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In many practical and theoretical contexts, the exact value of a definite integral may be inaccessible. This limitation typically arises when the antiderivative of a function is either unknown or cannot be expressed in a closed mathematical form. Alternatively, it can occur when a function is defined not by a formula but by a finite set of empirical data points, such as those collected during experiments. In these cases, approximate integration techniques provide a valuable solution.One of the...
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Updated: Feb 6, 2026

Rapid Manufacturing of Thin Soft Pneumatic Actuators and Robots
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Modular force approximating soft robotic pneumatic actuator.

Austin J Taylor1, Rudy Montayre1, Zhuo Zhao1

  • 1School of Electrical and Computer Engineering, The University of Georgia, 597 DW Brooks Dr., Athens, GA, 30602, USA.

International Journal of Computer Assisted Radiology and Surgery
|August 9, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel modular soft robotic pneumatic actuator for compliant endoscopic devices. Its unique design allows for a 145° bend, with machine learning enabling precise control for surgical navigation.

Keywords:
Electromagnetic trackingImage processingNeural networkSoft robot

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Materials Science

Background:

  • Soft robots offer enhanced flexibility and maneuverability, crucial for surgical applications.
  • Endoscopic devices require compliant instruments for navigating complex internal body structures.
  • Existing endoscopic tools can be limited in their ability to reach difficult anatomical areas.

Purpose of the Study:

  • To present a modular soft robotic pneumatic actuator as a proof of concept for a compliant endoscopic device.
  • To demonstrate the actuator's potential for improved maneuverability in minimally invasive procedures.
  • To develop a rapidly prototyped and precisely controlled soft robotic solution for endoscopy.

Main Methods:

  • The soft robotic actuator was fabricated using fused deposition modeling (FDM) 3D printing.
  • Bending angle was quantified via image processing (MATLAB), and end-effector displacement was measured using electromagnetic tracking.
  • Actuator performance was evaluated under varying pneumatic pressures (10-35 psi) and uniaxial tensile loads (0-120 g).

Main Results:

  • The actuator achieved a maximum bending angle of 145 degrees.
  • Polynomial regression (R²=0.998) and a feedforward neural network (R²=0.996) accurately modeled actuator displacement under load.
  • The neural network demonstrated robust computer-assisted control for precise manipulation.

Conclusions:

  • A novel modular soft robotic pneumatic actuator was successfully developed and validated.
  • The actuator's inherent curled shape facilitates simple manufacturing and superior bending capabilities.
  • The integration of machine learning enables accurate prediction and precise control, enhancing its utility in endoscopic applications.