Young Jin Heo

RRT-based path planning with kinematic constraints of AUV in underwater structured environment

In this paper, we conduct Rapidly-exploring Random Trees(RRT)-based local path planning for an Autonomous Underwater Vehicle(AUV). RRT is a randomized sampling based data structure which can easily handle non-holonomic constraints such as kinematic model of the AUV. It is enable to solve the path planning problem efficiently in high-dimensional state space. We applied RRT to solve the local path planning problem of the AUV considering its kinematic model which has non-holonomic constraints. And then, the A* algorithm was used to find the shortest trajectory from the RRT which avoids obstacles in the known environment.

Real-time Image Processing for Microscopy-based Label-free Imaging Flow Cytometry in a Microfluidic Chip

Imaging flow cytometry (IFC) is an emerging technology that acquires single-cell images at high-throughput for analysis of a cell population. Rich information that comes from high sensitivity and spatial resolution of a single-cell microscopic image is beneficial for single-cell analysis in various biological applications. In this paper, we present a fast image-processing pipeline (R-MOD: Real-time Moving Object Detector) based on deep learning for high-throughput microscopy-based label-free IFC in a microfluidic chip. The R-MOD pipeline acquires all single-cell images of cells in flow, and identifies the acquired images as a real-time process with minimum hardware that consists of a microscope and a high-speed camera. Experiments show that R-MOD has the fast and reliable accuracy (500 fps and 93.3% mAP), and is expected to be used as a powerful tool for biomedical and clinical applications.

Robust control for valveless flow switching in microfluidic networks

We propose a novel flow control method to realize flow regulation and valveless flow switching in a microfluidic network. The proposed controller enables precise regulation of flow rates (or velocity fields) in micro-channels regardless of model uncertainties such as cross-sectional area and microchannel configuration. The controller can also minimize the pressure exerted on the microfluidic chip. The proposed system can dramatically improve performances of flow regulation and can overcome the limitations of conventional pump systems for microfluidic devices.

Tuning-free controller to accurately regulate flow rates in a microfluidic network

We describe a control algorithm that can improve accuracy and stability of flow regulation in a microfluidic network that uses a conventional pressure pump system. The algorithm enables simultaneous and independent control of fluid flows in multiple micro-channels of a microfluidic network, but does not require any model parameters or tuning process. We investigate robustness and optimality of the proposed control algorithm and those are verified by simulations and experiments. In addition, the control algorithm is compared with a conventional PID controller to show that the proposed control algorithm resolves critical problems induced by the PID control. The capability of the control algorithm can be used not only in high-precision flow regulation in the presence of disturbance, but in some useful functions for lab-on-a-chip devices such as regulation of volumetric flow rate, interface position control of two laminar flows, valveless flow switching, droplet generation and particle manipulation. We demonstrate those functions and also suggest further potential biological applications which can be accomplished by the proposed control framework.

Feedback control of inertial focusing using real-time extraction of equilibrium positions

Inertial microfluidics has facilitated promising applications of lab-on-a-chip devices for high-throughput biological analysis. However, as the mechanism of inertial microfluidics remains largely unknown, there are many difficulties in obtaining the desired equilibrium positions; hence, most researchers have tried to modify the channel geometry to obtain the desired equilibrium positions. In this paper, to improve the consistency of inertial focusing experiments, we apply feedback control to inertial focusing devices. The feedback control was realized by an image processing algorithm for real-time measurement of equilibrium positions using a high-speed vision system. The proposed image processing algorithm and feedback control scheme were verified through experiments.

On-chip automation of sequential flow switching with serially connectable fluidic monostable multivibrator

We propose a fluidic monostable multivibrator (fMMV) circuit, which is a new flow-switching mechanism in microfluidic chips. Flow control using parallel fMMV circuits does not require an external controller; this is a distinct advantage over various conventional micro-flow control methods. The fMMV circuit mainly consists of an inverter and an RC circuit. Each of the parts uses a microchannel as a resistor, a membrane chamber as a capacitor and a membrane valve as a transistor. The inverter acts as a trigger which opens the membrane valve and the RC circuit delays the closing of the valve, so that flow switching can occur over a time interval. A serial fMMV circuit can switch flows in multiple channels and regulate flowrates with pre-defined durations. By modifying the resistances in fMMV circuits, the flow rates and duration of each phase could be adjusted. For a proof of concept demonstration, we performed experiments using 3 parallel fMMV and 4 parallel fMMV circuit systems and these experiments show the applicability of fMMV circuit to various biochemical processes.

Modeling and simulation of flow rate in microfluidic networks

In this study, we propose a simulation model of fluid flow in a microfluidic network using electric circuit components. Laminar flow in a single microfluidic channel is described by the circuit components, and a microfluidic network is synthesized by integrating the proposed single channel model. We also applied the robust controller to the proposed microfluidic network model to verify the model and the controller by numerical simulations.