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Learning tensegrity locomotion using open-loop control signals and coevolutionary algorithms.

Atil Iscen1, Ken Caluwaerts2, Jonathan Bruce3

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Summary
This summary is machine-generated.

Evolutionary algorithms enable control for soft robots, specifically tensegrity robots like NASA's SUPERball. This approach evolves open-loop control signals for robust locomotion without complex sensors.

Keywords:
Evolutionary algorithmscoevolutionfitness shapinglocomotiontensegrity

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Soft robots offer advantages but are challenging to control with traditional methods.
  • Tensegrity structures represent an emerging field in soft robotics.
  • The Spherical Underactuated Planetary Exploration Robot ball (SUPERball) is a tensegrity robot developed at NASA.

Purpose of the Study:

  • To apply evolutionary algorithms for controlling tensegrity soft robots.
  • To develop and analyze a rolling locomotion algorithm for the SUPERball robot.
  • To investigate the effectiveness of distributed, open-loop control strategies.

Main Methods:

  • Utilized a historical-average fitness-shaping algorithm for coevolutionary learning.
  • Employed a distributed control approach by coevolving open-loop control signals for individual controllers.
  • Simulated the SUPERball robot using the NASA Tensegrity Robotics Toolkit for evaluating evolved behaviors.

Main Results:

  • Successfully evolved rolling locomotion for the SUPERball tensegrity robot.
  • Demonstrated that simple, distributed open-loop controllers are viable for hardware implementation.
  • Analyzed the characteristics of learned rolling gaits, including signal complexity, frequency, and energy consumption.

Conclusions:

  • Evolutionary algorithms provide an effective control paradigm for complex soft robots like tensegrity structures.
  • Open-loop, distributed control is a practical approach for implementing locomotion on hardware without sensors.
  • The study provides insights into the correlation between control signals and robot gait dynamics.