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Empirical Data-Driven Linear Model of a Swimming Robot Using the Complex Delay-Embedding DMD Technique.

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Summary

This study introduces a data-driven method to model the complex dynamics of anguilliform locomotion in soft robots. The technique successfully extracts underlying patterns from movement data, simplifying robot control.

Keywords:
CDE DMDbio-inspired locomotionbio-roboticsdata-driven modelingsoft robotics

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Area of Science:

  • Robotics
  • Fluid Dynamics
  • Biomimicry

Background:

  • Anguilliform locomotion is an efficient aquatic movement strategy involving whole-body fluid-body interaction.
  • Understanding the complex physics of this locomotion is crucial for developing advanced robotic systems.
  • Data-driven approaches offer a potential pathway to model these dynamics without direct hydrodynamic measurements.

Purpose of the Study:

  • To propose empirical kinematic control and data-driven modeling for a soft robotic fish.
  • To develop a novel algorithm for extracting dynamic models from experimental data.
  • To analyze and describe the underlying dynamics of the soft robot's locomotion.

Main Methods:

  • A six-segment soft robot actuated by pneumatic artificial muscles was designed.
  • Kinematic equations were used to generate desired actuation patterns mimicking anguilliform kinematics.
  • Experimental data on robot motion was collected using QualiSys® Tracking Manager.
  • A new complex variable delay-embedding dynamic mode decomposition (CDE DMD) algorithm was developed and applied.

Main Results:

  • The CDE DMD algorithm successfully extracted both linear and chaotic modes from the experimental data.
  • Analysis revealed that the robot's dynamics can be approximated by a linearized model with intermittent chaotic behavior.
  • The proposed method effectively identified coherent modes from limited sensor measurements.

Conclusions:

  • Data-driven modeling, specifically using the CDE DMD algorithm, can effectively capture the complex dynamics of soft robotic locomotion.
  • The robot's movement is characterized by a combination of predictable linear dynamics and unpredictable chaotic modes.
  • This approach offers a powerful tool for understanding and controlling bio-inspired robotic systems.