磁驱动微型机器人生物操作系统的开发与实现

Hui Tang, Yangmin Li, Yuchun Song
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引用次数: 1

摘要

近年来,生物操纵微机器人已成为一种有前途的执行实际生物微操作任务的工具,引起了学术界和工业界的广泛关注。在微流控芯片上进行生物微操作可以提高生物操作的效率和稳定性,是该领域研究的新热点。本研究旨在开发一种磁驱动微型机器人的微操作系统,在微流控芯片上实现实际的生物操作。提出了一种具有大工作空间和微纳运动精度的柔性微机械臂。经过一系列机构优化设计和解析建模,利用ANSYS Workbench平台对微机械臂的定位性能进行了评价。在机构制作完成后,考虑压电陶瓷(PZT)作动器固有的速率滞后效应,提出了一种结合极限学习机(ELM)和人工神经网络(ANN)的混合视觉伺服控制(HVSC)策略。随后,利用所设计的控制策略成功地进行了一系列实际的生物操作实验。理论分析和视觉跟踪结果一致表明,所开发的系统具有令人满意的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and implementation of a biomanipulation system with magnetic-driven microrobots
Recently, biomanipulation microrobots have attracted much attention in both academia and industry since they have become a promising tool to perform the practical biological micromanipulation tasks. In this field, biological micromanipulation on micro-fluidic chip emerge as a new focus since it can be used to improve the biomanipulation efficiency and stability. This study aims to develop a micromanipulation system with magnetic driven microrobots to implement the practical biomanipulations on a micro-fluidic chip. A novel flexure-based micromanipulator with large-workspace and micro/nano scale motion accuracy is proposed in this paper. After a series of mechanism optimal designs and analytic modeling, the positioning performance of the micromanipulators are evaluated by using the ANSYS Workbench Platform. After the mechanism fabrication, with the consideration of rate-dependent hysteresis effect inherent in piezoelectric ceramics (PZT) actuators, a novel hybrid visual servo control (HVSC) strategy combining with the Extreme Learning Machine (ELM) and the Artificial Neural Network (ANN) is proposed. Afterwards, a series of practical biomanipulation experiments are successfully implemented by using the designed control strategy. Both theoretical analysis and visual tracking results uniformly demonstrate the satisfactory performance of the developed system.
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