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Learning efficient navigation in vortical flow fields.

Peter Gunnarson1, Ioannis Mandralis1, Guido Novati2

  • 1Graduate Aerospace Laboratories, California Institute of Technology, 1200 E California Blvd, Pasadena, CA, 91125, USA.

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Reinforcement learning enables robots to navigate efficiently in ocean currents. A velocity-sensing approach proved surprisingly effective, achieving high success rates for underwater robotic navigation.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Fluid Dynamics

Background:

  • Efficient point-to-point navigation is crucial for robotic applications like ocean surveying.
  • Robots often operate with limited environmental knowledge or face time-varying currents, hindering traditional optimal control methods.

Purpose of the Study:

  • To apply a Reinforcement Learning (RL) algorithm for discovering time-efficient navigation policies for a fixed-speed swimmer in unsteady 2D flow fields.
  • To evaluate the impact of different environmental cues on navigation success and efficiency.

Main Methods:

  • Utilized a deep neural network that takes environmental cues as input to determine swimmer actions.
  • Implemented a Remember and Forget Experience Replay mechanism within the RL algorithm.
  • Tested both velocity sensing and bio-mimetic vorticity sensing approaches.

Main Results:

  • Swimmers successfully exploited background flow to reach target locations.
  • Navigation success was dependent on the environmental cue used.
  • Velocity sensing significantly outperformed vorticity sensing, achieving near 100% success.
  • The velocity sensing approach approached the time-efficiency of optimal navigation trajectories.

Conclusions:

  • Reinforcement Learning can discover effective navigation policies for robots in complex flow fields.
  • Velocity sensing is a more effective environmental cue than vorticity sensing for this navigation task.
  • RL-based navigation offers a promising solution for autonomous underwater vehicles in dynamic environments.