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Robotic bin-picking : benchmarking robotics grippers with modified YCB object and model setTone Lerher,
Primož Bencak,
Luka Bizjak,
Darko Hercog,
Boris Jerman, 2023, published scientific conference contribution
Abstract: Robotic bin-picking is increasingly important in the order-picking process in intralogistics. However, many aspects of the robotic bin-picking process (object detection, grasping, manipulation) still require the research community's attention. Established methods are used to test robotic grippers, enabling comparability of the research community's results. This study presents a modified YCB Robotic Gripper Assessment Protocol that was used to evaluate the performance of four robotic grippers (twofingered, vacuum, gecko, and soft gripper). During the testing, 45 objects from the modified YCB Object and Model Set from the packaging categories, tools, small objects, spherical objects, and deformable objects were grasped and manipulated. The results of the robotic gripper evaluation show that while some robotic grippers performed substantially well, there is an expressive grasp success variation over diverse objects. The results indicate that selecting the object grasp point next to selecting the most suitable robotic gripper is critical in successful object grasping. Therefore, we propose grasp point determination using mechanical software simulation with a model of a two-fingered gripper in an ADAMS/MATLAB cosimulation. Performing software simulations for this task can save time and give comparable results to real-world experiments.
Keywords: intralogistics, robotic bin-picking, YCB protocol, robotic gripper evaluation, mechanical software simulations, performance analysis
Published in DKUM: 21.03.2024; Views: 298; Downloads: 15
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