"Gait switching and targeted navigation of microswimmers via deep reinf" by Zonghao Zou, Yuexin Liu et al.
 

Gait switching and targeted navigation of microswimmers via deep reinforcement learning

Document Type

Article

Publication Date

12-1-2022

Abstract

Swimming microorganisms switch between locomotory gaits to enable complex navigation strategies such as run-and-tumble to explore their environments and search for specific targets. This ability of targeted navigation via adaptive gait-switching is particularly desirable for the development of smart artificial microswimmers that can perform complex biomedical tasks such as targeted drug delivery and microsurgery in an autonomous manner. Here we use a deep reinforcement learning approach to enable a model microswimmer to self-learn effective locomotory gaits for translation, rotation and combined motions. The Artificial Intelligence (AI) powered swimmer can switch between various locomotory gaits adaptively to navigate towards target locations. The multimodal navigation strategy is reminiscent of gait-switching behaviors adopted by swimming microorganisms. We show that the strategy advised by AI is robust to flow perturbations and versatile in enabling the swimmer to perform complex tasks such as path tracing without being explicitly programmed. Taken together, our results demonstrate the vast potential of these AI-powered swimmers for applications in unpredictable, complex fluid environments.

Identifier

85132149790 (Scopus)

Publication Title

Communications Physics

External Full Text Location

https://doi.org/10.1038/s42005-022-00935-x

e-ISSN

23993650

Issue

1

Volume

5

Grant

1614863

Fund Ref

National Science Foundation

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