Details
Name
Eduardo SilvaCluster
Networked Intelligent SystemsRole
TEC4 CoordinatorSince
01st December 2010
Nationality
PortugalCentre
Robotics and Autonomous SystemsContacts
+351228340554
eduardo.silva@inesctec.pt
2020
Authors
Fernandes, D; Pinheiro, F; Dias, A; Martins, A; Almeida, J; Silva, E;
Publication
Advances in Intelligent Systems and Computing
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.
2020
Authors
Teixeira, B; Silva, H; Matos, A; Silva, E;
Publication
IEEE Access
Abstract
2020
Authors
Barbosa, J; Dias, A; Almeida, J; Silva, E;
Publication
Advances in Intelligent Systems and Computing
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.
2020
Authors
Ferreira, A; Matias, B; Almeida, J; Silva, E;
Publication
International Journal of Advanced Robotic Systems
Abstract
2020
Authors
Almeida, J; Matias, B; Ferreira, A; Almeida, C; Martins, A; Silva, E;
Publication
Sensors
Abstract
Supervised Thesis
2019
Author
JOÃO DAVID GUIMARÃES FIGUEIREDO DIAS
Institution
IPP-ISEP
2018
Author
André Miguel Pinheiro Dias
Institution
Outra
2018
Author
RICARDO DANIEL CARNEIRO PEREIRA
Institution
IPP-ISEP
2015
Author
JOÃO PEDRO MENDES PEREIRA RIBEIRO
Institution
IPP-ISEP
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