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Sobre
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Sobre

Olá!

Sou investigador do Centro de Robótica e Sistemas Autónomos (CRAS) no INESC TEC.

Recebi o meu título de "Mestre", pela Faculdade de Engenharia da Universidade do Porto (FEUP), Portugal, em 2010 e o título de "Doutoramento",  pela Faculdade de Engenharia da Universidade do Porto (FEUP), Portugal, em 2014.

Nos últimos 7 anos, eu tenho estado envolvido em diversos projetos de I&D relacionados com o desenvolvimento de robôs móveis de serviço, sistemas inteligentes e plataformas autónomas. Estive ainda envolvido em algumas parcerias empresariais.  Sou o autor de diversos artigos em algumas das revistas mais conceituadas da área da robótica e visão computacional. 

Atualmente, as minhas atividades de investigação incluem a visão artificial, robótica, percepção visual do movimento, análise do movimento, flúxo ótico, segmentação não-supervisionada, recontruções 3D do ambiente e, ainda, a visão subaquática.

Tópicos
de interesse
Detalhes

Detalhes

009
Publicações

2021

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

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

Publicação
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.

2021

A Modular Inductive Wireless Charging Solution for Autonomous Underwater Vehicles

Autores
Agostinho, LR; Ricardo, NC; Silva, RJ; Pinto, AM;

Publicação
2021 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC)

Abstract
In recent years, autonomous underwater vehicles (AUVs) have gained prominence in the most varied fields of application of underwater missions. The most common solution for recharging their batteries still implies removing them from the water, which is exceptionally costly. The use of Inductive Power Transfer (IPT) technologies allows to mitigate the associated costs and to extend the vehicles' operation time. In consequence, a prototype has been developed, whose objective is to integrate commercially available IPT technologies, while allowing the employment by most of the AUVs. The prototype is a funnel structure and its counterpart aimed to be fixed to a docking station and the AUV respectively. When coupled, it enables the batteries to recharge by electromagnetic induction. Energy transmission has been tested, resulting in encouraging results. This particular solution achieved over 90% efficiency during underwater experiments. The next objective will be to develop a commercial version of the prototype, that allows a direct, practical and effective use of wireless charging technologies within this particular scenario.

2021

A 3-D Lightweight Convolutional Neural Network for Detecting Docking Structures in Cluttered Environments

Autores
Pereira, MI; Leite, PN; Pinto, AM;

Publicação
MARINE TECHNOLOGY SOCIETY JOURNAL

Abstract
The maritime industry has been following the paradigm shift toward the automation of typically intelligent procedures, with research regarding autonomous surface vehicles (ASVs) having seen an upward trend in recent years. However, this type of vehicle cannot be employed on a full scale until a few challenges are solved. For example, the docking process of an ASV is still a demanding task that currently requires human intervention. This research work proposes a volumetric convolutional neural network (vCNN) for the detection of docking structures from 3-D data, developed according to a balance between precision and speed. Another contribution of this article is a set of synthetically generated data regarding the context of docking structures. The dataset is composed of LiDAR point clouds, stereo images, GPS, and Inertial Measurement Unit (IMU) information. Several robustness tests carried out with different levels of Gaussian noise demonstrated an average accuracy of 93.34% and a deviation of 5.46% for the worst case. Furthermore, the system was fine-tuned and evaluated in a real commercial harbor, achieving an accuracy of over 96%. The developed classifier is able to detect different types of structures and works faster than other state-of-the-art methods that establish their performance in real environments.

2021

Advancing Autonomous Surface Vehicles: A 3D Perception System for the Recognition and Assessment of Docking-Based Structures

Autores
Pereira, MI; Claro, RM; Leite, PN; Pinto, AM;

Publicação
IEEE ACCESS

Abstract
The automation of typically intelligent and decision-making processes in the maritime industry leads to fewer accidents and more cost-effective operations. However, there are still lots of challenges to solve until fully autonomous systems can be employed. Artificial Intelligence (AI) has played a major role in this paradigm shift and shows great potential for solving some of these challenges, such as the docking process of an autonomous vessel. This work proposes a lightweight volumetric Convolutional Neural Network (vCNN) capable of recognizing different docking-based structures using 3D data in real-time. A synthetic-to-real domain adaptation approach is also proposed to accelerate the training process of the vCNN. This approach makes it possible to greatly decrease the cost of data acquisition and the need for advanced computational resources. Extensive experiments demonstrate an accuracy of over 90% in the recognition of different docking structures, using low resolution sensors. The inference time of the system was about 120ms on average. Results obtained using a real Autonomous Surface Vehicle (ASV) demonstrated that the vCNN trained with the synthetic-to-real domain adaptation approach is suitable for maritime mobile robots. This novel AI recognition method, combined with the utilization of 3D data, contributes to an increased robustness of the docking process regarding environmental constraints, such as rain and fog, as well as insufficient lighting in nighttime operations.

2021

Exploiting Motion Perception in Depth Estimation Through a Lightweight Convolutional Neural Network

Autores
Leite, PN; Pinto, AM;

Publicação
IEEE ACCESS

Abstract
Understanding the surrounding 3D scene is of the utmost importance for many robotic applications. The rapid evolution of machine learning techniques has enabled impressive results when depth is extracted from a single image. High-latency networks are required to achieve these performances, rendering them unusable for time-constrained applications. This article introduces a lightweight Convolutional Neural Network (CNN) for depth estimation, NEON, designed for balancing both accuracy and inference times. Instead of solely focusing on visual features, the proposed methodology exploits the Motion-Parallax effect to combine the apparent motion of pixels with texture. This research demonstrates that motion perception provides crucial insight about the magnitude of movement for each pixel, which also encodes cues about depth since large displacements usually occur when objects are closer to the imaging sensor. NEON's performance is compared to relevant networks in terms of Root Mean Squared Error (RMSE), the percentage of correctly predicted pixels (delta(1)) and inference times, using the KITTI dataset. Experiments prove that NEON is significantly more efficient than the current top ranked network, estimating predictions 12 times faster; while achieving an average RMSE of 3.118 m and a delta(1) of 94.5%. Ablation studies demonstrate the relevance of tailoring the network to use motion perception principles in estimating depth from image sequences, considering that the effectiveness and quality of the estimated depth map is similar to more computational demanding state-of-the-art networks. Therefore, this research proposes a network that can be integrated in robotic applications, where computational resources and processing-times are important constraints, enabling tasks such as obstacle avoidance, object recognition and robotic grasping.

Teses
supervisionadas

2021

Sistema inteligente de carregamento sem fios para robôs móveis marítimos

Autor
Andreia da Rocha Seabra

Instituição
UP-FEUP

2021

Novos processos de gestão logística na indústria de equipamentos eletrónicos

Autor
Luís Simões Neves

Instituição
UP-FEUP

2020

Multi-agent Reinforcement Learning for Distributed Perception Systems in Maritime Environments

Autor
Maria Inês Rodrigues Pereira

Instituição
UP-FEUP

2020

Shape completion with a 3D Convolutional Neural Network for multi-domain O&M activities in offshore wind farms.

Autor
Ricardo Fernando de Freitas Dinis

Instituição
UP-FEUP

2020

Distributed Perception from Multiple Intelligent Systems for Offshore Marine Surveys

Autor
Daniel Filipe Barros Campos

Instituição
UP-FEUP