Cell aspiration is a common technique in cell manipulation for cell transfer or intracellular property measurement. In this paper, we present a robotic micromanipulation system for cell aspiration and positioning by a micropipette. Considering the relative motion of the object and the fluid, we first establish an overall dynamic model of microbead motion inside and outside the micropipette based on computational fluid dynamics (CFD). Then we design an adaptive sliding mode controller (ASMC) for microbead aspiration outside the micropipette and positioning inside the pipette based on the dynamic model. The controller is proven to achieve asymptotic stability by Lyapunov techniques. Simulation and experimental results demonstrate the effectiveness of the fluid model and the performance of the designed control system.

Single-cell transportation is one of the most common cell operations. Transporting cells with micropipettes is convenient for a wide range of biomedical applications. For high-efficiency cell transportation, the cells must be aspirated into the orifice of a micropipette. However, this is very difficult to achieve, as there is relative movement between the cell and the culture medium when the fluid drives the cell in the culture medium. It is crucial to use cell dynamics rather than fluid dynamics as the control objects to improve control performance and stop the cell immediately when it approaches the micropipette. In this study, the cell dynamics were modeled using a second-order model by integrating the dynamic model between the fluid and the cell into a first-order fluid dynamic model. A backstepping controller-based extended state observer was proposed to control the cell movement inside the micropipette. Experiments demonstrated that the proposed controller could aspirate cells into the orifice of the micropipette with high accuracy and no overshoot. Furthermore, the proposed controller was applied to automated somatic cell nuclear transfer, and it significantly boosted operational efficiency. Note to Practitioners —The need to apply advanced automation methods to transfer cells in life sciences has increased at a steady pace. The key feature of such systems is the ability to select and transfer cells at a predetermined position in space and time for biological applications. We propose a cell positioning control method based on vision-guided robotics that can directly aspirate cells to specified positions near the orifice of a micropipette. In somatic cell nuclear transfer, the proposed method of transferring somatic cells into oocytes occurs at a faster pace than manual operation. This provides essential functionality for single-cell transfer and is an appropriate technology for practitioners with this functional requirement.

Oocyte enucleation is a critical procedure for somatic cell nuclear transfer. Yet, the main steps of oocyte enucleation are still manually operated, which presents several drawbacks such as low precision, high repetition error, and long training time for operators. For improving the operation efficiency and success rate, a robotic precise oocyte blind enucleation method is presented in this paper. The proposed method involves the following key techniques: oocyte translation control, oocyte immobilization and penetration control, and enucleation volume control based on the adaptive slide mode. Compared with the manual blind enucleation method, the proposed robotic blind enucleation method reduced the operation time by 44.5% (manual method: 62 s vs. proposed method: 34.4 s), increased the accuracy of enucleation by 83.1% (manual method: 30.7 vs. proposed method: 5.2), increased the success rate from 80% to 93.3%, and increased the cleavage rate from 41.7% to 63.3%.

Measuring the mechanical force applied to a cell during penetration has long been studied. However, existing sensor methods can disturb or even prevent a planned cell surgery because of their physical units. We propose a contactless mechanical force estimation method based on an improved point-load model and cell contour detection. We extended the point-load model to large cell deformations and obtained a relationship between the mechanical force and the morphology of the deformed cell. We designed two cell contour detection methods for visually detecting the deformation of different cell morphologies to obtain the geometric parameters required by the model. Finally, we carried out penetration experiments on porcine oocytes and zebrafish embryos. The contour detection methods showed a consistency of up to 99.29% compared with manual marking, and the force estimation accuracy was 1.05 μN. We compared the estimation results of our method with measurements taken in real experiments and confirmed that our method accurately estimated the mechanical force applied to the cell without contact. Thus, it may be applied to estimating the applied force during cell surgery in real time.

In this paper, robust $H_{\infty }$ control for fractional-order switched systems (FOSSs) with uncertainty is studied. Firstly, the fractional-order switching law for FOSSs is proposed. Then, $H_{\infty }$ control for FOSSs is proven based on the switching law and linear matrix inequalities (LMIs). Moreover, $H_{\infty }$ control for FOSSs with a state feedback controller is extended. Furthermore, the LMI-based condition of robust $H_{\infty }$ control for FOSSs with uncertainty is proven. Furthermore, the condition of robust $H_{\infty }$ control is proposed to design the state feedback controller. Finally, four simulation examples verified the effectiveness of the proposed methods.