SurgGrip: a compliant 3D printed gripper for vision-based grasping of surgical thin instruments. 2022

Jaeseok Kim, and Anand Kumar Mishra, and Lorenzo Radi, and Muhammad Zain Bashir, and Olivia Nocentini, and Filippo Cavallo
BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.

This paper presents a conceptual design and implementation of a soft, compliant 3D printed gripper (SurgGrip), conceived for automated grasping of various surgery-based thin-flat instruments. The proposed solution includes (1) a gripper with a resilient mechanism to increase safety and better adaptation to the unstructured environment; (2) flat fingertips with mortise and tenon joint to facilitate pinching and enveloping based grasping of thin and random shape tools; (3) a soft pad on the fingertips to enable the high surface area to maintain stable grasping of the surgical instruments; (4) a four-bar linkage with a leadscrew mechanism to provide a precise finger movement; (5) enable automated manipulation of surgical tools using computer vision. Our gripper model is designed and fabricated by integrating soft and rigid components through a hybrid approach. The SurgGrip shows passive adaptation through inherent compliance of linear and torsional spring. The four-bar linkage mechanism controlled by a motor-leadscrew-nut drive provides precise gripper opening and closing movements. The experimental results show that the SurgGrip can detect, segment through a camera, and grasp surgical instruments (maximum 606.73 gs), with a 67% success rate (grasped 10 out of 12 selected tools) at 3.21 mm/s grasping speed and 15.81 s object grasping time autonomously. Besides, we demonstrated the pick and place abilities of SurgGrip on flat and nonflat surfaces in real-time. The online version contains supplementary material available at 10.1007/s11012-022-01594-6.

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