Overcoming Channel Uncertainties in Touchable Molecular Communication for Direct-Drug-Targeting-Assisted Immuno-Chemotherapy

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

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.

Blind Obstacle Avoidance Using Taxicab Geometry for NanorobotAssisted Direct Drug Targeting

In this paper, we present a novel controller for steering nanorobots in lattice-like vessel systems while avoiding potential obstacles such as the vessel walls without any prior knowledge of the obstacles' positions. The proposed control method consists of two sub-modules, namely a blind obstacle avoidance (BOA) and a model predictive control (MPC). In the case that a nanorobot might encounter an obstacle on its path, the BOA module is activated, which gives rise to a desirable heading angle to change the direction of the nanorobot's movement to bypass the obstacle. On the other hand, the MPC module offers a series of actuating field's directions that control the nanorobots' movement in the blood vessel with a grid structure representing potential paths of vascular growth, and introduces a repulsive boundary function to stop nanorobots from getting too close to the boundaries. This new formulation offers successful control and steering of nanorobots while avoiding obstacles in a blind manner by taking into account realistic in vivo physical constraints. Simulation results demonstrate the effectiveness of the proposed feedback control design.