Independent Control and Path Planning of Microswimmers with a Uniform Magnetic Field

TL;DR

This paper presents new control strategies, including reinforcement learning, for independently navigating multiple magnetic micro-swimmers using a uniform magnetic field, demonstrating improved efficiency and effective path planning.

Contribution

It introduces analytical and reinforcement learning methods for independent control of micro-swimmers with a uniform magnetic field, a novel approach in the field.

Findings

Reinforcement learning outperforms analytical control in minimizing travel time.

First demonstration of independent navigation of realistic micro-swimmers in viscous flow.

Effective path planning achieved with a uniform magnetic field.

Abstract

Artificial bacteria flagella (ABFs) are magnetic helical micro-swimmers that can be remotely controlled via a uniform, rotating magnetic field. Previous studies have used the heterogeneous response of microswimmers to external magnetic fields for achieving independent control. Here we introduce analytical and reinforcement learning control strategies for path planning to a target by multiple swimmers using a uniform magnetic field. The comparison of the two algorithms shows the superiority of reinforcement learning in achieving minimal travel time to a target. The results demonstrate, for the first time, the effective independent navigation of realistic micro-swimmers with a uniform magnetic field in a viscous flow field.