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Related Concept Videos

Measurements of Strain01:27

Measurements of Strain

Strain quantifies the deformation of a material under force, typically measured as normal strain, which represents the change in length when compared with the original length. Electrical strain gauges are used for enhanced accuracy. These devices consist of a conductive wire mounted on a paper backing that adheres to the material's surface. These gauges operate on the piezoresistive effect, where the wire's electrical resistance changes in response to mechanical deformation. The strain gauge...

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Updated: Jun 19, 2026

Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers
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Configuration identification of on-demand variable stiffness strain-limiting layers in zig-zag soft pneumatic

Palpolage Don Shehan Hiroshan Gunawardane1, Duhyeon Lee1, Phoebe Cheung1

  • 1Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC, Canada.

The International Journal of Robotics Research
|April 10, 2026
PubMed
Summary

This study introduces a machine learning method to predict soft pneumatic actuator (SPA) configurations for desired trajectories, avoiding costly redesign. This enables versatile robotic applications without re-fabrication.

Keywords:
machine learning techniques in stiffness modulationsoft robotic materials and designssoft roboticssoft sensors and actuatorsstiffness modulationvariable-stiffness strain-limiting layerszig-zag soft pneumatic actuators

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Area of Science:

  • Robotics
  • Materials Science
  • Artificial Intelligence

Background:

  • Soft pneumatic actuators (SPAs) typically have fixed trajectories, requiring redesign for different motions.
  • Achieving multiple trajectories necessitates complex re-fabrication of SPAs.
  • Passive modular variable stiffness SPAs offer adaptability but pose challenges in inverse problem modeling.

Purpose of the Study:

  • To develop a method for predicting strain-limiting layer (SLL) configurations for SPAs to achieve specific tip trajectories.
  • To enable functional and application-specific SPA deployment without structural redesign.
  • To address the challenge of solving the inverse problem for SPAs with modular SLLs.

Main Methods:

  • A hybrid methodology combining feed-forward neural networks and convolutional neural networks was employed.
  • The approach predicts the required SLL configuration for a desired SPA tip trajectory in Cartesian space.
  • The methodology is designed to be generic for various SPA configurations.

Main Results:

  • The proposed method accurately predicted SPA configurations for diverse applications, including endoscopes, grippers, and human finger mimics.
  • An average prediction error of 1.65% was achieved across tested applications.
  • The study demonstrated the feasibility of generating specific trajectories by varying SLL configurations.

Conclusions:

  • A versatile methodology is presented for creating function and application-specific SPAs.
  • Strategic combination of SLLs and machine learning enables trajectory generation without re-fabrication.
  • This approach enhances the adaptability and utility of soft robotic manipulators.