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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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Journal of Manufacturing and Materials Processing |
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| Online Access: | https://www.mdpi.com/2504-4494/8/5/218 |
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| author | Eber L. Gouveia John G. Lyons Declan M. Devine |
| author_facet | Eber L. Gouveia John G. Lyons Declan M. Devine |
| author_sort | Eber L. Gouveia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9c1454cc5e9947fa8d39a9d3b5f034a0 |
| institution | OA Journals |
| issn | 2504-4494 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj-art-9c1454cc5e9947fa8d39a9d3b5f034a02025-08-20T02:11:11ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942024-09-018521810.3390/jmmp8050218Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor BeltsEber L. Gouveia0John G. Lyons1Declan M. Devine2PRISM Research Institute, Technological University of the Shannon, Midlands Midwest, N37 HD68 Athlone, IrelandFaculty of Engineering and Informatics Technological, University of the Shannon, Midlands Midwest, N37 HD68 Athlone, IrelandPRISM Research Institute, Technological University of the Shannon, Midlands Midwest, N37 HD68 Athlone, IrelandThe 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.https://www.mdpi.com/2504-4494/8/5/218computer visionrandom flowpick-and-place taskrobot operating system |
| spellingShingle | Eber L. Gouveia John G. Lyons Declan M. Devine Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor Belts Journal of Manufacturing and Materials Processing computer vision random flow pick-and-place task robot operating system |
| title | Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor Belts |
| title_full | Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor Belts |
| title_fullStr | Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor Belts |
| title_full_unstemmed | Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor Belts |
| title_short | Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor Belts |
| title_sort | implementing a vision based ros package for reliable part localization and displacement from conveyor belts |
| topic | computer vision random flow pick-and-place task robot operating system |
| url | https://www.mdpi.com/2504-4494/8/5/218 |
| work_keys_str_mv | AT eberlgouveia implementingavisionbasedrospackageforreliablepartlocalizationanddisplacementfromconveyorbelts AT johnglyons implementingavisionbasedrospackageforreliablepartlocalizationanddisplacementfromconveyorbelts AT declanmdevine implementingavisionbasedrospackageforreliablepartlocalizationanddisplacementfromconveyorbelts |