is simple and convenient for educational purposes, since the outcome is unambiguous (e.g., the goal is reached or not, a collision happened or not). Due to a lack of open-source graphical interactive simulators concerning the field of RL, this paper combines theoretical exposition with an accessible practical tool to facilitate the apprehension. The results demonstrated are produced by a Python script that is released as open-source to reduce the learning barriers in such innovative research topic in robotics.

Abstract
This paper presents a visual localization approach that is suitable for domestic and industrial environments as it enables accurate, reliable and robust pose estimation. The mobile robot is equipped with a single camera which update sits pose whenever a landmark is available on the field of view. The innovation presented by this research focuses on the artificial landmark system which has the ability to detect the presence of the robot, since both entities communicate with each other using an infrared signal protocol modulated in frequency. Besides this communication capability, each landmark has several high intensity light-emitting diodes (LEDs) that shine only for some instances according to the communication, which makes it possible for the camera shutter and the blinking of the LEDs to synchronize. This synchronization increases the system tolerance concerning changes in brightness in the ambient lights over time, independently of the landmarks location. Therefore, the environment's ceiling is populated with several landmarks and an Extended Kalman Filter is used to combine the dead-reckoning and landmark information. This increases the flexibility of the system by reducing the number of landmarks required. The experimental evaluation was conducted in a real indoor environment with an autonomous wheelchair prototype.