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About

Armando Sousa received his Ph.D. degrees in the area of Robotics at the University of Porto, Portugal in 2004.
He is currently an Auxiliary Professor in the same faculty and an integrated researcher in the INESCTEC (Institute for Systems and Computer Engineering of Porto - Technology and Science).
He received several international awards in robotic soccer under the RoboCup Federation (mainly in the small size league). He has also received the Pedagogical Excellence award of the UP in year 2015.
His main research interests include education, robotics, data fusion and vision systems. He has co-authored over 50 international peer-reviewed publications and participated in over 10 international projects in the areas of education and robotics.

Interest
Topics
Details

Details

002
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

Visual Trunk Detection Using Transfer Learning and a Deep Learning-based Coprocessor

Authors
Aguiar, AS; Dos Santos, FN; Miranda De Sousa, AJM; Oliveira, PM; Santos, LC;

Publication
IEEE Access

Abstract

2020

Reinforcement Learning in Navigation and Cooperative Mapping

Authors
Cruz, JA; Cardoso, HL; Reis, LP; Sousa, A;

Publication
2020 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2020, Ponta Delgada, Portugal, April 15-17, 2020

Abstract

2020

Controller for Real and Simulated Wheelchair With a Multimodal Interface Using Gazebo and ROS

Authors
Cruz, AB; Sousa, A; Reis, LP;

Publication
2020 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2020, Ponta Delgada, Portugal, April 15-17, 2020

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.

Supervised
thesis

2019

Simulação e Melhoramento do PiTank com Sistema de Inteligência Artificial

Author
Sérgio Daniel Marinho de Lima Teixeira

Institution
UP-FEUP

2019

Odometria visual monocular em robôs para a agricultura com camara(s) com lentes "olho de peixe"

Author
André Silva Pinto de Aguiar

Institution
UP-FEUP

2019

Application of Safety Verification Techniques on ROS Software

Author
Tiago Filipe Miranda Neto

Institution
UP-FEUP

2019

Tangible language for educational programming of robots and other targets

Author
Ângela Filipa Pereira Cardoso

Institution
UP-FEUP

2019

Multi-Robot Learning of High-Level Skills in RoboCup

Author
Pedro Lavarinhas Amaro

Institution
UP-FEUP