用离散损失优化实现微流控中微型设备的高效导航
Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss
- 通过优化离散损失函数实现闭环控制
- 比强化学习快三个数量级，且在高维空间表现更优
- 适合复杂流场中的微型机器人路径规划
在微流控环境中对微观设备进行最优路径规划与控制，对靶向药物递送、环境监测等应用至关重要。由于微装置与流体相互作用的复杂性，实现高效导航极具挑战。本文提出一种闭环控制方法——基于离散损失优化（ODIL），兼顾动力学特性和路径目标。相比强化学习，该方法在鲁棒性上更优，速度提升达三个数量级，并在高维动作/状态空间中表现卓越，是复杂流场下导航的强大工具。
原文摘要 · Abstract (English)
Optimal path planning and control of microscopic devices navigating in fluid environments is essential for applications ranging from targeted drug delivery to environmental monitoring. These tasks are challenging due to the complexity of microdevice-flow interactions. We introduce a closed-loop control method that optimizes a discrete loss (ODIL) in terms of dynamics and path objectives. In comparison with reinforcement learning, ODIL is more robust, up to three orders faster, and excels in high-dimensional action/state spaces, making it a powerful tool for navigating complex flow environments.
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