Active Cloaking in Stokes Flows via Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based method for actively cloaking micro-swimmers in Stokes flows, enabling dynamic, robust, and non-invasive hydrodynamic invisibility in crowded suspensions.

Contribution

It presents a novel AI-driven active cloaking technique that adaptively controls swimming agents to conceal objects in viscous flows, a significant advancement over passive or static methods.

Findings

Reinforcement learning enables adaptive control of cloaking agents.

The method achieves robust and non-invasive cloaking.

Applicable to multiple intruders and arbitrary regions.

Abstract

Hydrodynamic signatures at the Stokes regime, pertinent to motility of micro-swimmers, have a long-range nature. This implies that movements of an object in such a viscosity-dominated regime, can be felt tens of body-lengths away and significantly alter dynamics of the surrounding environment. Here, we devise a systematic methodology to actively cloak swimming objects within any arbitrarily crowded suspension of micro-swimmers. Specifically, our approach is to conceal the target swimmer throughout its motion using cooperative flocks of swimming agents equipped with adaptive decision-making intelligence. Through a reinforcement learning algorithm, the cloaking agents experientially learn optimal adaptive behavioral policy in the presence of flow-mediated interactions. This artificial intelligence enables them to dynamically adjust their swimming actions, so as to optimally form and robustly retain any desired arrangement around the moving object without disturbing it from its original path. Therefore, the presented active cloaking approach not only is robust against disturbances, but also is non-invasive to motion of the cloaked object. We then further generalize the proposed approach and demonstrate how our cloaking agents can be readily used, in any region of interest, to realize hydrodynamic invisibility cloaks around any number of arbitrary intruders.