Abstract
This paper presents an autonomous robotic system, IRIS, designed for lost fishing gear recovery. The vehicle was developed in the context of the NetTag project. This is a European Union project funded by EASME the Executive Agency for Small and Medium Enterprises addressing marine litter, and the reduction of quantity and impact of lost fishing gears in the ocean. NetTag intends to produce new technological devices for location and recovery of fishing gear and educational material about marine litter, raise awareness of fisheries industry and other stakeholders about the urgent need to combat marine litter and increase scientific knowledge on marine litter problematic, guaranteeing the engagement of fishers to adopt better practices to reduce and prevent marine litter derived from fisheries. The design of IRIS is presented in detail, addressing the mechanical design, hardware architecture, sensor system and navigation and control. Preliminary tests in tank and in controlled sea conditions are presented and ongoing developments on the recovery system are discussed.

Abstract
A dense and accurate disparity map is relevant for a large number of applications, ranging from autonomous driving to robotic grasping. Recent developments in machine learning techniques enable us to bypass sensor limitations, such as low resolution, by using deep regression models to complete otherwise sparse representations of the 3D space. This article proposes two main approaches that use a single RGB image and sparse depth information gathered from a variety of sensors/techniques (stereo, LiDAR and Light Stripe Ranging (LSR)): a Convolutional Neural Network (CNN) and a cascade architecture, that aims to improve the results of the first. Ablation studies were conducted to infer the impact of these depth cues on the performance of each model. The models trained with LiDAR sparse information are the most reliable, achieving an average Root Mean Squared Error (RMSE) of 11.8 cm on our own Inhouse dataset; while the LSR proved to be too sparse of an input to compute accurate predictions on its own. © Springer Nature Switzerland AG 2020.

Abstract
The use of robotic solutions in tasks such as the inspection and monitorization of offshore wind farms aims to, not only mitigate the involved risks, but also to reduce the costs of operating and maintaining these structures. Performing a complete inspection of the platforms in useful time is crucial. Therefore, multiple agents can prove to be a cost-effective solution. This work proposes a trajectory planning algorithm, based on the Ant Colony metaheuristic, capable of optimizing the number of Autonomous Surface Vehicles (ASVs) to be used, and their corresponding route. Experiments conducted on a simulated environment, representative of the real scenario, proves this approach to be successful in planning a trajectory that is able to select the appropriate number of agents and the trajectory of each agent that avoids collisions and at the same time guarantees the full observation of the offshore structures.

Abstract
In recent years, research concerning the operation of Autonomous Surface Vehicles (ASVs) has seen an upward trend, although the full-scale application of this type of vehicles still encounters diverse limitations. In particular, the docking and undocking processes of an ASV are tasks that currently require human intervention. Aiming to take one step further towards enabling a vessel to dock autonomously, this article presents a Deep Learning approach to detect a docking structure in the environment surrounding the vessel. The work also included the acquisition of a dataset composed of LiDAR scans and RGB images, along with IMU and GPS information, obtained in simulation. The developed network achieved an accuracy of 95.99%, being robust to several degrees of Gaussian noise, with an average accuracy of 9334% and a deviation of 5.46% for the worst case.

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
<p>The UNEXUP project, funded under EIT Raw Materials, is a direct continuation of the Horizon 2020 UNEXMIN project. While in UNEXMIN efforts were made towards the design, development and testing of an innovative exploration technology for underground flooded mines, in UNEXUP the main goal is to push the UNEXMIN technology into the market, while further improving the system’s hardware, software and capabilities. In parallel, the aim is to make a strong business case for the improved UNEXUP technology, as a result of tests and data collection from previous testing. Improvements to the UX-1 research prototypes will raise technology readiness levels from TRL 6, as verified at the end of the UNEXMIN project, to TRL 7/8 by 2022. A "real service-to-real client" approach will be demonstrated, supporting mineral exploration and mine surveying efforts in Europe with unique data from flooded environments that cannot be obtained without high costs, or risks to human lives, in any other ways.</p><p>The specific purpose of UNEXUP is to commercially deploy a new raw materials exploration / mine mapping service based on a new class of mine explorer robots, for non-invasive resurveying of flooded mines. The inaccessibility of the environment makes autonomy a critical and primary objective of the project, which will present a substantial effort in resurveying mineral deposits in Europe where the major challenges are the geological uncertainty, and technological / economic feasibility of mine development. The robot’s ability to gather high-quality and high-resolution information from currently inaccessible mine sites will increase the knowledge of mineral deposits in Europe, whilst decreasing exploration costs – such as the number of deep exploration drillholes needed. This can potentially become a game changing technology in the mining panorama, where the struggle for resources is ever increasing.</p><p>On the technical side, a fourth robot, modular in nature, will be added to the current multi-robot platform, providing additional functionalities to the exploration system, including better range and depth performance. Hardware and software upgrades, as well as new capabilities delivered by the platform will greatly extend the usefulness of the platform in different environments and applications. Among these: rock sampling, better data acquisition and management, further downsizing, extended range, improved self-awareness and decision making, mature post-processing (such as the deployment of 3D virtual reality models), ability to rescue other robots, and interaction with the data will be targeted during the next years. Upgrading the overall technology with these tools, and possibly additional ones, will allow the system to operate with more reliability and security, with reduced costs.</p><p>These added functions arise from different stakeholders’ feedbacks from the UNEXMIN project. UNEXUP targets parties from the mining, robotics and mineral exploration sectors, as well as all other sectors that have any kind of underwater structure that needs to be surveyed – caves, underground reservoirs, water pipelines and fisheries are among them. For the purpose of exploitation of the technology, a joint company was founded, “UNEXMIN GeoRobotics Ltd”, which is part of the UNEXUP consortium, and is responsible for selling the service to the market.</p>