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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.

2022

Using Simulation to Evaluate a Tube Perception Algorithm for Bin Picking

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

Publication
ROBOTICS

Abstract
Bin picking is a challenging problem that involves using a robotic manipulator to remove, one-by-one, a set of objects randomly stacked in a container. In order to provide ground truth data for evaluating heuristic or machine learning perception systems, this paper proposes using simulation to create bin picking environments in which a procedural generation method builds entangled tubes that can have curvatures throughout their length. The output of the simulation is an annotated point cloud, generated by a virtual 3D depth camera, in which the tubes are assigned with unique colors. A general metric based on micro-recall is proposed to compare the accuracy of point cloud annotations with the ground truth. The synthetic data is representative of a high quality 3D scanner, given that the performance of a tube modeling system when given 640 simulated point clouds was similar to the results achieved with real sensor data. Therefore, simulation is a promising technique for the automated evaluation of solutions for bin picking tasks.

2022

Contactless Soil Moisture Mapping Using Inexpensive Frequency-Modulated Continuous Wave RADAR for Agricultural Purposes

Authors
Coutinho, RM; Sousa, A; Santos, F; Cunha, M;

Publication
APPLIED SCIENCES-BASEL

Abstract
Soil Moisture (SM) is one of the most critical factors for a crop’s growth, yield, and quality. Although Ground-Penetrating RADAR (GPR) is commonly used in satelite observation to analyze soil moisture, it is not cost-effective for agricultural applications. Automotive RADAR uses the concept of Frequency-Modulated Continuous Wave (FMCW) and is more competitive in terms of price. This paper evaluates the viability of using a cost-effective RADAR as a substitute for GPR for soil moisture content estimation. The research consisted of four experiments, and the results show that the RADAR’s output signal and the soil moisture sensor SEN0193 have a high correlation with values as high as 0.93 when the SM is below 15%. Such results show that the tested sensor (and its cost-effective working principle) are able to determine soil water content (with certain limitations) in a non-intrusive, proximal sensing manner.

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

Abstract

Supervised
thesis

2021

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

Author
Gonçalo Freitas Ferreira Martins

Institution
UP-FEUP

2021

Formação ética em engenharia com recurso a metodologias ativas: caso de estudo em Engenharia Eletrotécnica

Author
Maria de Fátima Coelho Monteiro

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

Definition of a conceptual model to asses the environmental sustainability of parcel delivery

Author
Vasco Silva

Institution
IES_Outra