Gait switching and targeted navigation of microswimmers via deep reinforcement learning

Zonghao Zou, Yuexin Liu, Y. N. Young, On Shun Pak, Alan C.H. Tsang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number158
JournalCommunications Physics
Volume5
Issue number1
DOIs
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

Fingerprint

Dive into the research topics of 'Gait switching and targeted navigation of microswimmers via deep reinforcement learning'. Together they form a unique fingerprint.

Cite this