Abstract
Purpose This paper aims to present a mosaicking method for underwater robotic applications, whose result can be provided to other perceptual systems for scene understanding such as real-time object recognition. Design/methodology/approach This method is called robust and large-scale mosaicking (ROLAMOS) and presents an efficient frame-to-frame motion estimation with outlier removal and consistency checking that maps large visual areas in high resolution. The visual mosaic of the sea-floor is created on-the-fly by a robust registration procedure that composes monocular observations and manages the computational resources. Moreover, the registration process of ROLAMOS aligns the observation to the existing mosaic. Findings A comprehensive set of experiments compares the performance of ROLAMOS to other similar approaches, using both data sets (publicly available) and live data obtained by a ROV operating in real scenes. The results demonstrate that ROLAMOS is adequate for mapping of sea-floor scenarios as it provides accurate information from the seabed, which is of extreme importance for autonomous robots surveying the environment that does not rely on specialized computers. Originality/value The ROLAMOS is suitable for robotic applications that require an online, robust and effective technique to reconstruct the underwater environment from only visual information.

Abstract
Reinforcement learning algorithms are now more appealing than ever. Recent approaches bring power and tuning simplicity to the everyday work machine. The possibilities are endless, and the idea of automating learning without domain knowledge is quite tempting for many researchers. However, in competitive environments such as the RoboCup 3D Soccer Simulation League, there is a lot to be done regarding humanlike behaviors. Current teams use many mechanical movements to perform basic skills, such as running and dribbling the ball. This paper aims to use the PPO algorithm to optimize those skills, achieving natural gaits without sacrificing performance. We use Simspark to simulate a NAO humanoid robot, using visual and body sensors to control its actuators. Based on our results, we propose an indirect control approach and detailed parameter setups to obtain natural running and dribbling behaviors. The obtained performance is in some cases comparable or better than the top RoboCup teams. However, some skills are not ready to be applied in competitive environments yet, due to instability. This work contributes towards the improvement of RoboCup and some related technical challenges.

Abstract

Abstract
Across the world, several developing countries with recent wind energy (WE) diffusion have been demonstrating an interest in expanding WE generation quickly and expressively. In Brazil, this fast and remarkable WE diffusion process started in the past decade, facing a variety of barriers. This research proposes to reliably identify and analyse current barriers to the implementation of onshore wind farms in the country, aiming at informing other developing countries with the intention to expand WE utilization about potential barriers, as well as assisting the Brazilian WE sector in its efforts to keep growing sustainably. To achieve this research goal, 41 key stakeholders from the Brazilian WE sector were interviewed. Based on a content analysis of the interviews, twenty-four barriers where identified, three of which with particular relevance: poor transmission infrastructure, unattractive financial loans and unstable macroeconomic environment. The data collected suggests a serious risk that Brazil's WE sector stops growing if the recovery of its national economy, the reestablishment of regular energy auctions, the access to competitive credit, and improvements to the transmission system infrastructure keep being held back. In the face of this scenario, this study concludes by providing policy recommendations concerning the sustainability of the Brazilian WE sector.

Abstract
Under the VitalResponder® (VR) line of research, mostly funded by the Carnegie Mellon University (CMU)-Portugal program, we have been developing, in partnership with colleagues from CMU, novel wearable monitoring solutions for hazardous professionals such as first responders (FR). We are exploring the synergy between innovative wearable technologies, scattered sensor network and precise localization to provide secure, reliable and effective first-response information services in emergency scenarios. This enables a thorough teams’management, namely on FR exposure to different hazardous elements, effort levels and critical situations that contribute to team members’ stress and fatigue levels. © The Institution of Engineering and Technology 2017.

Abstract
The continuous proliferation of distributed energy resources (DER), mainly from renewable energy sources (RES) is changing the operational planning of distribution grids. Microgrids (MGs) as a small part of distribution grids are characterized by their ability to partially/fully self-producing their energy needs, and for the ability to trade different energy products (e.g. energy and reserve). This paper, addresses the energy and reserve market problem within the MG environment considering the RES uncertain production. Thus, a two-stage stochastic programming was modelled, minimizing the energy and reserve costs of the MG operator. A DC Optimal Power Flow (OPF) was incorporated to mitigate potential congestion that may occur in the MG. The assessment of the model is carried out through a test case based on actual generation data, considering a 37-bus distribution grid. The performance and accuracy of the model is determined based on the expected value of perfect information (EVPI) and value of stochastic solution (VSS).