Yu Huiyang, Liu Bo, Wang Chengyun, Liu Xuechao, Lu Xi-Yun, Huang Haibo
Department of Modern Mechanics, University of Science and Technology of China, Hefei, Anhui 230026, China.
Phys Rev E. 2022 Apr;105(4-2):045105. doi: 10.1103/PhysRevE.105.045105.
It is fascinating that fish groups spontaneously form different formations. The collective locomotions of two and multiple undulatory self-propelled foils swimming in a fluid are numerically studied and the deep reinforcement learning (DRL) is applied to control the locomotion. We explored whether typical patterns emerge spontaneously under the driven two DRL strategies. One strategy is that only the following fish gets hydrodynamic advantages. The other is that all individuals in the group take advantage of the interaction. In the DRL strategy, we use swimming efficiency as the reward function, and the visual information is included. We also investigated the effect of involving hydrodynamic force information, which is an analogy to that detected by the lateral line of fish. Each fish can adjust its undulatory phase to achieve the goal. Under the two strategies, collective patterns with different characteristics, i.e., the staggered-following, tandem-following phalanx and compact modes emerge. They are consistent with the results in the literature. The hydrodynamic mechanism of the above high-efficiency collective traveling modes is analyzed by the vortex-body interaction and thrust. We also found that the time sequence feature and hydrodynamic information in the DRL are essential to improve the performance of collective swimming. Our research can reasonably explain the controversial issue observed in the relevant experiments. The paper may be helpful for the design of bionic fish.
鱼类群体能自发形成不同的队形，这很有趣。对两个及多个在流体中波动式自行推进的箔片的集体运动进行了数值研究，并应用深度强化学习（DRL）来控制运动。我们探究了在两种DRL策略驱动下是否会自发出现典型模式。一种策略是只有跟随的鱼能获得水动力优势。另一种是群体中的所有个体都利用这种相互作用。在DRL策略中，我们将游泳效率用作奖励函数，并纳入视觉信息。我们还研究了引入水动力信息的效果，这类似于鱼类侧线所检测到的信息。每条鱼都可以调整其波动相位以实现目标。在这两种策略下，会出现具有不同特征的集体模式，即交错跟随、串联跟随方阵和紧凑模式。它们与文献中的结果一致。通过涡体相互作用和推力分析了上述高效集体游动模式的水动力机制。我们还发现，DRL中的时间序列特征和水动力信息对于提高集体游泳的性能至关重要。我们的研究可以合理地解释相关实验中观察到的有争议的问题。本文可能有助于仿生鱼的设计。
