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About

About

Aníbal Matos received a PhD in Electrical and Computer Engineering form Porto University in 2001. He is currently associate professor at the Faculty of Engineering of Porto University and a member of the board of directors at INESC TEC. His main research interests are related to perception, sensing, navigation, and control of autonomous marine robots, being the author or co-author of more than 80 publications in international journals and conferences. He has participated and lead several research projects on marine robotics and its application to monitoring, inspection, search and rescue, and defense.

Interest
Topics
Details

Details

032
Publications

2022

3DupIC: An Underwater Scan Matching Method for Three-Dimensional Sonar Registration

Authors
Ferreira, A; Almeida, J; Martins, A; Matos, A; Silva, E;

Publication
SENSORS

Abstract
This work presents a six degrees of freedom probabilistic scan matching method for registration of 3D underwater sonar scans. Unlike previous works, where local submaps are built to overcome measurement sparsity, our solution develops scan matching directly from the raw sonar data. Our method, based on the probabilistic Iterative Correspondence (pIC), takes measurement uncertainty into consideration while developing the registration procedure. A new probabilistic sensor model was developed to compute the uncertainty of each scan measurement individually. Initial displacement guesses are obtained from a probabilistic dead reckoning approach, also detailed in this document. Experiments, based on real data, demonstrate superior robustness and accuracy of our method with respect to the popular ICP algorithm. An improved trajectory is obtained by integration of scan matching updates in the localization data fusion algorithm, resulting in a substantial reduction of the original dead reckoning drift.

2022

An Autonomous System for Collecting Water Samples from the Surface

Authors
Pinto, AF; Cruz, NA; Ferreira, BM; Abreu, NM; Goncalves, CE; Villa, MP; Matos, AC; Honorio, LD; Westin, LG;

Publication
OCEANS 2022

Abstract

2022

Modular Multi-Domain Aware Autonomous Surface Vehicle for Inspection

Authors
Campos, DF; Matos, A; Pinto, AM;

Publication
IEEE ACCESS

Abstract

2022

Multi-criteria metric to evaluate motion planners for underwater intervention

Authors
Silva, R; Matos, A; Pinto, AM;

Publication
AUTONOMOUS ROBOTS

Abstract
Underwater autonomous manipulation is the capability of a mobile robot to perform intervention tasks that require physical contact with unstructured environments without continuous human supervision. Being difficult to assess the behaviour of existing motion planner algorithms, this research proposes a new planner evaluation metric to identify well-behaved planners for specialized tasks of inspection and monitoring of man-made underwater structures. This metric is named NEMU and combines three different performance indicators: effectiveness, safety and adaptability. NEMU deals with the randomization of sampling-based motion planners. Moreover, this article presents a benchmark of multiple planners applied to a 6 DoF manipulator operating underwater. Results conducted in real scenarios show that different planners are better suited for different tasks. Experiments demonstrate that the NEMU metric can be used to distinguish the performance of planners for particular movement conditions. Moreover, it identifies the most promising planner for collision-free motion planning, being a valuable contribution for the inspection of maritime structures, as well as for the manipulation procedures of autonomous underwater vehicles during close range operations.

2021

Multi-domain inspection of offshore wind farms using an autonomous surface vehicle

Authors
Campos, DF; Matos, A; Pinto, AM;

Publication
SN APPLIED SCIENCES

Abstract
AbstractThe offshore wind power industry is an emerging and exponentially growing sector, which calls to a necessity for a cyclical monitoring and inspection to ensure the safety and efficiency of the wind farm facilities. Thus, the emersed (aerial) and immersed (underwater) scenarios must be reconstructed to create a more complete and reliable map that maximizes the observability of all the offshore structures from the wind turbines to the cable arrays, presenting a multi domain scenario.This work proposes the use of an Autonomous Surface Vehicle (ASV) to map both domains simultaneously. As such, it will produce a multi-domain map through the fusion of navigational sensors, GPS and IMU, to localize the vehicle and aid the registration process for the perception sensors, 3D Lidar and Multibeam echosounder sonar. The performed experiments demonstrate the ability of the multi-domain mapping architecture to provide an accurate reconstruction of both scenarios into a single representation using the odometry system as the initial seed to further improve the map with data filtering and registration processes. An error of 0.049 m for the odometry estimation is observed with the GPS/IMU fusion for simulated data and 0.07 m for real field tests. The multi-domain map methodology requires an average of 300 ms per iteration to reconstruct the environment, with an error of at most 0.042 m in simulation.

Supervised
thesis

2021

O impacto do ruído no roteamento de veículos para a logística urbana

Author
CLÁUDIA SOFIA ELIAS MORAIS PEREIRA

Institution
IPP-ISEP

2021

Solving Sparse-Reward Problems in Robot Manipulation Tasks using Deep Reinforcement Learning

Author
Eduardo Miguel Lage Dixo Sousa

Institution
UP-FCUP

2021

Collaborative Tools for Lung Cancer Diagnosis in Computed Tomography

Author
Carlos Alexandre Nunes Ferreira

Institution
UP-FEUP

2021

Data Privacy and the Value of Information.

Author
Fábio Miguel Azevedo Correia

Institution
UP-FEP

2021

Business Diplomacy Relevance in Successful International Endeavours of MNCs: A Multiple Case Study Analysis.

Author
Rui Manuel Ribeiro Monteiro

Institution
UP-FEP