Related Experiment Video
Updated: Jun 27, 2026
07:49
Rod-based Fabrication of Customizable Soft Robotic Pneumatic Gripper Devices for Delicate Tissue Manipulation
Published on: August 2, 2016
Size Estimation of Grasped Objects Using a Soft Pneumatic Gripper Integrated with a Piezoresistive CNT/PDMS Sensor
Wongi Hong1, Jaehoon Jeong1, Kun-Woo Nam1
1Department of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-Gu, Seoul 06978, Republic of Korea.
Micromachines
|June 26, 2026
Summary
This study introduces a soft pneumatic gripper with a novel sensor for estimating object size. The gripper uses sensor signals to determine size, advancing soft robotics capabilities.
Area of Science:
- Robotics
- Materials Science
- Sensor Technology
Background:
- Soft pneumatic grippers offer compliant grasping but lack quantitative size estimation.
- Existing methods often rely on external visual feedback, limiting adaptability.
Purpose of the Study:
- To develop and validate a soft pneumatic gripper capable of estimating object size using only integrated sensor signals.
- To investigate the use of a piezoresistive Carbon Nanotube/Polydimethylsiloxane (CNT/PDMS) composite sensor for this purpose.
Main Methods:
- A soft pneumatic gripper was integrated with a CNT/PDMS composite sensor.
- The sensor's pressure-sensing characteristics were evaluated.
- Object size estimation was performed by analyzing the sensor's normalized resistance response to applied pneumatic pressure, correlating initial contact pressure with object diameter.
Main Results:
- The 1 wt% CNT/PDMS sensor demonstrated high sensitivity in the 0-500 kPa range.
- Distinct resistance response patterns correlated with different object sizes.
- Larger objects induced significant resistance changes at lower applied pressures, enabling size differentiation.
Conclusions:
- The study successfully demonstrates the feasibility of estimating grasped object size using only sensor-based feedback from a soft pneumatic gripper.
- This approach lays the groundwork for soft robotic systems with enhanced environmental perception and adaptive manipulation capabilities.
