Abstract
The ATLANTIS project aims to establish a pioneer pilot infrastructure that will allow the demonstration of key enabling robotic technologies for inspection and maintenance of offshore wind farms. The pilot will be implemented in Viana do Castelo, Portugal, and will allow for testing, validation and demonstration of technologies with a range of technology readiness level, in near-real/real environments.The demonstration of robotic technologies can promote the transition from traditional inspection and maintenance methodologies towards automated robotic strategies, that remove or reduce the need of human-in-the-loop, reducing costs and improving the safety of interventions. Eight scenarios, split into four showcases, will be used to determine the required developments for robotic integration and demonstrate the applicability in the inspection and maintenance processes. The scenarios considered were identified by end-users as key areas for robotics.

Abstract
Recently, research concerning the navigation of Autonomous Surface Vehicles (ASVs) has been increasing. However, a big scale implementation of these vessels is still held back by a plethora of challenges such as multi-object tracking. This article presents the development of a tracking model through transfer learning techniques, based on referenced object trackers for urban scenarios. The work consisted in training a neural network through deep learning techniques, including data association and comparison of three different optimisers, Adadelta, Adam and SGD, determining the best hyper-parameters to maximise the training efficiency. The developed model achieved decent performance at tracking large vessels in the ocean, being successful even in harsh lighting conditions and lack of image focus.

Abstract
The worldwide context has fostered the innovation geared to the blue growth. However, the aquatic environment imposes many restrictions to mobile robots, as their perceptual capacity becomes severely limited. DIIUS aims to strengthen the perception of distributed robotic systems to improve the current procedures for inspection of aquatic structures (constructions and/or vessels).The perception of large working areas from multiples robots raises a number of unresolved inference problems and calls for new interaction patterns between multiple disciplines, both at the conceptual and technical level. To address this important challenge, the DIIUS project seeks to reinforce the current state-of-art in several scientific domains that fit into artificial intelligence, computer vision, and robotics. Through case studies focused on 3D mapping of aquatic structures (ex., maritime constructions and adduction tunnels), the project investigates new spatio-temporal data association techniques, including the correlation of sensors from heterogeneous robot formations operating in environments with communications constraints.

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This paper presents a quadratically constrained programming (QCP) model to tackle the optimal power flow (OPF) problem in distribution networks. The proposed model is fast, reliable, and precise enough to be embedded into the multi-emporal power system analysis. The proposed model benefits from a standard QCP to solve the branch active and reactive power flows. The second-order conic programming (SOCP) approach has been applied to address the quadratic constraints. The nonconvex feature of the OPF problem has been relaxed utilizing the McCormick envelopes. To find the minimum current of each branch, the lossless power flow model has been first solved and the obtained results have been considered for solving the OPF problem. The IEEE 33-bus test system has been selected as the benchmark to verify the efficient performance of the proposed OPF model. The simulation study confirms that the McCormick envelopes used in the QCP approach lead to precise results with a very fast convergence time. Overall, the presented model for the OPF can be extended for both planning and operation purposes in distribution system studies.