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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

014
Publications

2020

Detecting and Solving Tube Entanglement in Bin Picking Operations

Authors
Leao, G; Costa, CM; Sousa, A; Veiga, G;

Publication
Applied Sciences

Abstract
Manufacturing and production industries are increasingly turning to robots to carry out repetitive picking operations in an efficient manner. This paper focuses on tackling the novel challenge of automating the bin picking process for entangled objects, for which there is very little research. The chosen case study are sets of freely curved tubes, which are prone to occlusions and entanglement. The proposed algorithm builds a representation of the tubes as an ordered list of cylinders and joints using a point cloud acquired by a 3D scanner. This representation enables the detection of occlusions in the tubes. The solution also performs grasp planning and motion planning, by evaluating post-grasp trajectories via simulation using Gazebo and the ODE physics engine. A force/torque sensor is used to determine how many items were picked by a robot gripper and in which direction it should rotate to solve cases of entanglement. Real-life experiments with sets of PVC tubes and rubber radiator hoses showed that the robot was able to pick a single tube on the first try with success rates of 99% and 93%, respectively. This study indicates that using simulation for motion planning is a promising solution to deal with entangled objects.

2020

Optimal automatic path planner and design for high redundancy robotic systems

Authors
Tavares, P; Marques, D; Malaca, P; Veiga, G; Costa, P; Moreira, AP;

Publication
INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION

Abstract
Purpose In the vast majority of the individual robot installations, the robot arm is just one piece of a complex puzzle of components, such as grippers, jigs or external axis, that together compose an industrial robotic cell. The success of such installations is very dependent not only on the selection of such components but also on the layout and design of the final robotic cell, which are the main tasks of the system integrators. Consequently, successful robot installations are often empirical tasks owing to the high number of experimental combinations that could lead to exhaustive and time-consuming testing approaches. Design/methodology/approach A newly developed optimized technique to deal with automatic planning and design of robotic systems is proposed and tested in this paper. Findings The application of a genetic-based algorithm achieved optimal results in short time frames and improved the design of robotic work cells. Here, the authors show that a multi-layer optimization approach, which can be validated using a robotic tool, is able to help with the design of robotic systems. Originality/value To date, robotic solutions lack flexibility to cope with the demanding industrial environments. The results presented here formalize a new flexible and modular approach, which can provide optimal solutions throughout the different stages of design and execution control of any work cell.

2020

ROBIN: An open-source middleware for plug'n'produce of Cyber-Physical Systems

Authors
Arrais, R; Ribeiro, P; Domingos, H; Veiga, G;

Publication
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS

Abstract
Motivated by the Fourth Industrial Revolution, there is an ever-increasing need to integrated Cyber-Physical Systems in industrial production environments. To address the demand for flexible robotics in contemporary industrial environments and the necessity to integrate robots and automation equipment in an efficient manner, an effective, bidirectional, reliable and structured data interchange mechanism is required. As an answer to these requirements, this article presents ROBIN, an open-source middleware for achieving interoperability between the Robot Operating System and CODESYS, a softPLC that can run on embedded devices and that supports a variety of fieldbuses and industrial network protocols. The referred middleware was successfully applied and tested in various industrial applications such as battery management systems, motion, robotic manipulator and safety hardware control, and horizontal integration between a mobile manipulator and a conveyor system.

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

Supervised
thesis

2019

Development and Simulation of an Automatic Tool Changer for an ABB Robot

Author
Paulo Jorge Leitão e Sousa

Institution
UP-FEUP

2019

Towards “Industrie 4.0” in the context of investment casting industry

Author
Isabel Maria Lousada Soares Figueiredo

Institution
UP-FEUP

2019

Mobile Robotics Simulation for ROS Based Robots Using Visual Components

Author
Gustavo Emanuel Barbosa Teixeira

Institution
UP-FEUP

2019

Open Scalable Production System: An Industry 4.0 Framework for Cyber-Physical Systems

Author
Rafael Lírio Arrais

Institution
UP-FEUP

2018

Development of robotic manipulators for scalable production lines

Author
Paulo Diogo Carvalho Ribeiro

Institution
UP-FEUP