Hyper‐Versatile Gripping: Synergizing Mechanical and Machine Intelligence of a Hybrid Robotic Gripper

Abstract

Integration of robotic solutions in manufacturing sector is growing. However, it is still concentrated in certain industries (i.e., electronics and automotive) where standardization of product physical form is high. Current state‐of‐the‐art gripping solutions fall short when they need to accommodate items with high variability in physical form. This challenging scenario for automation can be found in a few industries (i.e., e‐commerce). Automation of pick‐and‐place processes in this area requires a more versatile gripping solution. To resolve this challenge, this article proposes a novel way to improve grip‐versatility by synergizing the mechanical and machine intelligence of a hybrid robotic gripper (HRG). Comparative analysis with commercial grippers shows that HRG can pick a more diverse range of items with success rate 94.78%. Visual perception‐based picking strategy is developed to automate the reconfiguration of HRG into a stable grasp pose for different objects. Using the proposed reconfigurable picking strategy, the efficacy of HRG in pick‐and‐place tasks is evaluated using three parameters—mean pick per hour (MPPH), successful execution over total attempts (SETA), and average cycle time (AVGCT). HRG can effectively pick items in cluttered workspace with MPPH of 98.54 ± 15.49, SETA of 0.93 ± 0.11, and AVGCT of 34.76 ± 3.31 s.

Keywords

• grippers
• machine learning
• perception
• soft robot control