Eduardo Silva

Abstract
Teaching robotics based on challenge of our daily lives is always more motivating for students and teachers. Several competitions of self-driving have emerged recently, challenging students and researchers to develop solutions addressing the autonomous driving systems. The Portuguese Festival Nacional de Robótica (FNR) Autonomous Driving Competition is one of those examples. Even though the competition is an exciting challenger, it requires the development of real robots, which implies several limitations that may discourage the students and compromise a fluid teaching process. The simulation can contribute to overcome this limitation and can assume an important role as a tool, providing an effortless and costless solution, allowing students and researchers to keep their focus on the main issues. This paper presents a simulation environment for FNR, providing an overall framework able to support the exploration of robotics topics like perception, navigation, data fusion and deep learning based on the autonomous driving competition. © Springer Nature Switzerland AG 2020.

Abstract

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. © 2020, Springer Nature Switzerland AG.

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.