Pattern stabilisation in swarms of programmable active matter: a probe for turbulence at large length scales

TL;DR

This paper introduces an algorithm to stabilize and control the pattern of robotic swarms by suppressing specific fluctuations, revealing that the flow field's statistics depend solely on the stabilization forces.

Contribution

It presents a novel method for pattern stabilization in active matter swarms using calculated thrust forces that suppress non-affine displacements.

Findings

Stable swarm patterns can be maintained with minimal power input.

Flow field statistics are determined by stabilization forces.

The approach links pattern stability to turbulence characteristics.

Abstract

We propose an algorithm for creating stable, ordered, swarms of active robotic agents arranged in any given pattern. The strategy involves suppressing a class of fluctuations known as "non-affine" displacements, viz. those involving non-linear deformations of a reference pattern, while all (or most) affine deformations are allowed. We show that this can be achieved using precisely calculated, fluctuating, thrust forces associated with a vanishing average power input. A surprising outcome of our study is that once the structure of the swarm is maintained at steady state, the statistics of the underlying flow field is determined solely from the statistics of the forces needed to stabilize the swarm.