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Sobre

Sobre

Marcelo R. Petry is a robotics researcher and educator in the Centre for Robotics in Industry and Intelligent Systems at INESC TEC (Portugal). His research lies at the intersection of robotics, computer vision, and mixed reality, aiming the application of robots to logistics, inspection, and human assistance. Marcelo graduated in Control and Automation Engineering from the Pontifical Catholic University of Rio Grande do Sul in 2008 (Brazil) and obtained his PhD in Informatics Engineering from the University of Porto in 2013 (Portugal). Previously he was Assistant Professor at Federal University of Santa Catarina, and researcher at INESC P&D Brazil (2014 to 2019).

Tópicos
de interesse
Detalhes

Detalhes

017
Publicações

2023

Comparison of 3D Sensors for Automating Bolt-Tightening Operations in the Automotive Industry

Autores
Dias, J; Simoes, P; Soares, N; Costa, CM; Petry, MR; Veiga, G; Rocha, LF;

Publicação
SENSORS

Abstract
Machine vision systems are widely used in assembly lines for providing sensing abilities to robots to allow them to handle dynamic environments. This paper presents a comparison of 3D sensors for evaluating which one is best suited for usage in a machine vision system for robotic fastening operations within an automotive assembly line. The perception system is necessary for taking into account the position uncertainty that arises from the vehicles being transported in an aerial conveyor. Three sensors with different working principles were compared, namely laser triangulation (SICK TriSpector1030), structured light with sequential stripe patterns (Photoneo PhoXi S) and structured light with infrared speckle pattern (Asus Xtion Pro Live). The accuracy of the sensors was measured by computing the root mean square error (RMSE) of the point cloud registrations between their scans and two types of reference point clouds, namely, CAD files and 3D sensor scans. Overall, the RMSE was lower when using sensor scans, with the SICK TriSpector1030 achieving the best results (0.25 mm +/- 0.03 mm), the Photoneo PhoXi S having the intermediate performance (0.49 mm +/- 0.14 mm) and the Asus Xtion Pro Live obtaining the higher RMSE (1.01 mm +/- 0.11 mm). Considering the use case requirements, the final machine vision system relied on the SICK TriSpector1030 sensor and was integrated with a collaborative robot, which was successfully deployed in an vehicle assembly line, achieving 94% success in 53,400 screwing operations.

2023

Assessment of the influence of magnetic perturbations and dynamic motions in a commercial AHRS

Autores
Martins, JG; Petry, MR; Moreira, AP;

Publicação
IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2023, Tomar, Portugal, April 26-27, 2023

Abstract

2022

Deformable convolutions in multi-view stereo

Autores
Masson, JEN; Petry, MR; Coutinho, DF; Honorio, LD;

Publicação
IMAGE AND VISION COMPUTING

Abstract
The Multi-View Stereo (MVS) is a key process in the photogrammetry workflow. It is responsible for taking the camera's views and finding the maximum number of matches between the images yielding a dense point cloud of the observed scene. Since this process is based on the matching between images it greatly depends on the abil-ity of features matching throughout different images. To improve the matching performance several researchers have proposed the use of Convolutional Neural Networks (CNNs) to solve the MVS problem. Despite the progress in the MVS problem with the usage of CNNs, the Video RAM (VRAM) consumption within these approaches is usually far greater than classical methods, that rely more on RAM, which is cheaper to expand than VRAM. This work then follows the progress made in CasMVSNet in the reduction of GPU memory usage, and further study the changes in the feature extraction process. The Average Group-wise Correlation is used in the cost vol-ume generation, to reduce the number of channels in the cost volume, yielding a reduction in GPU memory usage without noticeable penalties in the result. The deformable convolutions are applied in the feature extraction net -work to augment the spatial sampling locations with learning offsets, without additional supervision, to further improve the network's ability to model transformations. The impact of these changes is measured using quanti-tative and qualitative tests using the DTU and the Tanks and Temples datasets. The modifications reduced the GPU memory usage by 32% and improved the completeness by 9% with a penalty of 6.6% in accuracy on the DTU dataset.(c) 2021 Published by Elsevier B.V.

2022

Collision Avoidance Considering Iterative Bezier Based Approach for Steep Slope Terrains

Autores
Santos, LC; Santos, FN; Valente, A; Sobreira, H; Sarmento, J; Petry, M;

Publicação
IEEE ACCESS

Abstract
The Agri-Food production requirements needs a more efficient and autonomous processes, and robotics will play a significant role in this process. Deploying agricultural robots on the farm is still a challenging task. Particularly in slope terrains, where it is crucial to avoid obstacles and dangerous steep slope zones. Path planning solutions may fail under several circumstances, as the appearance of a new obstacle. This work proposes a novel open-source solution called AgRobPP-CA to autonomously perform obstacle avoidance during robot navigation. AgRobPP-CA works in real-time for local obstacle avoidance, allowing small deviations, avoiding unexpected obstacles or dangerous steep slope zones, which could impose a fall of the robot. Our results demonstrated that AgRobPP-CA is capable of avoiding obstacles and high slopes in different vineyard scenarios, with low computation requirements. For example, in the last trial, AgRobPP-CA avoided a steep ramp that could impose a fall to the robot.

2022

Augmented Reality for Human-Robot Collaboration and Cooperation in Industrial Applications: A Systematic Literature Review

Autores
Costa, GD; Petry, MR; Moreira, AP;

Publicação
SENSORS

Abstract
With the continuously growing usage of collaborative robots in industry, the need for achieving a seamless human-robot interaction has also increased, considering that it is a key factor towards reaching a more flexible, effective, and efficient production line. As a prominent and prospective tool to support the human operator to understand and interact with robots, Augmented Reality (AR) has been employed in numerous human-robot collaborative and cooperative industrial applications. Therefore, this systematic literature review critically appraises 32 papers' published between 2016 and 2021 to identify the main employed AR technologies, outline the current state of the art of augmented reality for human-robot collaboration and cooperation, and point out future developments for this research field. Results suggest that this is still an expanding research field, especially with the advent of recent advancements regarding head-mounted displays (HMDs). Moreover, projector-based and HMDs developed approaches are showing promising positive influences over operator-related aspects such as performance, task awareness, and safety feeling, even though HMDs need further maturation in ergonomic aspects. Further research should focus on large-scale assessment of the proposed solutions in industrial environments, involving the solution's target audience, and on establishing standards and guidelines for developing AR assistance systems.

Teses
supervisionadas

2022

Evaluation of the influence and impacts of an augmented reality application as a tool to support production in the context of industry 4.0

Autor
Gabriel de Moura Costa

Instituição
UP-FEUP

2020

Autonomous Wheelchair to support Patients of Hospital Services

Autor
André Rodrigues Baltazar

Instituição
UP-FEUP

2020

Odometry and Extrinsic Sensor Calibration on Mobile Robots

Autor
Ricardo Barbosa Sousa

Instituição
UP-FEUP