PneuNet Based Hybrid Soft Gripper for Multi-Shape Object Handling

Soft robotic grippers are emerging as a promising solution for handling complex objects, offering enhanced flexibility in handling various tasks with a single gripper. This study presents a novel soft gripper which uses PneuNet based hybrid actuation to improve the grasping success in scenarios invo...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.190158-190168
Main Authors: Tennakoon, Chanuka L., Kulasekera, Asitha L., Gopura, R. A. R. C., Chathuranga, Damith S.
Format: Article
Language:English
Subjects:
Actuation
Actuators
Force
Grasping
Grasping (robotics)
Grasping force
Grippers
Handling
Identification methods
Jamming
Layer jamming actuator
Pneumatic actuators
PneuNet actuator
Shape
soft robotic gripper
Soft robotics
Success
Task complexity
Tendons
Three-dimensional printing
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Soft robotic grippers are emerging as a promising solution for handling complex objects, offering enhanced flexibility in handling various tasks with a single gripper. This study presents a novel soft gripper which uses PneuNet based hybrid actuation to improve the grasping success in scenarios involving multi-shape object handling. The proposed actuator integrates a layer jamming element (LJE) to replace the non-extensible layer in a standard PneuNet actuator, simultaneously acting as a locking mechanism capable of maintaining the bending angle without the support from PneuNet. Evaluation of the LJE's impact revealed that the use of a single LJE increased grasping force by up to 20%, while employing multiple LJEs showed no significant improvement. The success rates of three grasping modes, flat/parallel, spherical, and cylindrical were assessed using the proposed reconfigurable gripper. Experiments showed success rates of 62%, 68%, and 56% for grasping, and 54%, 53%, and 53% for jamming hold, respectively. By selecting an ideal grasping mode for each object, average success rates increased to 90% in grasping and 84% in jamming hold. The proposed vision-based grasping mode identification method selected the optimal mode based on object size and shape, leading to improved success rates of 96% in grasping and 86% in jamming hold.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3515265