Cookies Policy
We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out More
Close
  • Menu
About

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

012
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

2019

Collaborative Welding System using BIM for Robotic Reprogramming and Spatial Augmented Reality

Authors
Tavares, P; Costa, CM; Rocha, L; Malaca, P; Costa, P; Moreira, AP; Sousa, A; Veiga, G;

Publication
Automation in Construction

Abstract
The optimization of the information flow from the initial design and through the several production stages plays a critical role in ensuring product quality while also reducing the manufacturing costs. As such, in this article we present a cooperative welding cell for structural steel fabrication that is capable of leveraging the Building Information Modeling (BIM) standards to automatically orchestrate the necessary tasks to be allocated to a human operator and a welding robot moving on a linear track. We propose a spatial augmented reality system that projects alignment information into the environment for helping the operator tack weld the beam attachments that will be later on seam welded by the industrial robot. This way we ensure maximum flexibility during the beam assembly stage while also improving the overall productivity and product quality since the operator no longer needs to rely on error prone measurement procedures and he receives his tasks through an immersive interface, relieving him from the burden of analyzing complex manufacturing design specifications. Moreover, no expert robotics knowledge is required to operate our welding cell because all the necessary information is extracted from the Industry Foundation Classes (IFC), namely the CAD models and welding sections, allowing our 3D beam perception systems to correct placement errors or beam bending, which coupled with our motion planning and welding pose optimization system ensures that the robot performs its tasks without collisions and as efficiently as possible while maximizing the welding quality. © 2019 Elsevier B.V.

2019

Modeling of video projectors in OpenGL for implementing a spatial augmented reality teaching system for assembly operations

Authors
Costal, CM; Veiga, G; Sousa, A; Rocha, L; Sousa, AA; Rodrigues, R; Thomas, U;

Publication
19th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2019

Abstract
Teaching complex assembly and maintenance skills to human operators usually requires extensive reading and the help of tutors. In order to reduce the training period and avoid the need for human supervision, an immersive teaching system using spatial augmented reality was developed for guiding inexperienced operators. The system provides textual and video instructions for each task while also allowing the operator to navigate between the teaching steps and control the video playback using a bare hands natural interaction interface that is projected into the workspace. Moreover, for helping the operator during the final validation and inspection phase, the system projects the expected 3D outline of the final product. The proposed teaching system was tested with the assembly of a starter motor and proved to be more intuitive than reading the traditional user manuals. This proof of concept use case served to validate the fundamental technologies and approaches that were proposed to achieve an intuitive and accurate augmented reality teaching application. Among the main challenges were the proper modeling and calibration of the sensing and projection hardware along with the 6 DoF pose estimation of objects for achieving precise overlap between the 3D rendered content and the physical world. On the other hand, the conceptualization of the information flow and how it can be conveyed on-demand to the operator was also of critical importance for ensuring a smooth and intuitive experience for the operator. © 2019 IEEE.

Supervised
thesis

2018

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

Author
João Pedro Milano da Silva Cardoso

Institution
UP-FEUP

2017

Desenvolvimento de controlador para equipamento de corte e furação de vigas

Author
João André Ramos Silva

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