Details
Name
Alfredo MartinsCluster
Networked Intelligent SystemsRole
Assistant ResearcherSince
01st March 2011
Nationality
PortugalCentre
Robotics and Autonomous SystemsContacts
+351228340554
alfredo.martins@inesctec.pt
2021
Authors
Resende, J; Barbosa, P; Almeida, J; Martins, A;
Publication
2021 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
Abstract
2021
Authors
Loureiro, G; Dias, A; Martins, A; Almeida, J;
Publication
Remote Sensing
Abstract
2021
Authors
Amado, M; Lopes, F; Dias, A; Martins, A;
Publication
IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2021, Santa Maria da Feira, Portugal, April 28-29, 2021
Abstract
2021
Authors
Moura, A; Antunes, J; Dias, A; Martins, A; Almeida, J;
Publication
IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2021, Santa Maria da Feira, Portugal, April 28-29, 2021
Abstract
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.
Supervised Thesis
2021
Author
PEDRO NUNO DE QUEIRÓS SALCEDAS DE CARVALHO GERALDES
Institution
IPP-ISEP
2020
Author
JOÃO FILIPE AMORIM RESENDE
Institution
IPP-ISEP
2020
Author
RUI RODRIGO SERRA FIGUEIRINHA
Institution
IPP-ISEP
2020
Author
PEDRO EMANUEL JORGE BARBOSA
Institution
IPP-ISEP
2019
Author
SHRAVAN DEV RAJESH
Institution
IPP-ISEP
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