Journal Article

Efficient collective swimming by harnessing vortices through deep reinforcement learning

Koumoutsakos, Petros, Verma, Siddhartha, Novati, Guido

2018

Overview

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.

• Publisher: National Academy of Sciences
• Subject: 3-D technology / Algorithms / Animals / Applied Mathematics / Behavior, Animal - physiology / Biological Sciences / Biomechanical Phenomena / Biophysics and Computational Biology / Computer memory / Computer Simulation / Deformation mechanisms / Energy conservation / Energy harvesting / Fish / Fish harvest / Fishes - physiology / Fluid dynamics / Fluid flow / Learning / Learning - physiology / Long short-term memory / Machine learning / Memory cells / Models, Biological / Navier-Stokes equations / Physical Sciences / Propulsive efficiency / Recurrent neural networks / Reinforcement, Psychology / Robotics / Schooling behavior / Simulation / Spatial Navigation - physiology / Swimming / Swimming - physiology / Vortices