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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.
Nature Communications
|December 9, 2021
Summary
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.
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.
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