Swarming is the spontaneous organised motion of a large number of
individuals. It is observed at all scales, from bacterial colonies,
slime moulds and groups of insects to shoals of fish, flocks of birds
and animal herds. Now physicists Maksym Romenskyy and Vladimir Lobaskin
from University College Dublin, Ireland, have uncovered new collective
properties of swarm dynamics in a study just published in EPJ B.
Ultimately, this could be used to control swarms of animals, robots, or
human crowds by applying signals capable of emulating the underlying
interaction of individuals within the swarm, which could lead to
predicted motion patterns elucidated through modelling.
The authors were inspired by condensed matter models, used for
example in the study of magnetism, which were subsequently adapted to be
biologically relevant to animal swarms. In their model, in addition to
the ability to align with its neighbours, each model animal is endowed
with two new features: one for collision avoidance and another
preventing direction change at every step to ensure persistence of
motion. The team performed computer simulations of up to 100,000
self-propelled particles, each mimicking an individual animal and moving
at a constant speed on a plane surface.
They found that when the swarm becomes overcrowded, the globally
ordered motion breaks down. At high density and when the nearest
neighbours are within one step of each other, each animal can no longer
decide on the safe direction of motion. Instead, it is busy correcting
its motion to avoid collisions.
They also described, for the first time, a power law that quantifies
the average degree of alignment in the direction of motion for animals
within the swarm. The law describes how the alignment decays from the
centre of the swarm, where animals can best judge the swarm motion due
to their maximum number of neighbours, to the periphery.