Efficient collective swimming by harnessing vortices through deep reinforcement learning

TL;DR

This paper demonstrates that fish can enhance their swimming efficiency by actively intercepting vortices in their wake, a strategy uncovered through deep reinforcement learning combined with high-fidelity flow simulations, revealing a potential mechanism for collective energy savings.

Contribution

It introduces a novel approach combining deep reinforcement learning with flow simulations to uncover how fish harvest energy from vortices during schooling.

Findings

Fish improve efficiency by intercepting vortices

Deep reinforcement learning can optimize navigation in complex flows

Formation swimming offers energetic advantages

Abstract

Fish in schooling formations navigate complex flow-fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behaviour has been associated with evolutionary advantages including collective energy savings. How fish harvest energy from their complex fluid environment and the underlying physical mechanisms governing energy-extraction during collective swimming, is still unknown. Here we show that fish can improve their sustained propulsive efficiency by actively following, and judiciously intercepting, vortices in the wake of other swimmers. This swimming strategy leads to collective energy-savings and is revealed through the first ever combination of deep reinforcement learning with high-fidelity flow simulations. We find that a `smart-swimmer' can adapt its position and body deformation to synchronise with the momentum of the oncoming vortices, improving its average swimming-efficiency at no cost to the leader. The results show that fish may harvest energy deposited in vortices produced by their peers, and support the conjecture that swimming in formation is energetically advantageous. Moreover, this study demonstrates that deep reinforcement learning can produce navigation algorithms for complex flow-fields, with promising implications for energy savings in autonomous robotic swarms.