Development of a small batch flexible station for packaging waste sorting in circular economy applications

Efficient packaging waste sorting is an important requirement for implementing circular economy strategies, yet many automated sorting solutions are designed for large-scale, high-throughput recycling facilities and are not well suited to small-batch industrial environments characterized by limited waste volumes, frequent material changes, and the need for rapid system reconfiguration.

This paper presents the design, implementation, and laboratory validation of a modular collaborative robotic workstation for small-batch packaging waste sorting. The proposed system integrates a collaborative robot, RGB-D vision system, conveyor-based material flow, and a ROS2-based control architecture within a unified perception-decision-manipulation framework. The perception pipeline combines class-agnostic segmentation using the Segment Anything Model (SAM) with lightweight MobileNetV2-based classification for material recognition and is extended with optional modules for geometric reasoning and damage assessment. The proposed architecture reduces dependence on task-specific segmentation training and provides semantic information relevant to grasp planning, safety-aware task allocation, and human-robot collaboration.

The workstation was validated under controlled laboratory conditions using three representative packaging waste categories: plastic bottles, aluminium cans, and Tetra Pak containers. The SAM + MobileNetV2 perception pipeline achieved an accuracy of 0.93, precision of 0.93, recall of 0.92, and F1-score of 0.92. System-level experiments demonstrated successful integration of RGB-D object localization, conveyor tracking, camera-to-robot coordinate transformation, and robotic pick-and-place execution. In contrast to conventional high-throughput sorting systems, the proposed workstation emphasizes modularity, flexibility, and collaborative operation for small-batch industrial packaging waste streams. In addition, workstation-level manipulation tests over 200 grasp attempts resulted in a 92.0% grasp success rate, an 89.0% end-to-end sorting success rate, and an estimated sorting rate of 5.73 picks/min under controlled conveyor-based conditions.

The results demonstrate the feasibility of integrating foundation-model-based perception with collaborative robot in a modular workstation intended for small-batch industrial packaging waste sorting. The proposed approach emphasizes flexibility, modularity, and reduced reconfiguration effort for variable waste streams. The results provide a basis for further development toward adaptive and human-robot collaborative sorting, while broader validation under more diverse, cluttered, and industrially representative conditions remains necessary.