Volume 20, Issue 4 (December (Special Issue on ADLEEE) 2024)                   IJEEE 2024, 20(4): 3331-3331 | Back to browse issues page

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Herrera-Benavidez J, Pachón-Suescún C, Jimenez-Moreno R. Faster R-CNN and 3D reconstruction for handling tasks implementing a Scara robot. IJEEE 2024; 20 (4) :3331-3331
URL: http://ijeee.iust.ac.ir/article-1-3331-en.html
Faster R-CNN and 3D reconstruction for handling tasks implementing a Scara robot
Julian Herrera-Benavidez , Cesar Pachón-Suescún , Robinson Jimenez-Moreno
Abstract:   (262 Views)
This paper presents the design and results of using a deep learning algorithm for robotic manipulation in object handling tasks in a virtual industrial environment. The simulation tool used is V-REP and the environment corresponds to a production line based on a conveyor belt and a SCARA type robot manipulator. The main contribution of this work focuses on the integration of a depth camera located on the robot and the computation of the gripping coordinates by identifying and locating three different types of objects of interest with random locations on the conveyor belt, through a Faster R-CNN. The results show that the system manages to perform the indicated activities, obtaining a classification accuracy of 97.4% and a mean average precision of 0.93, which allowed a correct detection and manipulation of the objects.
Keywords: Faster R-CNN, Homogeneous Transformation Matrix, Point Cloud, V-REP
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Type of Study: Closed - 2024 Special Issue on Applications of Deep Learning in Electrical and Electronic Engineerin | Subject: Industrial Electronics
Received: 2024/06/13 | Revised: 2024/12/02 | Accepted: 2024/10/31
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.