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Bioinspired Soft Robot with Incorporated Microelectrodes

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Published on: February 28, 2020

Asymmetric Braided Artificial Muscles with Precise Electrothermal Actuation Control Enabled by Deep Learning.

Wendi Wang¹, Syed Rashedul Islam², Xuan Wang¹

¹State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Textiles, Donghua University, Shanghai 201620, China.

ACS Applied Materials & Interfaces

|May 30, 2025

Summary

This summary is machine-generated.

Researchers developed a novel electrothermal actuator using liquid crystal elastomer fibers and carbon nanotube yarns. This artificial muscle shows high performance in air and water, with precise control using a long short-term memory (LSTM) model.

Keywords:

artificial muscle asymmetric braiding deep learning electrothermal actuation liquid crystal elastomers performance prediction

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Fabrication of Carbon-Based Ionic Electromechanically Active Soft Actuators

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Published on: April 25, 2020

Area of Science:

Materials Science
Robotics
Polymer Science

Background:

Liquid crystal elastomers (LCEs) are promising for artificial muscles but face challenges in performance and control.
Achieving high actuation performance and reliable control under various conditions is crucial for LCE-based actuators.

Purpose of the Study:

To develop a novel fiber-shaped electrothermal actuator by integrating carbon nanotube yarns with LCE fibers.
To enhance the actuator's performance and controllability across different environments (air and water).
To utilize a long short-term memory (LSTM) model for precise prediction and control of actuation strain.

Main Methods:

An asymmetric braiding method using a Maypole braiding machine was employed to create the composite actuator.
The actuator's performance was evaluated in terms of lifting capacity, contraction percentage, and strain rate in both air and water.
A long short-term memory (LSTM) model was developed and trained to predict actuation strain.

Main Results:

The actuator demonstrated exceptional performance, lifting 261 times its weight (0.17 MPa) in 2.5 s with 45% contraction in air.
In water, the actuator achieved 32% contraction within 3 s.
The LSTM model accurately predicted actuation strain with a coefficient of determination (R²) of 0.994, enabling enhanced controllability.

Conclusions:

The novel braided electrothermal actuator exhibits superior performance and adaptability in diverse environments.
The integration of an LSTM model provides precise and programmable control for flexible robotics applications.
This study validates the potential of LCE-based actuators for advanced applications, including music robots and underwater grippers.