Document Type
Research Paper
Publication Date
Summer 6-21-2023
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 (two-fingered, 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 co-simulation. Performing software simulations for this task can save time and give comparable results to real-world experiments.
Publication Title
Progress in Material Handling Research
Recommended Citation
Lerher, Tone; Bencak, Primož; Hercog, Darko; Jerman, Boris; and Bizjak, Luka, "Robotic bin-picking: Benchmarking robotics grippers with modified YCB object and model set" (2023). 16th Proceedings (Dresden, Germany- 2023). 9.
https://digitalcommons.georgiasouthern.edu/pmhr_2023/9
