用贝叶斯优化控制微泳器精准跟跑轨迹，无需复杂计算。
Optimal Control of Microswimmers for Trajectory Tracking Using Bayesian Optimization
- 用B样条参数化+贝叶斯优化求解最优控制
- 成功复现生物启发轨迹，适应壁面流体干扰
- 适用于从低维到高维模型的统一控制框架
微泳器轨迹跟踪在微机器人领域仍是关键挑战，因其低雷诺数动力学导致控制设计极为复杂。本文将轨迹跟踪问题建模为最优控制问题，采用B样条参数化与贝叶斯优化相结合的方法，在不需复杂梯度计算的前提下应对高计算成本。该方法应用于鞭毛磁性泳器，成功复现多种生物启发轨迹，与实验观测一致。进一步在三球体泳器模型上验证，表明其能适应并部分补偿壁面诱导的流体效应。该优化策略可一致应用于不同精度的模型，从低维常微分方程模型到高维偏微分方程仿真，展现出强鲁棒性与通用性。结果表明，贝叶斯优化是复杂流固耦合下微尺度运动最优控制的有力工具。
原文摘要 · Abstract (English)
Trajectory tracking for microswimmers remains a key challenge in microrobotics, where low-Reynolds-number dynamics make control design particularly complex. In this work, we formulate the trajectory tracking problem as an optimal control problem and solve it using a combination of B-spline parametrization with Bayesian optimization, allowing the treatment of high computational costs without requiring complex gradient computations. Applied to a flagellated magnetic swimmer, the proposed method reproduces a variety of target trajectories, including biologically inspired paths observed in experimental studies. We further evaluate the approach on a three-sphere swimmer model, demonstrating that it can adapt to and partially compensate for wall-induced hydrodynamic effects. The proposed optimization strategy can be applied consistently across models of different fidelity, from low-dimensional ODE-based models to high-fidelity PDE-based simulations, showing its robustness and generality. These results highlight the potential of Bayesian optimization as a versatile tool for optimal control strategies in microscale locomotion under complex fluid-structure interactions.
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