Experimental Study on Autonomous Food Packaging with Delta Parallel Robot and Two Fingered Gripper

In response to the increasing supply and demand in the food industry, automated food packaging has emerged as a substitute for the manual labor that was previously required. The surge in automation is evident, with robotics playing a pivotal role in tasks ranging from sorting and packaging to labeling, and notably, pick-and-place operations which is the specific focus of this study. Leveraging robots for these tasks not only improves precision and speed but also takes packaging hygiene into account, resulting in a substantial cost reduction and heightened efficiency. Delta Parallel Robots are among the robotic systems well-suited for high-speed pick-and-place tasks. In this experimental study, the focus is on packaging several scattered chocolates, where both the container box and the chocolates will be randomly positioned and oriented. This is achieved by employing a Delta Parallel Robot paired with a two-fingered gripper, designed to incur minimal damage to the product when compared to other types of end-effectors. The positioning of the box and products will be determined through a classical image processing approach, specifically Edge Detection and Hough Transform, implemented using the OpenCV Python library. Following identification, the objects are localized through camera-robot calibration, and the resulting coordinates are transmitted to the robot as target points. Extensive testing has demonstrated that this study, employing visual perception for pick-and-place tasks, achieved a success rate of 82%. This study showcases the potential of automating packaging tasks in the food industry through the integration of robotic systems and artificial intelligence. The approach proves effective across diverse tasks, encompassing the packaging of variously shaped food items and drugs, applicable across different industries.