This paper proposes a practical micropipette-image 2-D pixel and 3-D spatial coordination) calibration method indispensable to somatic cell microinjection, leveraging advancements in modern motorized micromanipulators. The method determines the depth information of the micropipette in the microscope field by assessing the contact between the micropipette tip and the bottom of the culture dish. It uses recoverable deformation upon contact as a criterion for precise positioning, ensuring the tip is on the dish’s bottom surface and in the microscope’s focus plane. Additionally, the paper introduces an on-the-spot method for breaking a micropipette tip and a preprocessing technique for somatic cells. The proposed micropipette tip-breaking method, using a low-cost acrylic ring, overcomes previous drawbacks and proves quick and user-friendly. The preprocessing technique converts fully adherent somatic cells into semi-adherent cells, increasing cell thickness for easier puncturing. Combining these techniques, the study validated the approaches through over 900 injections on human dermal fibroblast (HDF) cells, achieving a success rate of 53.3% and a survival rate of 95.8%. Note to Practitioners–This study was initiated to address the challenge of establishing accurate mapping relationships between observed 2-D coordinates of a micropipette tip in microscope vision and 3-D positional data obtained from an external controller of an XYZ micromanipulator. However, its relevance extends beyond somatic cell microinjection to other micromanipulation applications like micro-assembly. Common calibration methods involve motorized objectives or stereo microscopic vision, whereas our paper proposes a practical approach utilizing recoverable deformation upon tip contact with the dish bottom for precise positioning. The paper mathematically characterizes relationships between the pixel coordinate system and spatial coordinate systems, demonstrating problem-solving by ensuring the tip aligns with the dish’s bottom surface and the microscope’s focus plane. Although experiments affirm feasibility, refinement and testing in other micromanipulation applications are pending. Future research aims to enhance system modeling and calibration accuracy.

Navigation control of microrobots in vivo has great potential in precision medicine and has attracted considerable attention in recent years. The control performance of the existing methods is considerably affected by hysteresis nonlinearity. This article presents a robust control method that can overcome hysteresis influence in navigating a microrobot actuated by an electromagnetic coil system. A motion planner that combines the breadth-first search (BFS) method and genetic algorithm (GA) is used to plan a reliable and flexible trajectory for the microrobot navigation. To compensate for hysteresis nonlinearity existing in the system, the Prandtl–Ishlinskii (PI) model is introduced. A robust controller that integrates adaptive sliding mode control (ASMC) and nonlinear disturbance observer is designed to guarantee the stability and accuracy of the microrobot in motion. Experiments have been performed to demonstrate the effectiveness of the proposed approach. The success of this research will advance the microrobot navigation for in vivo applications. Note to Practitioners—The motivation of this article is to eliminate the effect of hysteresis caused by the electromagnetic system on microrobot motion. Existing research on hysteresis compensation mostly uses inverse model or online model identification, which is not suitable for real-time control of the microrobot. In addition, the disturbance and uncertainty caused by the microfluidic environment at different flow rates will also affect the control performance of the microrobot. To solve the above problems, an adaptive robust control method is developed in this article. Experimental results have shown that the proposed method can successfully control the microrobot in the in vitro and in vivo environment.

Automatic 3D navigation control of microrobot in large microenvironment is one of the primary challenges hindering its applications. In this article, we present a systematic approach to use an electromagnetic manipulation system to deal with this challenge. Two movable orthogonal microscopic cameras scan the microenvironment on the top view and side view to build a stereo occupancy map automatically. The initial position of microrobot can be located on the basis of the similarity curves generated by the scanning sequences. In accordance with the distribution characteristic of electromagnetic fields in workspace, an enhanced rapidly exploring random tree algorithm is proposed to avoid obstacles in the complex environment. To ensure continuous visual servo in wide range, a field-of-view tracking method is developed using a specific image definition evaluation algorithm. A prescribed performance controller with disturbance observer is designed, which guarantees that the microrobot can remain in the view of microscopic cameras, and the transient and steady-state performance of the system can satisfy the expected requirements. A set of experiments are performed to verify the effectiveness of the proposed automatic 3D navigation strategy. Experimental results show that the microrobot can navigate automatically in a$\boldsymbol{12}\,\times 10\,\times \text{10-mm}$microenvironment containing obstacles, and achieve reliable field-of-view tracking and path following at high speed.