In recent years, magnetic field-controlled microparticles have demonstrated their superiority in biomedical applications. To improve the operation efficiency and imaging effect of microparticles, increasing numbers of researchers are devoted to driving a swarm composed of a group of microparticles. In this study, a new strategy for autonomously manipulating microparticle swarms in liquids via an electromagnetic coil system was investigated. Compared with the commonly used rotating magnetic field composed of multiple electromagnetic coils, the oscillating magnetic field drive system formed by a single electromagnetic coil reduces the occupied space meanwhile ensures the sufficient working space, making the microparticle movement more flexible while providing greater feasibility for good compatibility with imaging systems. Through designing the parameters of the input current, a vortex-shaped swarm can be formed, and the movement of the entire swarm can be pulled by controlling the coil position. Owing to the rotation of microparticles, the coefficient of the control input is unknown uncertainty. To solve this uncertainty problem, a sliding mode controller is used, and the chattering caused by the sliding mode surface is reduced using a saturation function. Lastly, the effectiveness of the proposed strategy is verified by simulation and experiments. Note to Practitioners—The motivation of this article is mainly to solve the problem of magnetic microparticle aggregation, aiming to form a stable swarm and automatically control the movement of the swarm. Most of the existing methods of aggregating microparticles to form a swarm are based on the creation of a rotating magnetic field by Helmholtz coils, which leads to the problem of spatial limitation. To solve this problem, this research uses one single solenoid to form a stable swarm, which provides more space for imaging equipment and objects requiring operation. The controlled movement of the stable swarm is achieved by manipulating the solenoid with a sliding mode controller. It reduces the impact of system uncertainties, allowing the swarm to automatically track a predefined path accurately. This will provide a new idea for the application of microparticles in the biomedical field.

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.

This study investigates the use of a tendon-driven continuum robot to deform a soft object, whereas the robot body is deformed into an arbitrary shape to adapt to a constrained environment. A dynamic estimator (DE) is developed to approximate the Jacobian matrix that associates the actuator input with the deformed output of the soft object. This helps solve the singularity problem and reduce the effects of noise. Then a visual predictive controller (VPC) with a reference trajectory is developed to ensure a smooth operation. A linear extended-state observer (ESO) is further designed to measure the robot states, such that the controller can compensate for the estimation error. Simulations and experiments are performed to verify the proposed control approach.Note to Practitioners—The motivation of this article is to solve the problem of automatic deformation control of soft objects in restricted environments. The existing soft object deformation control is achieved using rigid robots in an open environment, but rigid robots are difficult to use in specific applications where the environment is restricted (e.g., natural orifice surgery). Flexible continuum robots with mechanical compliance can manipulate soft objects in narrow spaces. However, due to environmental constraints, the robot body may be deformed into any shape regardless of the input of the actuator. To solve the problem, this research provides a new visual servo control strategy that deforms soft objects using a continuum robot in a restricted environment. The proposed method can control a flexible robot to manipulate soft objects while taking into account the change in the robot configuration in a restricted environment.