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Updated: Sep 6, 2025

Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery
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Control-Oriented Models for Hyperelastic Soft Robots Through Differential Geometry of Curves.

Brandon Caasenbrood1, Alexander Pogromsky1, Henk Nijmeijer1

  • 1Dynamics and Control Group, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.

Soft Robotics
|June 24, 2022
PubMed
Summary

This study introduces a new dynamic model for soft robots, integrating complex material properties for accurate and efficient control. The developed controller enhances robustness against uncertainties, enabling real-time simulations and improved robot performance.

Keywords:
continuum robothyper-redundant robotsphysical simulationsoft manipulation

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Area of Science:

  • Robotics
  • Materials Science
  • Control Theory

Background:

  • Soft robots require accurate, computationally efficient models for control.
  • Existing models often lack detailed material behavior integration.
  • Bridging material and control research is crucial for soft robot advancement.

Purpose of the Study:

  • To develop a dynamic model for soft robots incorporating hyperelastic and viscoelastic material properties.
  • To enable real-time simulations and enhance control strategies for soft robots.
  • To validate the model and controller through simulations and experimental data.

Main Methods:

  • Derived continuum dynamics using differential geometry of spatial curves.
  • Integrated finite-element data for geometric and material nonlinearities.
  • Introduced a reduced-order integration scheme for efficient dynamic Lagrangian matrix computation.
  • Developed a passivity-based adaptive controller leveraging hyperelastic model parameterization.

Main Results:

  • Achieved real-time (multilink) soft robot models with high numerical precision.
  • Demonstrated enhanced robustness of the controller against material uncertainty and unmodeled dynamics.
  • Validated the dynamic model with an additively manufactured soft robot manipulator under various conditions.
  • Showcased good correspondence between model predictions and experimental results.

Conclusions:

  • The proposed framework accurately captures soft robot dynamics with complex material behaviors.
  • The passivity-based adaptive controller offers robust performance for real-time applications.
  • The study provides a solid foundation for advanced control of soft robots through integrated modeling and experimentation.