A Continuification-Based Control Solution for Large-Scale Shepherding

TL;DR

This paper introduces a continuum model-based control method for large-scale shepherding, enabling scalable and effective confinement of followers using leader agents, validated through simulations and real-world experiments.

Contribution

It presents a novel continuification approach transforming agent dynamics into PDEs for scalable control in large swarms, with proven convergence guarantees.

Findings

Effective confinement of large follower groups demonstrated

Scalable control strategy validated in mixed-reality experiments

Global convergence guarantees established

Abstract

In this paper, we address the large-scale shepherding control problem using a continuification-based strategy. We consider a scenario in which a large group of follower agents (targets) must be confined within a designated goal region through indirect interactions with a controllable set of leader agents (herders). Our approach transforms the microscopic agent-based dynamics into a macroscopic continuum model via partial differential equations (PDEs). This formulation enables efficient, scalable control design for the herders' behavior, with guarantees of global convergence. Numerical and experimental validations in a mixed-reality swarm robotics framework demonstrate the method's effectiveness.