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Updated: Oct 20, 2025

Four-Dimensional Printing of Stimuli-Responsive Hydrogel-Based Soft Robots

Four-Dimensional Printing of Stimuli-Responsive Hydrogel-Based Soft Robots

Published on: January 13, 2023

A Multimodal Hydrogel Soft-Robotic Sensor for Multi-Functional Perception.

Yu Cheng^1,2,3,4, Runzhi Zhang⁵, Wenpei Zhu^1,2,3

¹Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China.

Frontiers in Robotics and AI

|September 13, 2021

Summary

This summary is machine-generated.

This study introduces a new solid-state soft sensor for extreme environments. The multimodal sensor uses hydrogel and silicone for robust, large-deformation sensing and temperature estimation.

Keywords:

electrical and optical properties hydrogel multifunctional perception multimodal soft sensor

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Area of Science:

Robotics
Materials Science
Sensor Technology

Background:

Soft robots are ideal for extreme environments due to their adaptability and resilience.
Sensing in harsh conditions, especially with large deformations and temperature fluctuations, remains a significant challenge for soft robots.
Current liquid-metal soft sensors face limitations in balancing large deformation capabilities with environmental robustness.

Purpose of the Study:

To develop a novel multimodal solid-state soft sensor for extreme environment applications.
To overcome the limitations of existing soft sensing technologies.
To enable multifunctional perception in soft robots under challenging conditions.

Main Methods:

Proposed a multimodal solid-state soft sensor integrating hydrogel and silicone.
Exploited hydrogel's conductance and transparency for combined optical and resistive sensing.
Developed a dedicated neural network for sensory information extraction from the multimodal sensor.
Fabricated prototype sensors and conducted experiments involving stretching, twisting, and temperature variations.

Main Results:

The novel sensor successfully measured stretching and twisting under large deformations.
Simultaneous estimation of environmental temperatures was achieved by leveraging distinct responses to temperature changes.
The multimodal approach demonstrated effective multifunctional perception capabilities.
Prototype sensors showed promising performance in simulated extreme conditions.

Conclusions:

The proposed solid-state multimodal soft sensor offers a promising solution for sensing in extreme environments.
This approach overcomes the trade-offs of existing soft sensors, enhancing real-world applicability.
The technology enables robust, multifunctional perception for soft robots operating under harsh conditions.