Self-learning how to swim at low Reynolds number
Self-learning how to swim at low Reynolds number
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DOI：
10.1103/physrevfluids.5.074101
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发表时间：
2020-07-10
影响因子：
2.7
通讯作者：
Pak, On Shun
中科院分区：
文献类型：
--
作者：
Tsang, Alan Cheng Hou;Tong, Pun Wai;Pak, On Shun
由于在低雷诺数（Re）下对自推进的严格限制，设计合成微型游泳者的运动步态一直是一个挑战。在这里，我们介绍了一种新的理论方法，通过强化学习设计一类自学习，自适应（或“智能”）microswimmer。从传统的范式指定运动步态先验的分歧，在这里，一个自我学习的游泳者可以开发和适应其推进策略的基础上与周围介质的相互作用。我们说明了这种新的方法，使用一个最小的，但有代表性的模型游泳者组成的一个组件的球体连接的可扩展杆。在不需要任何低Re运动的先验知识的情况下，我们证明了这种自我学习的游泳者可以恢复以前已知的推进策略，识别更有效的运动步态，并在不同的媒体中调整其运动步态。这种方法为设计具有强大机车能力的下一代智能微型机器人开辟了另一条途径。
Designing locomotory gaits for synthetic microswimmers has been a challenge due to stringent constraints on self-propulsion at low Reynolds numbers (Re). Here, we introduce a new theoretical approach of designing a class of self-learning, adaptive (or "smart") microswimmers via reinforcement learning. Diverging from the traditional paradigm of specifying locomotory gaits a priori, here a self-learning swimmer can develop and adapt its propulsion strategy based on its interactions with the surrounding medium. We illustrate this new approach using a minimal but representative model swimmer consisting of an assembly of spheres connected by extensible rods. Without requiring any prior knowledge of low Re locomotion, we demonstrate that this self-learning swimmer can recover a previously known propulsion strategy, identify more effective locomotory gaits, and adapt its locomotory gaits in different media. This approach opens an alternative avenue to designing the next generation of smart microrobots with robust locomotive capabilities.