Abstract
The big growth of electrical demand by the countries required larger and more complex power systems, which have led to a greater need for monitoring and maintenance of these systems. To overcome this problem, UAVs equipped with appropriated sensors have emerged, allowing the reduction of the costs and risks when compared with traditional methods. The development of UAVs together with the great advance of the deep learning technologies, more precisely in the detection of objects, allowed to increase the level of automation in the process of inspection. This work presents an electrical assets monitoring system for detection of insulators and structures (poles and pylons) from images captured through a UAV. The proposed detection system is based on lightweight Convolutional Neural Networks and it is able to run on a portable device, aiming for a low cost, accurate and modular system, capable of running in real time.

Abstract
The global navigation satellite system (GNSS) constitutes an effective and affordable solution to the outdoor positioning problem. When combined with precise positioning techniques, such as the real time kinematic (RTK), centimeter-level positioning accuracy becomes a reality. Such performance is suitable for a whole new range of demanding applications, including high-accuracy field robotics operations. The RTKRCV, part of the RTKLIB package, is one of the most popular open-source solutions for real-time GNSS precise positioning. Yet the lack of integration with the robot operating system (ROS), constitutes a limitation on its adoption by the robotics community. This article addresses this limitation, reporting a new implementation which brings the RTKRCV capabilities into ROS. New features, including ROS publishing and control over a ROS service, were introduced seamlessly, to ensure full compatibility with all original options. Additionally, a new observation synchronization scheme improves solution consistency, particularly relevant for the moving-baseline positioning mode. Real application examples are presented to demonstrate the advantages of our rtkrcv_ros package. For community benefit, the software was released as an open-source package.

Abstract
Emerging opportunities in the exploration of inland water bodies, such as underwater mining of flooded open pit mines, require accurate real-time positioning of multiple underwater assets. In the mining operation scenarios, operational requirements deny the application of standard acoustic positioning techniques, posing additional challenges to the localization problem. This paper presents a novel underwater localization solution, implemented for the VAMOS! project, based on the combination of raw measurements from a short baseline (SBL) array and an inverted ultrashort baseline (iUSBL). An extended Kalman filter (EKF), fusing IMU raw measurements, pressure observations, SBL ranges, and USBL directional angles, estimates the localization of an underwater mining vehicle in 6DOF. Sensor bias and the speed of sound in the water are estimated indirectly by the filter. Moreover, in order to discard acoustic outliers, due to multipath reflections in such a confined and cluttered space, a data association layer and a dynamic SBL master selection heuristic were implemented. To demonstrate the advantage of this new technique, results obtained in the field, during the VAMOS! underwater mining field trials, are presented and discussed.

Abstract
This paper addresses Three-Dimensional (3D) reconstruction of historical sites with an Unmanned Aerial Vehicle (UAV), combining the information from a visible spectrum camera with a Light Detection and Ranging (LiDAR). The developed solution was validated in two sites located in Monastery of Tibães (Braga, NW Portugal), within the scope of MineHeritage project, which intends to reach society on the importance of raw materials through a historical approach. The outputs obtained from the datasets, resulted in a successfully 3D reconstruction of the two studied sites on the Monastery. Although the research is still ongoing on this topic, this paper is a starting point and an important contribution to this field and this type of scenarios. © CLAWAR Association Ltd.

Abstract
This paper presents an innovative modular autonomous underwater vehicle (MARA) developed for the exploration of underwater confined spaces such as underwater caves, flooded underground mines or complex tight infrastructures in underwater environments. The particular mission scenario of exploration of flooded underground mines was used as a key driver for the robot development. The autonomous underwater vehicle is described from the mechanical, hardware and software points of view. The availability of the INESC TEC underwater systems test tank and access conditions to Porto harbour and the Urgeirica mine allows for easy robot field validation. Preliminary results are also presented and discussed.

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.