Unlocking the potential of robot manipulators: seamless integration framework

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dc.contributor.authorBueno, Mikel
dc.contributor.authorBernardino, Irene
dc.contributor.authorAsif, Seemal
dc.contributor.authorWebb, Phil
dc.date.accessioned2024-11-21T15:36:10Z
dc.date.available2024-11-21T15:36:10Z
dc.date.freetoread2024-11-21
dc.date.issued2024-08-28
dc.date.pubOnline2024-10-23
dc.descriptionThe source code, testing results and videos are available in IFRA-Cranfield’s GitHub
dc.description.abstractThis study introduces a groundbreaking framework designed to enhance the adaptability and efficiency of robot manipulators in manufacturing, leveraging ROS 2 and a modular middleware to transcend traditional robotic constraints. The framework's efficacy is exemplified through a pick-and-place task, serving not merely as a demonstration but as robust evidence of the framework's ability to enable complex object manipulation tasks far beyond repetitive activities. By integrating advanced perception capabilities with a YOLOv8-based object detection model and an OpenCV-based pose estimation module, the framework showcases a seamless interaction between sophisticated software tools and robotic hardware. This integration not only simplifies the incorporation of intelligent components into robotic systems but also significantly broadens their applicability and enhances efficiency across diverse tasks and applications. The use-case, therefore, stands as compelling evidence of the framework's potential to revolutionize industrial and collaborative robotics, providing a unified and adaptable platform for the development, testing, and deployment of robotic solutions in modern manufacturing settings. The research aims to simplify the integration of intelligent components into robotic systems, thereby extending their utility and efficiency across a broader range of tasks and applications, ultimately advancing the capabilities of industrial and collaborative robotics for modern manufacturing needs.
dc.description.conferencename2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)
dc.description.sponsorshipEngineering and Physical Sciences Research Council
dc.description.sponsorshipThis research is supported by EPSRC grant (EP/V051180/1) for the Reconfigurable Robotics for Responsive Manufacture - R3M Project
dc.format.extentpp. 2610-2617
dc.identifier.citationBueno M, Bernardino I, Asif S, Webb P. (2024) Unlocking the potential of robot manipulators: seamless integration framework. In: 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE). 28 August 2024 - 1 September 2024, Bari, Italy, pp. 2610-2617
dc.identifier.eissn2161-8089
dc.identifier.elementsID558581
dc.identifier.issn2161-8070
dc.identifier.urihttps://doi.org/10.1109/case59546.2024.10711476
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23199
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10711476
dc.relation.isreferencedbyhttps://github.com/IFRA-Cranfield/irb120_PoseEstimation
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subject9 Industry, Innovation and Infrastructure
dc.titleUnlocking the potential of robot manipulators: seamless integration framework
dc.typeConference paper
dcterms.coverageBari, Italy
dcterms.temporal.endDate1 Sep 2024
dcterms.temporal.startDate28 Aug 2024

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