Can flocking aid the path planning of microswimmers in turbulent flows?

TL;DR

This paper demonstrates that incorporating flocking behavior into microswimmer path planning via reinforcement learning significantly improves navigation efficiency in turbulent flows.

Contribution

It introduces a novel flocking-enhanced reinforcement learning approach for microswimmer navigation in turbulent environments, building on previous non-interacting models.

Findings

Flocking improves microswimmer path efficiency.

Flocking-based strategy outperforms previous methods.

Enhanced navigation success rate in turbulent flows.

Abstract

We show that flocking of microswimmers in a turbulent flow can enhance the efficacy of reinforcement-learning-based path-planning of microswimmers in turbulent flows. In particular, we develop a machine-learning strategy that incorporates Vicsek-model-type flocking in microswimmer assemblies in a statistically homogeneous and isotropic turbulent flow in two dimensions (2D). We build on the adversarial-reinforcement-learning of Ref.~\cite{alageshan2020machine} for non-interacting microswimmers in turbulent flows. Such microswimmers aim to move optimally from an initial position to a target. We demonstrate that our flocking-aided version of the adversarial-reinforcement-learning strategy of Ref.~\cite{alageshan2020machine} can be superior to earlier microswimmer path-planning strategies.