Browsing by Author "Bueno, Mikel"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Rapid and automated configuration of robot manufacturing cells
    (Elsevier, 2025-04-01) Asif, Seemal; Bueno, Mikel; Ferreira, Pedro; Anandan, Paul; Zhang, Ze; Yao, Yue; Ragunathan, Gautham; Tinkler, Lloyd; Sotoodeh-Bahraini, Masoud; Lohse, Niels; Webb, Phil; Hutabarat, Windo; Tiwari, Ashutosh
    This study presents the Reconfigurable and Responsive Robot Manufacturing (R3M) architecture, a novel framework engineered to autonomously adapt to fluctuating product variants and demands within manufacturing environments. At the heart of R3M lies an integrated architecture that ensures a seamless data flow between critical modules, facilitated by an advanced communication platform. These modules are central to delivering a range of services crucial for operational efficiency. Key to the architecture is the incorporation of Automated Risk Assessment aligned with ISO-12100 standards, utilizing ROS2 Gazebo for the dynamic modification of robot skills in a plug-and-produce manner. The architecture's unique approach to requirements definition employs AutomationML (AML), enabling effective system integration and the consolidation of varied information sources. This is achieved through the innovative use of skill-based concepts and AML Class Libraries, enhancing the system's adaptability and integration within manufacturing settings. The narrative delves into the intricate descriptions of products, equipment, and processes within the AML framework, highlighting the strategic consideration of profitability in the product domain and distinguishing between atomic and composite skills in equipment characterization. The process domain serves as an invaluable knowledge repository, bridging the gap between high-level product demands and specific equipment capabilities via process patterns. The culmination of these elements within the R3M framework provides a versatile and scalable solution poised to revolutionize manufacturing processes. Empirical results underscore the architecture's robust perception abilities, with a particular focus on a real-world application in robotic lamination stacking, elucidating both the inherent challenges and the tangible outcomes of the R3M deployment.
  • Thumbnail Image
    ItemOpen Access
    Unlocking the potential of robot manipulators: seamless integration framework
    (IEEE, 2024-08-28) Bueno, Mikel; Bernardino, Irene; Asif, Seemal; Webb, Phil
    This 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.