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A Feasibility Study of In Vivo Control and Tracking of Microrobot Using Taxicab Geometry for Direct Drug Targeting

机译:用紫外线靶向使用出租车几何体体内控制和跟踪微毒素的可行性研究

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摘要

In vivo direct drug targeting aims at delivering drug molecules loaded on microrobots to the diseased site using the shortest possible physiological routes, which potentially improves targeting efficiency and reduces systemic toxicity. It is thus essential to consider realistic in-body limitations for direct drug targeting applications. Here, we present a novel controller for microrobot maneuver by considering four key in vivo constraints: non-Euclidean structure of capillaries, irreversibility of blood flow, invisibility of microvasculature, and inaccuracy of microrobot tracking. We use the taxicab geometry of capillaries as the a priori knowledge for steering and tracking a microrobot in lattice-like vessels. Furthermore, we introduce a minimax repulsive boundary function to prevent the microrobot from getting too close to the boundaries imposed by the direction of blood flow. We also propose a novel Kalman filtering algorithm to reduce tracking error, while avoiding possible obstacles such as vessel walls without knowing their actual locations. The proposed control method consists of four modules, namely a model predictive control module for tumor targeting, a Kalman filtering module for microrobot tracking, a blind obstacle detection module, and a vessel structure estimation module. The interplay of these four modules offers successful maneuver and tracking of the microrobot while avoiding obstacles in a blind manner by utilizing the taxicab geometry of blood vessels. We present a comprehensive in silico simulation study to verify our designed controller.
机译:体内直接药物靶向旨在使用最短可能的生理途径将装载的药物分子递送到患病部位,这可能提高靶向效率并降低全身毒性。因此,需要考虑直接药物靶向应用的现实体内限制。在这里,我们通过考虑体内限制的四个关键来提出一个新的控制器:毛细血管的非Euclidean结构,血流量不可逆转,微血管内的不可见性,微管状跟踪的不恰当性。我们使用毛细血管的特征几何形状作为用于在格子状血管中转向和跟踪微机器的先验知识。此外,我们引入了最小的排斥边界功能,以防止微机管过于靠近血流方向施加的边界。我们还提出了一种新颖的卡尔曼滤波算法来减少跟踪误差,同时避免可能在不知道其实际位置的情况下诸如船壁的障碍物。所提出的控制方法由四个模块组成,即用于肿瘤靶向的模型预测控制模块,用于微机器跟踪的卡尔曼滤波模块,盲障障碍物检测模块和血管结构估计模块。这四个模块的相互作用提供了成功的操纵和跟踪微机器,同时通过利用血管的出租车几何形状以盲目的方式避免障碍物。我们在Silico仿真研究中提供了全面的验证我们设计的控制器。

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