André Dias

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
This paper presents the results from search and rescue missions performed with the aerial robot OTUS in the the context of the ISEP/INESC TEC aerial robotics team participation on the euRathlon 2015 robotics competition. The multi-domain (land, sea and air) search and rescue scenario is described and technical solution adopted is presented with emphasis on the perception system. The calibration of the image based system is addressed. Results from the operational missions performed are also discussed. The aerial autonomous vehicle was able to successfully perform multiple tasks from the aerial reconnaissance and 3D mapping to the identification of leaking pipes, obstructed passages and missing workers. The system was validated a realistic operational scenario and won the Grand Challenge in cooperation with land and marine robotics partner teams. This challenge was the first time that a real time collaborative team of aerial, land and marine robots was deployed successfully in a search and rescue mission. © 2018 Springer Science+Business Media B.V., part of Springer Nature

Abstract
The effective monitoring and maintenance of power lines are becoming increasingly important due to a global growing dependence on electricity. The costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced by using UAVs with the appropriate sensors. However, this implies developing algorithms to make the power line inspection process reliable and autonomous. In order to overcome the limitations of visual methods in the presence of poor light and noisy backgrounds, we propose to address the problem of power line detection and modeling based on LiDAR. The PL 2 DM, Power Line LiDAR-based Detection and Modeling, is a novel approach to detect power lines. Its basis is 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. Using a real dataset, the algorithm showed promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.

Abstract
The importance of planktonic microbial communities is well acknowledged, since they are fundamental for several natural processes of aquatic ecosystems. Microorganisms naturally control the flux of nutrients, and also degrade and recycle anthropogenic organic and inorganic contaminants. Nevertheless, climate change effects and/or the runoff of nutrients/ pollutants can affect the equilibrium of natural microbial communities influencing the occurrence of microbial pathogens and/or microbial toxin producers, which can compromise ecosystem environmental status. Therefore, improved microbial plankton monitoring is essential to better understand how these communities respond to environmental shifts. The study of marine microbial communities typically involves highly cost and time-consuming sampling procedures, which can limit the frequency of sampling and data availability. In this context, we developed and validated an in situ autonomous biosampler (IS-ABS) able to collect/concentrate in situ planktonic communities of different size fractions (targeting prokaryotes and unicellular eukaryotes) for posterior genomic, metagenomic, and/or transcriptomic analysis at a home laboratory. The IS-ABS field prototype is a small size and compact system able to operate up to 150 m depth. Water is pumped by a micropump (TCS MG2000) through a hydraulic circuit that allows in situ filtration of environmental water in one or more Sterivex filters placed in a filter cartridge. The IS-ABS also includes an application to program sampling definitions, allowing pre-setting configuration of the sampling. The efficiency of the IS-ABS was tested against traditional laboratory filtration standardized protocols. Results showed a good performance in terms of DNA recovery, as well as prokaryotic (16S rDNA) and eukaryotic (18S rDNA) community diversity analysis, using either methodologies. The IS-ABS automates the process of collecting environmental DNA, and is suitable for integration in water observation systems, what will contribute to substantially increase biological surveillances. Also, the use of highly sensitive genomic approaches allows a further study of the diversity and functions of whole or specific microbial communities. © 2019 Ribeiro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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. © 2019 IEEE.