# Fluid Pressure Sensing Strategy Suitable for Swallowing Soft Gripper

**Authors:** Mingge Li, Wenxi Zhang, Quan Liu, Zhongjun Yin

PMC · DOI: 10.3390/s26030960 · Sensors (Basel, Switzerland) · 2026-02-02

## TL;DR

This paper introduces a fluid pressure sensing method for a soft gripper that can adapt to objects of different shapes by detecting changes in internal pressure during the grasping process.

## Contribution

The novel contribution is a fluid pressure sensing strategy for swallowing-type soft grippers that enables object classification and size sorting through volume-pressure modeling.

## Key findings

- A volume-pressure variation model was derived for the sealed cavity of the soft gripper.
- The sensing method successfully enabled closed-loop control for object classification and sorting.
- The strategy allows the gripper to adaptively grasp objects based on their shape and size.

## Abstract

Soft grippers exhibit excellent adaptability in handling objects of various shapes. However, due to the large deformation and high compliance of their constituent materials, the integration of sensing capabilities has long been a major research challenge. Based on the swallowing-type soft gripper proposed in previous work, this study explores the gripper’s capability to perceive object information by leveraging the characteristic that the sealed cavity undergoes volume change due to compression by the object during swallowing, thereby altering the pressure of the internal fluid medium. By establishing the geometric configuration of the sealed cavity composed of elastic membranes, the volume-pressure variation sensing model during the object swallowing process was derived. The performance of this sensing method was tested, and the application of the fluid pressure sensing strategy in closed-loop control was demonstrated, including the classification of objects by shape and sorting by size. This work provides a solution for the object shape-adaptive swallowing-type soft gripper to achieve sensory grasping functionality.

## Full-text entities

- **Chemicals:** Gripper (-)

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12899872/full.md

## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12899872/full.md

## References

27 references — full list in the complete paper: https://tomesphere.com/paper/PMC12899872/full.md

---
Source: https://tomesphere.com/paper/PMC12899872