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Related Concept Videos

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Buoyancy

When an object is placed in a fluid, it either floats or sinks. All objects in a fluid experience a buoyant force. For example, a metal ball sinks, while a rubber ball floats. Similarly, a submarine can sink and float by adjusting its buoyancy.  The concept of buoyancy raises several interesting questions. For instance, where does this buoyant force come from? How much buoyant force is required to make an object sink or float? Do objects that sink get any support at all from the fluid? 
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Physical reservoir computing on a soft bio-inspired swimmer.

Shan He1, Patrick Musgrave1

  • 1Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, USA.

Neural Networks : the Official Journal of the International Neural Network Society
|October 2, 2024
PubMed
Summary

Physical Reservoir Computing (PRC) in soft robots enables state estimation for better control. This bio-inspired approach leverages the robot

Keywords:
Bio-inspired swimmerPhysical reservoir computing

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Area of Science:

  • Robotics and Control Systems
  • Computational Neuroscience
  • Fluid Dynamics

Background:

  • Soft bio-inspired underwater vehicles offer advantages but are difficult to control due to their underactuated nature.
  • State estimation is crucial for controlling these flexible robots, but conventional methods are challenging.
  • Physical Reservoir Computing (PRC) offers a novel approach by embedding recurrent neural networks into physical systems.

Purpose of the Study:

  • To investigate the application of PRC for state estimation in soft bio-inspired autonomous underwater vehicles.
  • To explore the potential of PRC-informed state estimation for enhancing vehicle control.
  • To evaluate the computational efficiency and prediction accuracy of PRC using a soft propulsor as a physical reservoir.

Main Methods:

  • A soft bio-inspired propulsor was designed and utilized as a physical reservoir.
  • The propulsor's nonlinear fluid-structural dynamics served as the reservoir, with kinematic feedback as readouts.
  • The system's ability to predict hydrodynamic forces and benchmark computational tasks was evaluated based on applied forcing.

Main Results:

  • The soft propulsor demonstrated accurate prediction of hydrodynamic thrust and benchmark tasks at specific input frequencies.
  • Spectral analysis revealed that the system's dynamic response and nonlinearity are key to processing information and improving prediction.
  • Nonlinearity transforms input signals into a broader frequency spectrum at the readouts, essential for accurate target signal reconstruction.

Conclusions:

  • PRC can be effectively embodied within the physical structure of soft bio-inspired swimmers for efficient state estimation.
  • The dynamic response and nonlinear properties of the physical reservoir are critical for accurate prediction performance.
  • This study represents a foundational step towards integrating computation directly into soft robotic systems for advanced control.