In vivo manipulation of microparticles, such as biological cells and drugs, has attracted considerable attention in recent years. This paper presents the development of robot-aided manipulation technology that can control targeted microparticles to move a relatively long distance in an in vivo environment. The field of view can be updated online, such that the controlled microparticle can be tracked automatically in transportation. Simultaneous localization and mapping for in vivo applications are first investigated. Based on the in vivo map, an artificial potential field-based controller with disturbance compensation is developed to navigate microparticles in vivo. Experiments on navigating single cells in living zebrafish embryos by using optical tweezers manipulator are performed to demonstrate the effectiveness of the proposed control approach in a dynamic in vivo environment.

In vivo manipulation of biological cells has attracted considerable attention in recent years. This process is particularly useful for precision medicine, such as cancer target therapy. Robotics technology is becoming necessary to stably and effectively manipulate and control single target cells in a complex in vivo environment. This paper presents a robot-aided optical tweezers-based manipulation technology that serves a function in the transport of single biological cells in vivo. An enhanced disturbance compensation controller is developed to minimize the effect of fluids (e.g., blood flow) on the cell. The method has exhibited advantages of flexibility in adjusting cell tracking trajectory online and the capability to minimize steady-state error and eliminate overshoot. Simulations and experiments of tracking single target cells in living zebrafish embryos have demonstrated the effectiveness of the proposed approach in a dynamic in vivo environment.

As rapid development of precision medicine, in vivo manipulation of micro/nano-scaled particles have attracted increasing attention in recent years. To accommodate complex in-vivo environment, robot-aided automated manipulation technology is highly demanded in trapping and controlling micro/nano-particles stably and effectively. This paper presents an in-vivo cell manipulation system, where a disturbance compensation controller is utilized to minimize the effect of fluid (e.g., blood flow) on the cell. The controller has exhibited advantages in adjusting cell tracking trajectory online, minimizing the steady-state error, and eliminating overshoot. Simulation and experimental results verify the performance of the controller.

This paper presents a seven-step pedagogical model for introducing 3D printing technology to students with different levels of technological literacy in a freshman level course. The model was derived from a classic instructional design theory on the development of intellectual skills, the Conditions of Learning, with consideration of the technical requirements of 3D printers. It aimed to tackle the practical problems arose when course instructors adopted 3D printing as learning activities in classes, such as the diversity of students' technical backgrounds, the low throughput of 3D printers and the difficulty in integrating 3D printing into learning task. The model was piloted for two years in a General Education course under the engineering domain that opened to all freshmen. Most of the students had no prior experience in 3D printing and all of them agreed that they have had a good understanding of the technology after completed the course. They reported that the learning task supported the development of their innovative ideas and enhanced their learning motivations. However, their perceptions of the workload vary.

Few of the current injection technologies can be applied to those human cells whose diameters are ranged about 10-25 (im only. This paper reports our most recent effort in developing a robot-aided microinjection system to solve the challenging problem of automated injection on human cells. A unique microfluidic cell holding chip is designed and fabricated to trap the single cells in the predefined docking area. Imaging processing technique is used to recognize automatically the target cells to be injected. A microrobot system equipped with a micropipette is used to perform the injection tasks on these target cells. Injection experiments on human embryonic stem cells (hESCs) (ranged about 17-25μm) are performed to demonstrate the effectiveness of the proposed microinjection system.

In this paper, we demonstrate the effectiveness of robotic cell stretching with optical tweezers for biomechanical characterization. Optical traps serve as end-effectors to manipulate micro-beads attached to the cell surface. The dynamics of the cell-bead mixture during cell stretching is investigated for the first time. Based on our previous work, cell stiffness is extracted and biomechanical properties of cells can be characterized. Our study shows that the modeling results agree with the experimental data. Further, the area compressibility moduli of two types of cells, human embryonic stem cells (hESC) and hESC-derived cardiomyocytes (hESC-CM), are characterized. The results indicate that undifferentiated stem cells are much softer than differentiated ones, which provides an important insight into the cell mechanics during hESC differentiation. In summary, this paper successfully demonstrates that the robot-tweezer system can manipulate biological cells effectively to characterize biomechanical properties of living cells.

Cell manipulation has received considerable attentions in recent years. Most of cell manipulations are performed manually without guarantee of high precision and high throughput. This paper reports our latest research on integrating robotics technologies into optical tweezers system for manipulation and biomechanical characterization of human red blood cells (RBCs). We first demonstrate the effectiveness of the robot-tweezers system in manipulation of micro-beads, which is followed by stretching RBCs to different levels of deformations. The whole manipulation process is conducted with visual guidance and position feedback control, where the cell stretching direction is determined automatically through image analysis. The relationship between the stretching force and the induced deformation is obtained through force calibration and image processing. To characterize the mechanical properties of RBCs from the obtained experimental results, a mechanical model based cell property characterization strategy is introduced. Comparing the modeling results to the experimental data, the mechanical properties of human RBCs are characterized. In conclusion, this study demonstrates that the robotic manipulation technology with optical tweezers can be used to manipulate biological cells, and further, to characterize the biomechanical properties based on the cell mechanical model.

Robotic cell injection is a technique that employs automated device to insert substances into a single living cell with a fine needle. Most existing microinjection methods are based on position control without explicit regulation of the injection force. The injection force, if not controlled properly, may destroy the cell and lead to death of the cells. In this paper, we propose a force control based cell injection technology to explicitly regulate the injection force to follow the desired force trajectory during the injection. Any desired force trajectory that is twice continuously differentiable can be realized by the proposed approach. The convergence of the force tracking algorithm is provably guaranteed. Experiments performed on a laboratorial robotic cell injection system demonstrate the effectiveness of the proposed approach.

In this paper, a vision-based impedance control algorithm is proposed to regulate the cell injection force, based on dynamic modeling conducted on a laboratory test-bed cell injection system. The injection force is initially calibrated to derive the relationship between the force and the cell deformation utilizing a cell membrane point-load model. To increase the success rate of injection, the injector is positioned out of the focal plane of the camera, used to obtain visual feedback for the injection process. In this out-of-plane injection process, the total cell membrane deformation is estimated, based on the$X-Y$coordinate frame deformation of the cell, as measured with a microscope, and the known angle between the injector and the$X-Y$plane. Further, a relationship between the injection force and the injector displacement of the cell membrane, as observed with the camera, is derived. Based on this visual force estimation scheme, an impedance control algorithm is developed. Experimental results of the proposed injection method are given which validate the approach.

Biological cell injection is laborious work that requires lengthy training and suffers from a low success rate. In this paper, a robotic cell-injection system for automatic injection of batch-suspended cells is proposed. To facilitate the process, these suspended cells are held and fixed to a cell array by a specially designed cell-holding device, and injected one by one through an ldquoout-of-planerdquo cell-injection process. A micropipette equipped with a polyvinylidene fluoride microforce sensor to measure real-time injection force is integrated in the proposed system. Through calibration, an empirical relationship between the cell-injection force and the desired injector pipette trajectory is obtained in advance. Then, after decoupling the out-of-plane cell injection into a position control in theX-Yhorizontal plane and an impedance control in theZ-axis, a position and force control algorithm is developed to control the injection pipette. The depth motion of the injector pipette, which cannot be observed by microscope, is indirectly controlled via the impedance control, and the desired force is determined from the onlineX-Yposition control and cell calibration results. Finally, experimental results demonstrate the effectiveness of the proposed approach.

Automatic cell injection has been the focus of many researches and commercial development for several years. In this paper, a robotic cell injection system for automatic batch injection of suspended cells is developed. To facilitate the process, these suspended cells are held and fixed to a cell array by a specially designed cell holding device, and injected one by one through an “out-of-plane” cell injection process. A micropipette equipped with a PVDF micro force sensor is integrated in the proposed system. The force sensor is utilized to measure real time injection force applied to the cells during injection process. Through calibration of the relationship between the cell injection force and the desired injector pipette trajectory, a position (vision) and force control algorithm is proposed and applied to the motion control of the injection pipette in three-coordinate directions during an injection process. The out-of-plane cell injection task is decoupled into a position control in X-Y horizontal plane and an impedance force control in Z-axis. The depth motion of the injector pipette, a common problem of three-dimensional micromanipulation, is indirectly controlled by the force control. Finally, experimental results are given to demonstrate the effectiveness of the proposed approach.

Robotic cell microinjection is a technique that employs an automatic method to insert substances into a single living cell with a fine needle. Most available microinjection methods are based on position/velocity tracking, which make it incapable of controlling the injection force. The uncontrolled injection force, however, may destroy the cell and lead to the death of the cell. In this paper, a new adaptive force tracking algorithm within the impedance control framework is proposed to control the injection force applied on the cell. A target impedance is specified in the inner-loop and a trajectory modifying controller is design in the outer-loop for time varying ramp force tracking. The adaptive technique is also used to compensate for the uncertainty of the cell membranepsilas stiffness, the convergence of the system is provably guaranteed. Experiments performed on a laboratorial robotic cell injection system demonstrate the effectiveness of the approach.

In this paper, we present a relational-tubular (ReTu) deformable model for segmenting a complex and the entire tubular network structure with branches in close proximity of each other. Specifically, we incorporate a priori knowledge of the target anatomy structure as well as the spatial relationship between branches to reduce possible segmentation errors due to the effects of a variety of imaging artifacts and noise. To get more robust description of the data properties than a simple 3D edge map, a new data energy functional is proposed based on testing the volumetric density within the model cross-sections. The deformation process is formulated as a two-stage procedure: tubular medial axis deformation and tubular surface deformation. The efficiency of this approach is demonstrated by our experiments which show that satisfactory quantifications of the entire zebrafish vasculature recorded from the fluorescence confocal microscope. The experiments also demonstrate the robustness of our deformable model in the presence of complex issue structure that adhered to the vessel branches.