Harnessing swarms for directed migration of interacting active particles via optimal global control

TL;DR

This paper demonstrates that simple, optimized global control strategies can significantly improve the directed migration of interacting active particle swarms in confined channels, overcoming natural clogging and band formation.

Contribution

It introduces reinforcement learning-based optimization of global controls to enhance collective transport in active particle swarms, showing effectiveness even with limited system observations.

Findings

Global alignment controls increase net particle flux.

Reinforcement learning optimizes control to suppress clogging.

Coarse system observations suffice for near-optimal control.

Abstract

This study investigates the use of global control strategies to enhance the directed migration of swarms of interacting self-propelled particles confined in a channel. Uncontrolled dynamics naturally leads to wall accumulation, clogging, and band formation due to the interplay between self-organization and confinement. This work explores whether a uniform global control, such as magnetic field acting on all particles, can optimize collective transport. Using a discrete Vicsek-like model, it is found that simple global alignment controls, optimized via reinforcement learning, efficiently suppress unfavorable configurations and significantly increase the net particle flux along a prescribed channel direction. These results highlight that coarse, system-level observations are sufficient to achieve near-optimal control, even in regimes with strong fluctuations or partial ordering.