In this work, we discuss the design of soft robotic fingers for robust precision grasping. Through a conceptual analysis of the finger shape and compliance during grasping, we confirm that antipodal grasps are more stable when contact with the object occurs on the side of the fingers (i.e., pinch grasps) instead of the fingertips. In addition, we show that achieving such pinch grasps with soft fingers for a wide variety of objects requires at least two independent bending segments each, but only requires actuation in the proximal segment. Using a physical prototype hand, we evaluate the improvement in pinch-grasping performancemore »
This content will become publicly available on August 1, 2022
A Novel Variable Stiffness Soft Robotic Gripper
Compliant grasping is crucial for secure handling
objects not only vary in shapes but also in mechanical properties.
We propose a novel soft robotic gripper with decoupled
stiffness and shape control capability for performing adaptive
grasping with minimum system complexity. The proposed soft
fingers conform to object shapes facilitating the handling of
objects of different types, shapes, and sizes. Each soft gripper
finger has a length constraining mechanism (an articulable
rigid backbone) and is powered by pneumatic muscle actuators.
We derive the kinematic model of the gripper and use an
empirical approach to simultaneously map input pressures
to stiffness control and bending deformation of fingers. We
use these mappings to demonstrate decoupled stiffness and
shape (bending) control of various grasping configurations. We
conduct tests to quantify the grip quality when holding objects
as the gripper changes orientation, the ability to maintain the
grip as the gripper is subjected to translational and rotational
movements, and the external force perturbations required to
release the object from the gripper under various stiffness
and shape (bending) settings. The results validate the proposed
gripper’s performance and show how the decoupled stiffness
and shape control can improve the grasping quality in soft
robotic grippers.
- Award ID(s):
- 1718075
- Publication Date:
- NSF-PAR ID:
- 10296223
- Journal Name:
- IEEE International Conference on Automation Science and Engineering CASE
- Page Range or eLocation-ID:
- 2222-2227
- ISSN:
- 2161-8070
- Sponsoring Org:
- National Science Foundation
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