INDEX Gripper: Industrial Dexterous Robotic Gripper Capable of All-Orientational Object Manipulation
To advance research and industrial applications of robotic systems, enhancing object manipulation capabilities is crucial. This study introduces the INDEX gripper, an industrial dexterous robotic gripper designed to efficiently grasp objects of various shapes and precisely manipulate their orientations. The gripper features a 3-finger, 9-degree-of-freedom (DOF) linkage-driven mechanism. Its kinematic structure incorporates four four-bar linkages, enabling active 3-DOF motion for each finger. Developed through kinematic analysis, the INDEX gripper is capable of performing multiple grasping postures and accurately manipulating objects in all orientations within its grasp. Experimental results demonstrated a maximum fingertip force of 40.6 N, a payload capacity of 12.2 kg, and a repeatability error of less than 0.0244 mm. The gripper successfully grasped 15 objects of different shapes and manipulated a spherical object by 27.6<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> along the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i>-axis, 34.2<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> along the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i>-axis, and 98.3<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> along the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</i>-axis. In addition, the gripper was tested in two industrial tasks: installing a light bulb and performing a bolting process using a driver. These tasks validated the gripper's effectiveness and reliability in industrial applications.
Authors
- Yooseong Lee
- Uikyum Kim (ORCID: https://orcid.org/0000-0003-2814-5546)
- Joon-Ho Lee (ORCID: https://orcid.org/0000-0002-9438-3051)
- Dong Il Park (ORCID: https://orcid.org/0000-0003-4560-6340)
- Dawoon Jung (ORCID: https://orcid.org/0000-0002-8717-2766)
Institutions
- Korea Institute of Machinery & Materials (KR)
- Ajou University (KR)
Publication Details
- Journal
- IEEE/ASME Transactions on Mechatronics
- Published
- 2024-11-20
- DOI
- https://doi.org/10.1109/tmech.2024.3488817
- Citations
- 7
- Primary Topic
- Robot Manipulation and Learning
- Type
- article
- Field-Weighted Citation Impact
- 2.30
Funders
- National Research Foundation of Korea