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CFD-based multi-objective controller optimization for soft robotic fish with muscle-like actuation.

Andrew Hess1, Xiaobo Tan, Tong Gao

  • 1Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48824, United States of America. Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, United States of America.

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Researchers developed a computational framework to optimize soft robotic fish controllers. This method uses artificial muscle actuation and advanced fluid-structure interaction simulations for efficient swimming maneuvers.

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Area of Science:

  • Robotics
  • Fluid Dynamics
  • Control Systems

Background:

  • Soft robots offer unique capabilities due to nonlinear dynamics, but their complex behaviors, especially in fluid environments, pose significant design and control challenges.
  • Soft robotic swimmers face difficulties due to highly nonlinear fluid-structure interactions, limiting their maneuverability and efficiency.

Purpose of the Study:

  • To present a computational framework for optimizing feedback controllers for soft robotic fish maneuvers.
  • To enable efficient swimming gaits like forward motion and turning using localized active strains.
  • To investigate trade-offs between tracking error, cost of transport, and elastic strain energy in soft swimmers.

Main Methods:

  • A multi-objective evolutionary method was employed to optimize proportional-integral-derivative (PID) controllers.
  • Fluid-structure interactions were solved using a novel fictitious domain/active strain method for a 2D elastic plate.
  • Active contractile strains were imposed locally on the elastic body to generate swimming gaits.

Main Results:

  • Localized active strains effectively produced various swimming gaits with fewer control parameters than traditional methods.
  • Optimized PID controllers demonstrated effective target tracking across different trajectories and Reynolds numbers.
  • Pareto fronts revealed correlations and trade-offs among tracking error, cost of transport, and elastic strain energy.

Conclusions:

  • The developed framework provides a holistic approach to designing and controlling soft robotic swimmers.
  • Imposing local active strains is an efficient strategy for achieving complex maneuvers in soft robots.
  • The study offers key insights into the multi-objective optimization of soft robotic fish control systems.