The role of hydrodynamics in collective motions of fish schools and bioinspired underwater robots

Abstract

Collective behaviour defines the lives of many animal species on the Earth. Underwater swarms span several orders of magnitude in size, from coral larvae and krill to tunas and dolphins. Agent-based algorithms have modelled collective movements of animal groups by use of social forces, which approximate the behaviour of individual animals. But details of how swarming individuals interact with the fluid environment are often under-examined. How do fluid forces shape aquatic swarms? How do fish use their flow-sensing capabilities to coordinate with their schooling mates? We propose viewing underwater collective behaviour from the framework of fluid stigmergy, which considers both physical interactions and information transfer in fluid environments. Understanding the role of hydrodynamics in aquatic collectives requires multi-disciplinary efforts across fluid mechanics, biology and biomimetic robotics. To facilitate future collaborations, we synthesize key studies in these fields.

Keywords: collective behaviour; fish school; fluid mechanics; fluid stigmergy.

Figure 1.

Underwater swarms in nature span several orders of magnitude, from (a) dolphins and (b) giant trevally on the metre scale, to (c) squid and (d) mackerel on the centimetre scale, to (e) coral larvae on the millimetre scale. Images obtained through Education licences from Adobe Stock.

Figure 2.

Different constructs of swarm models. Agent interactions in traditional models depend on relative distance and locations, sometimes considering vision and a limited field of view. The perspective of fluid stigmergy includes the fluid environment and emphasizes fluid–agent interactions and coordination strategies based on flow-sensing.

Figure 3.

(a) Fluid fields behind a swimming fish, behind an obstacle, and around a dipole. Red vortices rotate clockwise while blue vortices rotate counterclockwise. (b) Hydrodynamic arguments support either a diamond formation, a phalanx formation or an inline formation. (c) The three-dimensional fluid field around a swimming fish can be characterized by tomographic PIV (permission from [43]), and hydrodynamic interactions between a pair of fish can be studied using (d) fluid simulations (permission from [44]) and (e) hydrofoil experiments (permission from [45]).

Figure 4.

(a) Superficial neuromasts (SN, red) and canal neuromasts (CN, blue) and their relative relationship with the boundary layer flow. Grey arrows indicate fluid velocity. (b) Interspecies diversity in trunk canal placements (adapted from [82]). (c) Frequency response of SN and CNs (adapted from [42]).

Figure 5.

Underwater robots with onboard sensing capabilities. (a) Vision-based coordination in BlueSwarms, reproduced with permission from [37]. (b–e) Individual robots with flow sensors (permission from [123] for b, and [124] for c). (d,e) Robots that use flow-sensing to detect their neighbours (permission from [125] for d, and [126] for e).

Figure 6.

Future directions and goals for understanding underwater collectives using (a,b) theoretical, (c,d) biological and (e,f) robotic approaches.