Literature DB >> 28355166

Synchronisation through learning for two self-propelled swimmers.

Guido Novati1, Siddhartha Verma, Dmitry Alexeev, Diego Rossinelli, Wim M van Rees, Petros Koumoutsakos.   

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.

Mesh:

Year:  2017        PMID: 28355166     DOI: 10.1088/1748-3190/aa6311

Source DB:  PubMed          Journal:  Bioinspir Biomim        ISSN: 1748-3182            Impact factor:   2.956


  8 in total

1.  Efficient collective swimming by harnessing vortices through deep reinforcement learning.

Authors:  Siddhartha Verma; Guido Novati; Petros Koumoutsakos
Journal:  Proc Natl Acad Sci U S A       Date:  2018-05-21       Impact factor: 11.205

2.  Stable formations of self-propelled fish-like swimmers induced by hydrodynamic interactions.

Authors:  Longzhen Dai; Guowei He; Xiang Zhang; Xing Zhang
Journal:  J R Soc Interface       Date:  2018-10-17       Impact factor: 4.118

3.  Tail Beat Synchronization during Schooling Requires a Functional Posterior Lateral Line System in Giant Danios, Devario aequipinnatus.

Authors:  Prasong J Mekdara; Fazila Nasimi; Margot A B Schwalbe; Eric D Tytell
Journal:  Integr Comp Biol       Date:  2021-09-08       Impact factor: 3.326

4.  On the energetics and stability of a minimal fish school.

Authors:  Gen Li; Dmitry Kolomenskiy; Hao Liu; Benjamin Thiria; Ramiro Godoy-Diana
Journal:  PLoS One       Date:  2019-08-28       Impact factor: 3.240

Review 5.  Thriving artificial underwater drag-reduction materials inspired from aquatic animals: progresses and challenges.

Authors:  Guizhong Tian; Dongliang Fan; Xiaoming Feng; Honggen Zhou
Journal:  RSC Adv       Date:  2021-01-18       Impact factor: 3.361

6.  Hydrodynamical Fingerprint of a Neighbour in a Fish Lateral Line.

Authors:  Gen Li; Dmitry Kolomenskiy; Hao Liu; Benjamin Thiria; Ramiro Godoy-Diana
Journal:  Front Robot AI       Date:  2022-02-11

7.  Scientific multi-agent reinforcement learning for wall-models of turbulent flows.

Authors:  H Jane Bae; Petros Koumoutsakos
Journal:  Nat Commun       Date:  2022-03-17       Impact factor: 14.919

8.  In vivo quantification of mechanical properties of caudal fins in adult zebrafish.

Authors:  Sahil Puri; Tinri Aegerter-Wilmsen; Anna Jaźwińska; Christof M Aegerter
Journal:  J Exp Biol       Date:  2018-02-20       Impact factor: 3.312

  8 in total

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