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Dynamic Modeling of a Soft Eversion-Based Growing Robot: Physical Analysis, Simulation, and Experimental Validation.

Abdonoor Kalibala1, Ayman A Nada1, Hiroyuki Ishii2

  • 1Department of Mechatronics and Robotics Engineering, Egypt-Japan University of Science and Technology, E-JUST, Alexandria, Egypt.

Soft Robotics
|April 24, 2026
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Summary

Researchers developed a dynamic model for soft growing robots, inspired by plants. This model accurately predicts robot growth by including internal pressure and air supply dynamics, crucial for navigating complex environments.

Keywords:
bio-inspired robotdynamic modeling and parameter identificationeversion robotssoft growing robotssoft robots

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Area of Science:

  • Robotics
  • Mechanical Engineering
  • Biomimetics

Background:

  • Soft growing robots, or vine robots, use tip extension for locomotion, mimicking climbing plants.
  • Their application in complex environments is hindered by limited understanding of their dynamics.
  • Existing models often neglect internal pressure and pneumatic supply system dynamics.

Purpose of the Study:

  • To develop a first-principles-based dynamic model for pressure-driven soft eversion-based growing robots.
  • To incorporate internal pressure evolution and pneumatic supply system flow dynamics into the model.
  • To validate the model through simulation and experimental testing.

Main Methods:

  • Developed a physics-based dynamic model for soft eversion-based growing robots.
  • Integrated internal pressure dynamics and pneumatic flow dynamics into the model.
  • Simulated the model and validated it using custom-built soft growing robots.

Main Results:

  • The dynamic model demonstrated excellent predictive capability for robot growth.
  • Achieved a root mean square error (RMSE) of 0.066 m, approximately 5.5% of the final everted length.
  • Experimental validation confirmed the model's accuracy.

Conclusions:

  • Integrating pressure and flow dynamics is critical for accurate modeling of soft eversion-based growing robots.
  • The developed model provides improved insight into the physical behavior of these robots.
  • This work enables enhanced control strategies for soft growing robots in challenging environments.