Learning to swim in potential flow
Learning to swim in potential flow
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DOI：
10.1103/physrevfluids.6.050505
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发表时间：
2021-05-12
影响因子：
2.7
通讯作者：
Kanso, Eva
中科院分区：
文献类型：
--
作者：
Jiao, Yusheng;Ling, Feng;Kanso, Eva
鱼靠摆动身体游动。这些推进运动需要与流体环境相互作用的物体的协调形状变化，但导致强劲的转弯和游泳运动的具体形状协调尚不清楚。为了解决水下运动规划问题，我们提出了一个简单的三连杆鱼在势流环境中游泳的模型，并使用无模型强化学习进行形状控制。我们得出了两个游泳任务的最佳形状变化：朝着所需的方向游泳和向已知目标游泳。这个鱼模型属于几何力学中的一类问题，称为无漂移动力系统，它允许我们用几何相位来分析鱼在形状空间上的游动行为。这些几何方法在存在漂移时不太直观。在这里，我们使用形状空间分析作为一种工具来评估、可视化和解释在没有漂移的情况下通过强化学习获得的控制策略。然后，我们检验这些策略对漂移相关扰动的稳健性。虽然鱼对漂移本身没有直接的控制，但它学会了利用适度漂移的存在来到达目标。
Fish swim by undulating their bodies. These propulsive motions require coordinated shape changes of a body that interacts with its fluid environment, but the specific shape coordination that leads to robust turning and swimming motions remains unclear. To address the problem of underwater motion planning, we propose a simple model of a three-link fish swimming in a potential flow environment and we use model-free reinforcement learning for shape control. We arrive at optimal shape changes for two swimming tasks: swimming in a desired direction and swimming towards a known target. This fish model belongs to a class of problems in geometric mechanics, known as driftless dynamical systems, which allow us to analyze the swimming behavior in terms of geometric phases over the shape space of the fish. These geometric methods are less intuitive in the presence of drift. Here, we use the shape space analysis as a tool for assessing, visualizing, and interpreting the control policies obtained via reinforcement learning in the absence of drift. We then examine the robustness of these policies to drift-related perturbations. Although the fish has no direct control over the drift itself, it learns to take advantage of the presence of moderate drift to reach its target.