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Learning to soar in turbulent environments.

Gautam Reddy1, Antonio Celani2, Terrence J Sejnowski3

  • 1Department of Physics, University of California, San Diego, La Jolla, CA 92093;

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|August 3, 2016
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Summary
This summary is machine-generated.

This study trains gliders to navigate turbulent air currents using reinforcement learning. The research shows gliders adopt conservative flight strategies in higher turbulence, optimizing energy use for soaring.

Keywords:
navigationreinforcement learningthermal soaringturbulence

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Area of Science:

  • Aerospace Engineering
  • Animal Behavior
  • Atmospheric Science

Background:

  • Birds and gliders use atmospheric thermals for energy-efficient flight.
  • Thermal soaring is crucial for migratory birds and autonomous glider range extension.
  • Thermal soaring occurs in turbulent atmospheric boundary layers.

Purpose of the Study:

  • To investigate soaring flight as a complex decision-making and navigation problem.
  • To develop and train autonomous gliders for effective thermal soaring in turbulent environments.
  • To quantify risk-taking behavior in soaring flight under varying turbulence levels.

Main Methods:

  • Simulating the atmospheric boundary layer using turbulent convective flow models.
  • Employing model-free, experience-based reinforcement learning algorithms to train gliders.
  • Analyzing learned policies to understand navigation strategies in moderate and strong turbulence.

Main Results:

  • Gliders learn to adopt increasingly conservative policies as turbulence intensity increases.
  • The study quantifies the degree of risk gliders can afford in turbulent conditions.
  • Reinforcement learning identifies key sensorimotor cues for effective soaring control.

Conclusions:

  • Reinforcement learning provides a viable method for training gliders to navigate turbulent thermals.
  • Learned policies demonstrate adaptive risk management in fluctuating atmospheric environments.
  • This research advances understanding of biological soaring and autonomous flight technologies.