Conceptualisation and Implementation of a ROS-Based Robotic Cell for a Flexible Pre-Assembly Task

Modern manufacturing is currently shifting toward highly flexible, high-mix, and low-volume production cycles, requiring small and medium-sized enterprises (SMEs) to adopt reconfigurable automation to remain competitive. However, the adoption of such technologies is often slowed down by the high cost and rigidity of commercial solutions, which typically rely on proprietary toolchains and necessitate specialized expert knowledge for reconfiguration. This study proposes a methodological framework for a modular robotic cell based on an open-architecture approach using ROS2 middleware, designed to be maintained by personnel without deep robotics expertise. The methodology emphasizes the replacement of fixed mechanical fixtures with an AI-driven perception pipeline, utilizing YOLO-based image segmentation to enable the autonomous localization of heterogeneous components. A rigorous tolerance chain analysis defines the design requirements of custom 3D-printed self-aligning fingertips, providing a mathematical and mechanical basis for ensuring assembly feasibility under tight geometric constraints. By adopting a node-based software topology, the framework facilitates rapid task reconfiguration and hardware interoperability. Experimental validation in an industrial-like environment confirms that this integrated approach provides a scalable pathway with the potential to improve cost-effectiveness in high-mix low-volume production scenarios to overcome manual production bottlenecks through intelligent, reconfigurable automation.