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Self-Propelling Robotic Hydrofoil Arrays: Mechanics, Efficiency, and Optimization

机译：自走式机器人水翼阵列：力学，效率和优化。

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摘要

Mathematical models for the self-propulsion of articulated bodies in fluids at low Reynolds number can be interpreted geometrically in terms of connections in principal bundles. Visualization of the local curvature of a connection in this context can serve as a tool for motion planning. We use laboratory data from physical experiments to estimate the local curvatures of connections for two planar robotic systems --- one canonical, one novel --- propelling themselves through fluids at Reynolds numbers of order one. In each case, the estimated curvature agrees with that predicted by a trusted theoretical model, validating our approach as a new strategy for extracting curvature estimates from physical data when theoretical model are unavailable.;For one of the two robotic systems in question, we also employ reinforcement learning to identify piecewise constant control inputs that optimize unidirectional translation. Our results show that optimal locomotion over long time scales is achieved through cyclic actuation that exploits curvature according to the geometric theory --- and that exploits the nontrivial topology of the manifold of internal robot shapes --- while optimal locomotion over short times scales requires adaptation for different initial conditions.;We also investigate the self-propulsion of pitching hydrofoils at higher Reynolds numbers through physical experiments with freely swimming two- and four-hydrofoil arrays, resembling simplified fish schools, in a pool of water. These experiments highlight the influence of hydrodynamic coupling among individual foils on the overall speed and efficiency of such arrays. We modulate this coupling by varying the spacing among foils and by varying the manner in which individual foils pitch as functions of time, recording both swimming speed and power consumption to identify optimal cooperative pitching patterns.
机译：在低雷诺数下，流体中的铰接体自动推进的数学模型可以用主束中的连接进行几何解释。在这种情况下，连接的局部曲率的可视化可以用作运动计划的工具。我们使用来自物理实验的实验室数据来估计两个平面机器人系统（一种规范的，一种新颖的）的连接的局部曲率，即通过雷诺数为一的流体推动自身。在每种情况下，估计的曲率都与可信赖的理论模型所预测的曲率一致，从而验证了我们的方法是一种在理论模型不可用时从物理数据中提取曲率估计值的新策略。对于上述两个机器人系统之一，我们还利用强化学习来识别可优化单向平移的分段恒定控制输入。我们的结果表明，通过根据几何理论利用曲率的循环驱动实现了长时间尺度上的最佳运动-并利用了内部机器人形状的流形的非平凡拓扑-而短时间尺度上的最优运动则需要我们还通过在自由水池中自由游泳两个和四个水翼阵列（类似于简化的鱼群）的物理实验，研究了雷诺数更高的俯仰水翼的自推进力。这些实验突出了各个箔之间的流体动力耦合对此类阵列的整体速度和效率的影响。我们通过改变金属箔之间的间距以及改变各个金属箔的间距随时间变化的方式来调制这种耦合，同时记录游泳速度和功耗以确定最佳的协作间距模式。

著录项

  • 作者

    Bhansali, Rakshit.;

  • 作者单位

    The University of North Carolina at Charlotte.;

  • 授予单位 The University of North Carolina at Charlotte.;
  • 学科 Mechanical engineering.;Robotics.
  • 学位 M.S.
  • 年度 2018
  • 页码 76 p.
  • 总页数 76
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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