title: Synchronisation through learning for two self-propelled swimmers.
abstract: The coordinated motion by multiple swimmers is a fundamental component in fish schooling. The flow field induced by the motion of each self-propelled swimmer implies non-linear hydrodynamic interactions among the members of a group. How do swimmers compensate for such hydrodynamic interactions in coordinated patterns? We provide an answer to this riddle though simulations of two, self-propelled, fish-like bodies that employ a learning algorithm to synchronise their swimming patterns. We distinguish between learned motion patterns and the commonly used a-priori specified movements, that are imposed on the swimmers without feedback from their hydrodynamic interactions. First, we demonstrate that two rigid bodies executing pre-specified motions, with an alternating leader and follower, can result in substantial drag-reduction and intermittent thrust generation. In turn, we study two self-propelled swimmers arranged in a leader-follower configuration, with a-priori specified body-deformations. These two self-propelled swimmers do not sustain their tandem configuration. The follower experiences either an increase or decrease in swimming speed, depending on the initial conditions, while the swimming of the leader remains largely unaffected. This indicates that a-priori specified patterns are not sufficient to sustain synchronised swimming. We then examine a tandem of swimmers where the leader has a steady gait and the follower learns to synchronize its motion, to overcome the forces induced by the leader's vortex wake. The follower employs reinforcement learning to adapt its swimming-kinematics so as to minimize its lateral deviations from the leader's path. Swimming in such a sustained synchronised tandem yields up to [Formula: see text] reduction in energy expenditure for the follower, in addition to a [Formula: see text] increase in its swimming-efficiency. The present results show that two self-propelled swimmers can be synchronised by adapting their motion patterns to compensate for flow-structure interactions. Moreover, swimmers can exploit the vortical structures of their flow field so that synchronised swimming is energetically beneficial.
authors.affiliation: Computational Science and Engineering Laboratory, Clausiusstrasse 33, ETH Zürich, CH-8092, Switzerland. Wallace Visiting Professor, Massachusetts Institute of Technology, MA, United States of America.
authors.forename: Guido
authors.initials: G
authors.lastname: Novati
authors.forename: Siddhartha
authors.initials: S
authors.lastname: Verma
authors.forename: Dmitry
authors.initials: D
authors.lastname: Alexeev
authors.forename: Diego
authors.initials: D
authors.lastname: Rossinelli
authors.forename: Wim M
authors.initials: WM
authors.lastname: van Rees
authors.forename: Petros
authors.initials: P
authors.lastname: Koumoutsakos
mesh_terms: D000465:Algorithms; D000818:Animals; D040761:Biomimetic Materials; D032701:Biomimetics / Q000295:instrumentation*; D003299:Cooperative Behavior; D005399:Fishes / Q000502:physiology*; D057446:Hydrodynamics; D008399:Mass Behavior; D012054:Reinforcement, Psychology; D013550:Swimming / Q000502:physiology*