Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor Belts

The use of computer vision in the industry has become fundamental, playing an essential role in areas such as quality control and inspection, object recognition/tracking, and automation. Despite this constant growth, robotic cell systems employing computer vision encounter significant challenges, su...

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Bibliographic Details
Published inJournal of Manufacturing and Materials Processing Vol. 8; no. 5; p. 218
Main Authors Gouveia, Eber L., Lyons, John G., Devine, Declan M.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2024
Subjects
Belt conveyors
Collaboration
Computer vision
Conveying machinery
Flexibility
Geospatial data
Industrial robots
Machine vision
Manufacturing
Object recognition
pick-and-place task
Pneumatics
Quality control
Quality control equipment
random flow
Robot arms
robot operating system
Robots
Sensors
Three dimensional flow
Vision systems
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Summary:The use of computer vision in the industry has become fundamental, playing an essential role in areas such as quality control and inspection, object recognition/tracking, and automation. Despite this constant growth, robotic cell systems employing computer vision encounter significant challenges, such as a lack of flexibility to adapt to different tasks or types of objects, necessitating extensive adjustments each time a change is required. This highlights the importance of developing a system that can be easily reused and reconfigured to address these challenges. This paper introduces a versatile and adaptable framework that exploits Computer Vision and the Robot Operating System (ROS) to facilitate pick-and-place operations within robotic cells, offering a comprehensive solution for handling and sorting random-flow objects on conveyor belts. Designed to be easily configured and reconfigured, it accommodates ROS-compatible robotic arms and 3D vision systems, ensuring adaptability to different technological requirements and reducing deployment costs. Experimental results demonstrate the framework’s high precision and accuracy in manipulating and sorting tested objects. Thus, this framework enhances the efficiency and flexibility of industrial robotic systems, making object manipulation more adaptable for unpredictable manufacturing environments.
ISSN:2504-4494
2504-4494
DOI:10.3390/jmmp8050218