Shepherding control and herdability in complex multiagent systems

TL;DR

This paper investigates shepherding control in multiagent systems, deriving scaling laws and thresholds for herding success, supported by PDE analysis and extensive numerical validation.

Contribution

It introduces a relaxed model of shepherding, establishes scaling laws and a herdability threshold, and provides PDE-based analysis for herder dynamics.

Findings

Scaling laws linking number of targets and herders

Existence of a herdability density threshold

Validation through numerical experiments

Abstract

We study the shepherding control problem where a group of "herders" need to orchestrate their collective behaviour in order to steer the dynamics of a group of "target" agents towards a desired goal. We relax the strong assumptions of targets showing cohesive collective behavior in the absence of the herders, and herders owning global sensing capabilities. We find scaling laws linking the number of targets and minimum herders needed, and we unveil the existence of a critical threshold of the density of the targets, below which the number of herders needed for success significantly increases. We explain the existence of such a threshold in terms of the percolation of a suitably defined herdability graph and support our numerical evidence by deriving and analysing a PDE describing the herders dynamics in a simplified one-dimensional setting. Extensive numerical experiments validate our methodology.