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Gait switching and targeted navigation of microswimmers via deep reinforcement learning

Engineering and Technology

Gait switching and targeted navigation of microswimmers via deep reinforcement learning

Z. Zou, Y. Liu, et al.

This exciting research led by Zonghao Zou, Yuexin Liu, Y.-N. Young, On Shun Pak, and Alan C. H. Tsang showcases how deep reinforcement learning empowers a model microswimmer to develop adaptive locomotory gaits for efficient navigation. The findings reveal its potential for complex fluid environments, promising innovative applications.

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Playback language: English
Abstract
This paper explores the use of deep reinforcement learning to enable a model microswimmer to self-learn effective locomotory gaits for translation, rotation, and combined motions. The AI-powered swimmer can adaptively switch between various gaits to navigate towards target locations, mimicking the gait-switching behaviors of swimming microorganisms. The study demonstrates the robustness of the AI-advised strategy to flow perturbations and its versatility in enabling complex tasks like path tracing without explicit programming, highlighting the potential of AI-powered swimmers for applications in complex fluid environments.
Publisher
Communications Physics
Published On
Jun 21, 2022
Authors
Zonghao Zou, Yuexin Liu, Y.-N. Young, On Shun Pak, Alan C. H. Tsang
Tags
deep reinforcement learning
microswimmer
locomotory gaits
navigation
AI strategy
fluid environments
path tracing

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