Gait switching and targeted navigation of microswimmers via deep reinforcement learning

Gait switching and targeted navigation of microswimmers via deep reinforcement learning
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通过深度强化学习实现微型游泳者的步态切换和定向导航

DOI:
10.1038/s42005-022-00935-x
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发表时间:
2022
影响因子:
5.5
通讯作者:
Tsang, Alan C.
Tsang, Alan C.
中科院分区:
物理与天体物理1区
文献类型:
--
作者:
Zou, Zonghao;Liu, Yuexin;Young, Y.-N.;Pak, On Shun;Tsang, Alan C.

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游泳微生物在运动步态之间切换,以实现复杂的导航策略,如奔跑和翻滚,以探索它们的环境并寻找特定的目标。这种通过自适应步态切换进行靶向导航的能力对于智能人工微泳者的开发是特别理想的,这种智能人工微泳者可以以自主方式执行复杂的生物医学任务,例如靶向药物递送和显微外科手术。在这里,我们使用深度强化学习方法,使模型microswimmer能够自我学习有效的运动步态,用于平移,旋转和组合运动。人工智能(AI)驱动的游泳者可以自适应地在各种运动步态之间切换,以朝向目标位置导航。多模态导航策略让人想起游泳微生物所采用的步态转换行为。我们表明,人工智能建议的策略对流量扰动具有鲁棒性,并且具有通用性,使游泳者能够在无需显式编程的情况下执行路径追踪等复杂任务。总之,我们的研究结果证明了这些人工智能游泳者在不可预测的复杂流体环境中的巨大潜力。
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.
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