Author:
Tawk Charbel,Mutlu Rahim,Alici Gursel
Abstract
A single universal robotic gripper with the capacity to fulfill a wide variety of gripping and grasping tasks has always been desirable. A three-dimensional (3D) printed modular soft gripper with highly conformal soft fingers that are composed of positive pressure soft pneumatic actuators along with a mechanical metamaterial was developed. The fingers of the soft gripper along with the mechanical metamaterial, which integrates a soft auxetic structure and compliant ribs, was 3D printed in a single step, without requiring support material and postprocessing, using a low-cost and open-source fused deposition modeling (FDM) 3D printer that employs a commercially available thermoplastic poly (urethane) (TPU). The soft fingers of the gripper were optimized using finite element modeling (FEM). The FE simulations accurately predicted the behavior and performance of the fingers in terms of deformation and tip force. Also, FEM was used to predict the contact behavior of the mechanical metamaterial to prove that it highly decreases the contact pressure by increasing the contact area between the soft fingers and the grasped objects and thus proving its effectiveness in enhancing the grasping performance of the gripper. The contact pressure can be decreased by up to 8.5 times with the implementation of the mechanical metamaterial. The configuration of the highly conformal gripper can be easily modulated by changing the number of fingers attached to its base to tailor it for specific manipulation tasks. Two-dimensional (2D) and 3D grasping experiments were conducted to assess the grasping performance of the soft modular gripper and to prove that the inclusion of the metamaterial increases its conformability and reduces the out-of-plane deformations of the soft monolithic fingers upon grasping different objects and consequently, resulting in the gripper in three different configurations including two, three and four-finger configurations successfully grasping a wide variety of objects.
Funder
University of Wollongong
Centre of Excellence for Electromaterials Science, Australian Research Council
Subject
Artificial Intelligence,Computer Science Applications
Cited by
22 articles.
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