Tunable stiffness enables fast and efficient swimming in fish-like robots

ORAL

Abstract

Fish maintain high swimming efficiencies over a wide range of speeds. A key to this achievement is their flexibility, yet even flexible robotic fish trail real fish in terms of performance. Here, we explore how fish leverage tunable flexibility by using their muscles to modulate the stiffness of their tails to achieve efficient swimming. We derived a model that explains how and why tuning stiffness affects performance. We show that to maximize efficiency, muscle tension should scale with swimming speed squared, offering a simple tuning strategy for fish-like robots. Tuning stiffness can double swimming efficiency at tuna-like frequencies and speeds (0 to 6 hertz; 0 to 2 body lengths per second). Energy savings increase with frequency, suggesting that high-frequency fish-like robots have the most to gain from tuning stiffness.

*This work was made possible by funding from the Office of Naval Research (N00014-14-1-0533, N00014-18-1-2537, and N00014-08-1- 0642; Program Manager: R. Brizzolara), the NSF (1921809; Program Manager: R. Joslin), and the University of Virginia.

Publication: Zhong, Q., Zhu, J., Fish, F. E., Kerr, S., Downs, A., Bart-Smith, H., & Quinn, D. B. (2021). Tunable stiffness enables fast and efficient swimming in fish-like robots. Science Robotics. (In press).

Presenters

  • Danniel Quinn

    • University of Virginia
    • Stanford University

Authors

  • Qiang Zhong

    • University of Virginia
  • Joseph Zhu

    • University of Virginia
  • Frank Fish

    • West Chester University
  • Sarah Kerr

    • West Chester University
  • Abigail Downs

    • West Chester University
  • Hilary Bart-Smith

    • University of Virginia
  • Danniel Quinn

    • University of Virginia
    • Stanford University