DOAJ Open Access 2023

Deep Learning-Empowered Robot Vision for Efficient Robotic Grasp Detection and Defect Elimination in Industry 4.0

Yassine Yazid Antonio Guerrero-González Ahmed El Oualkadi Mounir Arioua

Abstrak

Robot vision, enabled by deep learning breakthroughs, is gaining momentum in the industry 4.0 digitization process. The present investigation describes a robotic grasp detection application that makes use of a two-finger gripper and an RGB-D camera linked to a collaborative robot. The visual recognition system, which is integrated with edge computing units, conducts image recognition for faulty items and calculates the position of the robot arm. Identifying deformities in object photos, training and testing the images with a modified version of the You Only Look Once (YOLO) method, and establishing defect borders are all part of the process. Signals are subsequently sent to the robotic manipulator to remove the faulty components. The adopted technique used in this system is trained on custom data and has demonstrated a high accuracy and low latency performance as it reached a detection accuracy of 96% with 96.6% correct grasp accuracy.

Topik & Kata Kunci

Engineering machinery, tools, and implements

Penulis (4)

Yassine Yazid

Antonio Guerrero-González

Ahmed El Oualkadi

Mounir Arioua

Format Sitasi

APA MLA BibTeX

Yazid, Y., Guerrero-González, A., Oualkadi, A.E., Arioua, M. (2023). Deep Learning-Empowered Robot Vision for Efficient Robotic Grasp Detection and Defect Elimination in Industry 4.0. https://doi.org/10.3390/ecsa-10-16079

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.3390/ecsa-10-16079
Akses: Open Access ✓