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Sobre

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.

Tópicos
de interesse
Detalhes

Detalhes

002
Publicações

2020

Detecting and Solving Tube Entanglement in Bin Picking Operations

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

Publicação
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

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

Publicação
IEEE Access

Abstract

2020

Reinforcement Learning in Navigation and Cooperative Mapping

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

Publicação
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

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

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

Abstract

2020

Vineyard trunk detection using deep learning – An experimental device benchmark

Autores
Pinto de Aguiar, ASP; Neves dos Santos, FBN; Feliz dos Santos, LCF; de Jesus Filipe, VMD; Miranda de Sousa, AJM;

Publicação
Computers and Electronics in Agriculture

Abstract

Teses
supervisionadas

2019

Robotic simulator for the Tactode tangible block programming system

Autor
Márcia Sofia dos Santos Alves

Instituição
UP-FEUP

2019

Simulação e Melhoramento do PiTank com Sistema de Inteligência Artificial

Autor
Sérgio Daniel Marinho de Lima Teixeira

Instituição
UP-FEUP

2019

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

Autor
André Silva Pinto de Aguiar

Instituição
UP-FEUP

2019

Application of Safety Verification Techniques on ROS Software

Autor
Tiago Filipe Miranda Neto

Instituição
UP-FEUP

2019

Tangible language for educational programming of robots and other targets

Autor
Ângela Filipa Pereira Cardoso

Instituição
UP-FEUP