Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, 14850, USA.
Sci Rep. 2023 Jun 9;13(1):9397. doi: 10.1038/s41598-023-36305-y.
Biological microswimmers can coordinate their motions to exploit their fluid environment-and each other-to achieve global advantages in their locomotory performance. These cooperative locomotion require delicate adjustments of both individual swimming gaits and spatial arrangements of the swimmers. Here we probe the emergence of such cooperative behaviors among artificial microswimmers endowed with artificial intelligence. We present the first use of a deep reinforcement learning approach to empower the cooperative locomotion of a pair of reconfigurable microswimmers. The AI-advised cooperative policy comprises two stages: an approach stage where the swimmers get in close proximity to fully exploit hydrodynamic interactions, followed a synchronization stage where the swimmers synchronize their locomotory gaits to maximize their overall net propulsion. The synchronized motions allow the swimmer pair to move together coherently with an enhanced locomotion performance unattainable by a single swimmer alone. Our work constitutes a first step toward uncovering intriguing cooperative behaviors of smart artificial microswimmers, demonstrating the vast potential of reinforcement learning towards intelligent autonomous manipulations of multiple microswimmers for their future biomedical and environmental applications.
生物微型游泳者可以协调它们的运动，以利用它们的流体环境和彼此，在它们的运动性能方面获得全局优势。这些协作运动需要对个体游泳步态和游泳者的空间排列进行精细的调整。在这里，我们在具有人工智能的人工微型游泳者中探索这种协作行为的出现。我们首次使用深度强化学习方法来增强一对可重构微型游泳者的协作运动。人工智能建议的协作策略包括两个阶段：一个是接近阶段，游泳者近距离接近以充分利用水动力相互作用，然后是同步阶段，游泳者同步它们的运动步态以最大化它们的整体净推进力。同步运动使游泳者对能够协同一致地移动，从而实现单个游泳者无法实现的增强运动性能。我们的工作是揭示智能人工微型游泳者有趣的协作行为的第一步，展示了强化学习在未来生物医学和环境应用中对多个微型游泳者的智能自主操作的巨大潜力。
