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About

Germano Veiga is a Mechanical Engineer with a PhD in Mechanical Engineering (Robotics and Automation) (2010) by the University of Coimbra.
In 2005 he was an invited researcher at the University of Lund, Sweden, and was a researcher (2002-2011) and Invited Professor (2007-2011) at the University of Coimbra.
He is now Senior Researcher at INESC TEC, in Porto, and from 2016 is Auxiliar Professor at the Faculty of Engineering of the University of Porto.
His research interests are mostly focused on future industrial robotics including, plug-and-produce technologies,
robot programming, mobile manipulators and Human Robot Interfacing. During his PhD studies Germano was part of the FP6 SMErobot team (2005-2009) and later became member of the Exec. Committee of the FP7 ECHORD project (2009-2012)
More recently Germano became the coordinator of the INESC-TEC team participating in the projects FP7-CARLoS, FP7-STAMINA, FP7-SMErobotics, H2020-ColRobot.
Since January 2017 he is the Coordinator of the H2020 ScalABLE4.0 project.

Interest
Topics
Details

Details

011
Publications

2019

Testing the vertical and cyber-physical integration of cognitive robots in manufacturing

Authors
Krueger, V; Rovida, F; Grossmann, B; Petrick, R; Crosby, M; Charzoule, A; Garcia, GM; Behnke, S; Toscano, C; Veiga, G;

Publication
Robotics and Computer-Integrated Manufacturing

Abstract

2019

Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry

Authors
Malaca, P; Rocha, LF; Gomes, D; Silva, J; Veiga, G;

Publication
Journal of Intelligent Manufacturing

Abstract
This paper focus on the classification, in real-time and under uncontrolled lighting, of fabric textures for the automotive industry. Many industrial processes have spatial constraints that limit the effective control of illumination of their vision based systems, hindering their effectiveness. The ability to overcome these problems using robust classification methods with suitable pre-processing techniques and choice of characteristics will increase the efficiency of this type of solutions with obvious production gains and thus economical. For this purpose, this paper studied and analyzed various pre-processing techniques, and selected the most appropriate fabric characteristics for the considered industrial case scenario. The methodology followed was based on the comparison of two different machine learning classifiers, ANN and SVM, using a large set of samples with a large variability of lightning conditions to faithfully simulate the industrial environment. The obtained solution shows the sensibility of ANN over SVM considering the number of features and the size of the training set, showing the better effectiveness and robustness of the last. The characteristics vector uses histogram equalization, Laws filter and Sobel filter, and multi-scale analysis. By using a correlation based method was possible to reduce the number of features used, achieving a better balanced between processing time and classification ratio. © 2016 Springer Science+Business Media New York

2019

SMErobotics: Smart Robots for Flexible Manufacturing

Authors
Perzylo, A; Rickert, M; Kahl, B; Somani, N; Lehmann, C; Kuss, A; Profanter, S; Beck, AB; Haage, M; Hansen, MR; Nibe, MT; Roa, MA; Sornmo, O; Robertz, SG; Thomas, U; Veiga, G; Topp, EA; Kessler, I; Danzer, M;

Publication
IEEE Robotics and Automation Magazine

Abstract

2018

Landmark detection for docking tasks

Authors
Ferreira, F; Sobreira, H; Veiga, G; Moreira, A;

Publication
Advances in Intelligent Systems and Computing

Abstract
For docking manoeuvres, the detection of the objects to dock needs to be precise as the minimum deviation from the objective may lead to the failure of this task. The objective of this article is to test possible ways to detect a landmark using a laser rangefinder for docking manoeuvres. We will test a beacon-based localisation algorithm and an algorithm based on natural landmarks already implemented, however, we will apply modifications to such methods. To verify the possibility of docking using these methods, we will conduct experiments with a real robot. © Springer International Publishing AG 2018.

2018

Flexible work cell simulator using digital twin methodology for highly complex systems in industry 4.0

Authors
Tavares, P; Silva, JA; Costa, P; Veiga, G; Moreira, AP;

Publication
Advances in Intelligent Systems and Computing

Abstract
The continuous evolution in manufacturing processes has attracted substantial interest from both scientific and research community, as well as from industry. Despite the fact that streamline manufacturing relies on automation systems, most production lines within the industrial environment lack a flexible framework that allows for evaluation and optimisation of the manufacturing process. Consequently, the development of a generic simulators able to mimic any given workflow represent a promising approach within the manufacturing industry. Recently the concept of digital twin methodology has been introduced to mimic the real world through a virtual substitute, such as, a simulator. In this paper, a solution capable of representing any industrial work cell and its properties is presented. Here we describe the key stages of such solution which has enough flexibility to be applied to different working scenarios commonly found in industrial environment. © 2018, Springer International Publishing AG.

Supervised
thesis

2018

IM2HoT: Interactive Machine-Learning to improve the House of Things

Author
João Pedro Milano da Silva Cardoso

Institution
UP-FEUP

2017

Automação de sistema robotizado colaborativo para soldadura de componentes para construção soldada

Author
Luís Gonçalo Franco Ruas

Institution
UP-FEUP

2017

Desenvolvimento de um controlador modular para LeanAGV baseado na norma IEC 61131-3

Author
Luís Tiago da Silva Costa

Institution
UP-FEUP

2017

Grasp planning for handoff between robotic manipulators

Author
David Miguel Ribeiro de Sousa

Institution
UP-FEUP

2017

Navegação e controlo de robôs móveis com atrelagem de reboques automática

Author
Francisco Abílio Rodrigues Guerra Ferreira

Institution
UP-FEUP