Abstract
The growing dependence of modern-day societies on electricity increases the importance of effective monitoring and maintenance of power lines. Endowing UAVs with the appropriate sensors for inspecting power lines, the costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced. However, this implies the development of algorithms to make the inspection process reliable and autonomous. Visual methods are usually applied to locate the power lines and their components, but poor light conditions or a background rich in edges may compromise their results. To overcome those limitations, we propose to address the problem of power line detection and modeling based on LiDAR. A novel approach to the power line detection was developed, the PL2DM -Power Line LiDAR-based Detection and Modeling. It is based in a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. The algorithm was validated with a real dataset, showing promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.

Abstract
The overall aim of the ROSM project is the implementation of an innovative solution based on heterogeneous autonomous vehicles to tackle maritime pollution (in particular, oil spills). These solutions will be based on native microbial consortia with bioremediation capacity, and the adaptation of air and surface autonomous vehicles for in-situ release of autochthonous microorganisms (bioaugmentation) and nutrients (biostimulation). By doing so, these systems can be used as the first line of the responder to pollution incidents from several origins that may occur inside ports, around industrial and extraction facilities, or during transport activities, in a fast, efficient and low-cost way. The paper will address the development of a team of autonomous vehicles able to carry, as payload, native organisms to naturally degrade oil spills (avoiding the introduction of additional chemical or biological additives), the development of a multi-robot system able to provide a first line responses to oil spill incidents under unfavourable and harsh conditions with low human intervention, and then a decentralized cooperative planning with the ability to coordinate an efficient oil spill combat. Field tests have been performed in Leixoes Harbour in Porto and Medas, Portugal, with a simulated oil spill and validated the decentralized coordinated task between the autonomous surface vehicle (ASV) ROAZ and the unmanned aerial vehicle (UAV).

Abstract
This work addresses the use of hyperspectral imaging systems for remote detection of marine litter concentrations in oceanic environments. The work consisted on mounting an off-the-shelf hyperspectral imaging system (400-2500 nm) in two aerial platforms: manned and unmanned, and performing data acquisition to develop AI methods capable of detecting marine litter concentrations at the water surface. We performed the campaigns at Porto Pim Bay, Fail Island, Azores, resorting to artificial targets built using marine litter samples. During this work, we also developed a Convolutional Neural Network (CNN-3D), using spatial and spectral information to evaluate deep learning methods to detect marine litter in an automated manner. Results show over 84% overall accuracy (OA) in the detection and classification of the different types of marine litter samples present in the artificial targets.

Abstract
Offshore wind turbine application has been widespread in the last years, with an estimation that in 2030 will reach a total capacity of 234GW. Offshore wind farms introduce advantages in terms of environmental impact (noise, impact on birds, disrupted landscapes) and energy production (34% onshore and 43% offshore). Still, they also introduce scientific challenges in developing methodologies that allow wind farm inspection (preventive maintenance) safety for humans. This paper presents a UAV approach for autonomous inspection of inland windturbine and describes the field tests in Penela, Portugal. From the state-of-the-art available wind turbine inspection, in 2015, we carried out the first autonomous inspection with a UAV. The inspection of wind blades offshore is an ongoing project; therefore, the paper also presents the preliminary results with a simulation environment to validate the 3D LiDAR and the inspection procedure with new challenges effects: floating platform, wind gusts, and unknown initial blade position.

Abstract

Abstract
Offshore wind farms are becoming the main alternative to fossil fuels and the future key to mitigating climate change by achieving energy sustainability. With favorable indicators in almost every environmental index, these structures operate under varying and dynamic environmental conditions, leading to efficiency losses and sudden failures. For these reasons, it's fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper introduces a new simulation environment for testing and training autonomous inspection techniques under a more realistic offshore wind farm scenario. Combining the Gazebo simulator with ROS, this framework can include multi-robots with different sensors to operate in a customizable simulation environment regarding some external elements (fog, wind, buoyancy...). The paper also presents a use case composed of a 3D LiDAR-based technique for autonomous wind turbine inspection with UAV, including point cloud clustering, model estimation, and the preliminary results under this simulation framework using a mixed environment (offshore simulation with a real UAV platform).