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Journal article

Efficient collective swimming by harnessing vortices through deep reinforcement learning.

  • Verma S Computational Science and Engineering Laboratory, ETH Zürich, CH-8092 Zürich, Switzerland.
  • Novati G Computational Science and Engineering Laboratory, ETH Zürich, CH-8092 Zürich, Switzerland.
  • Koumoutsakos P Computational Science and Engineering Laboratory, ETH Zürich, CH-8092 Zürich, Switzerland petros@ethz.ch.
  • 2018-05-23
Published in:
  • Proceedings of the National Academy of Sciences of the United States of America. - 2018
autonomous navigation
deep reinforcement learning
energy harvesting
fish schooling
recurrent neural networks
Animals
Behavior, Animal
Biomechanical Phenomena
Computer Simulation
Fishes
Learning
Models, Biological
Reinforcement, Psychology
Spatial Navigation
Swimming
English Fish in schooling formations navigate complex flow fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behavior has been associated with evolutionary advantages including energy savings, yet the underlying physical mechanisms remain unknown. We show that fish can improve their sustained propulsive efficiency by placing themselves in appropriate locations in the wake of other swimmers and intercepting judiciously their shed vortices. This swimming strategy leads to collective energy savings and is revealed through a combination of high-fidelity flow simulations with a deep reinforcement learning (RL) algorithm. The RL algorithm relies on a policy defined by deep, recurrent neural nets, with long-short-term memory cells, that are essential for capturing the unsteadiness of the two-way interactions between the fish and the vortical flow field. Surprisingly, we find that swimming in-line with a leader is not associated with energetic benefits for the follower. Instead, "smart swimmer(s)" place themselves at off-center positions, with respect to the axis of the leader(s) and deform their body to synchronize with the momentum of the oncoming vortices, thus enhancing their swimming efficiency at no cost to the leader(s). The results confirm that fish may harvest energy deposited in vortices and support the conjecture that swimming in formation is energetically advantageous. Moreover, this study demonstrates that deep RL can produce navigation algorithms for complex unsteady and vortical flow fields, with promising implications for energy savings in autonomous robotic swarms.
Language
  • English
Open access status
bronze
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Persistent URL
https://folia.unifr.ch/global/documents/153805
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