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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.

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Publications

2022

Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data

Authors
Aguiar, AS; dos Santos, FN; Sobreira, H; Boaventura Cunha, J; Sousa, AJ;

Publication
Frontiers in Robotics and AI

Abstract
Developing ground robots for agriculture is a demanding task. Robots should be capable of performing tasks like spraying, harvesting, or monitoring. However, the absence of structure in the agricultural scenes challenges the implementation of localization and mapping algorithms. Thus, the research and development of localization techniques are essential to boost agricultural robotics. To address this issue, we propose an algorithm called VineSLAM suitable for localization and mapping in agriculture. This approach uses both point- and semiplane-features extracted from 3D LiDAR data to map the environment and localize the robot using a novel Particle Filter that considers both feature modalities. The numeric stability of the algorithm was tested using simulated data. The proposed methodology proved to be suitable to localize a robot using only three orthogonal semiplanes. Moreover, the entire VineSLAM pipeline was compared against a state-of-the-art approach considering three real-world experiments in a woody-crop vineyard. Results show that our approach can localize the robot with precision even in long and symmetric vineyard corridors outperforming the state-of-the-art algorithm in this context.

2021

Particle filter refinement based on clustering procedures for high-dimensional localization and mapping systems

Authors
Aguiar, AS; dos Santos, FN; Sobreira, H; Cunha, JB; Sousa, AJ;

Publication
Robotics and Autonomous Systems

Abstract

2021

Open Hardware and Software Robotics Competition for Additional Engagement in ECE Students - The Robot@Factory Lite Case Study

Authors
Pinto, VH; Sousa, A; Lima, J; Gonçalves, J; Costa, P;

Publication
Lecture Notes in Electrical Engineering - CONTROLO 2020

Abstract

2021

Bringing Semantics to the Vineyard: An Approach on Deep Learning-Based Vine Trunk Detection

Authors
Aguiar, AS; Monteiro, NN; dos Santos, FN; Pires, EJS; Silva, D; Sousa, AJ; Boaventura Cunha, J;

Publication
Agriculture

Abstract
The development of robotic solutions in unstructured environments brings several challenges, mainly in developing safe and reliable navigation solutions. Agricultural environments are particularly unstructured and, therefore, challenging to the implementation of robotics. An example of this is the mountain vineyards, built-in steep slope hills, which are characterized by satellite signal blockage, terrain irregularities, harsh ground inclinations, and others. All of these factors impose the implementation of precise and reliable navigation algorithms, so that robots can operate safely. This work proposes the detection of semantic natural landmarks that are to be used in Simultaneous Localization and Mapping algorithms. Thus, Deep Learning models were trained and deployed to detect vine trunks. As significant contributions, we made available a novel vine trunk dataset, called VineSet, which was constituted by more than 9000 images and respective annotations for each trunk. VineSet was used to train state-of-the-art Single Shot Multibox Detector models. Additionally, we deployed these models in an Edge-AI fashion and achieve high frame rate execution. Finally, an assisted annotation tool was proposed to make the process of dataset building easier and improve models incrementally. The experiments show that our trained models can detect trunks with an Average Precision up to 84.16% and our assisted annotation tool facilitates the annotation process, even in other areas of agriculture, such as orchards and forests. Additional experiments were performed, where the impact of the amount of training data and the comparison between using Transfer Learning and training from scratch were evaluated. In these cases, some theoretical assumptions were verified.

2021

Design and Comparison of Image Hashing Methods: A Case Study on Cork Stopper Unique Identification

Authors
Fitas, R; Rocha, B; Costa, V; Sousa, A;

Publication
Journal of Imaging

Abstract
Cork stoppers were shown to have unique characteristics that allow their use for authentication purposes in an anti-counterfeiting effort. This authentication process relies on the comparison between a user’s cork image and all registered cork images in the database of genuine items. With the growth of the database, this one-to-many comparison method becomes lengthier and therefore usefulness decreases. To tackle this problem, the present work designs and compares hashing-assisted image matching methods that can be used in cork stopper authentication. The analyzed approaches are the discrete cosine transform, wavelet transform, Radon transform, and other methods such as difference hash and average hash. The most successful approach uses a 1024-bit hash length and difference hash method providing a 98% accuracy rate. By transforming the image matching into a hash matching problem, the approach presented becomes almost 40 times faster when compared to the literature.

Supervised
thesis

2021

Machine Learning Based Controller for the Robot used in Autonomous Driving Competition

Author
Gonçalo Freitas Ferreira Martins

Institution
UP-FEUP

2021

REDI 4.0 - Robot for Demonstrations with Behaviour Defined on Paper

Author
Isabel Fernandes Neves

Institution
UP-FEUP

2021

Robot navigation in vineyards based on the visual vanish point concept

Author
José Maria Queirós Rodrigues Sarmento

Institution
UP-FEUP

2021

Tactode programming for robotics and other targets

Author
César Alexandre da Costa Pinho

Institution
UP-FEUP

2021

Cross-Sensor Face Detection

Author
José Duarte Penetro Mesquita Ferreira

Institution
UP-FEUP