A Prototype Pick and Place Solution for Harvesting White Button Mushrooms Using a Collaborative Robot

Abstract

Mushroom harvesting is a labor-intensive process. Implementing intelligent automation can improve the work environment for harvesters, reducing work-related musculoskeletal disorders caused by repetitive movements and awkward postures. This research prototypes a robotic harvesting solution for white button mushrooms using a systematic approach. The fungi growth cycle, bruising characteristics, and picking motion dynamics were all considered. To establish a bruising threshold, compression loads were incrementally applied to mushrooms until visible damage occurred. A force measurement glove was used to collect data from harvesters at a mushroom farm to determine the average force exerted on a mushroom during picking. Computer vision-based motion analysis was performed to define picking dynamics. Several compliant grippers were designed, simulated in Autodesk Inventor Nastran, and realized via 3D printing. Prototypes underwent durability testing using iterative cycle counts of 100 as well as moisture absorption testing to study performance in high humidity. A collaborative robot with specialty end of arm tooling was explored to harvest mature mushrooms from a selected area, validating the automation strategy. This system repeatedly located, picked, and placed mushrooms without damage. Recommendations for future work include a decision-making algorithm for mushroom grading preharvest and refinement of gripper tips.