Magnetic microrobots and nanorobots can be remotely controlled to propel in complex biological fluids with high precision by using magnetic fields. Their potential for controlled navigation in hard-to-reach cavities of the human body makes them promising miniaturized robotic tools to diagnose and treat diseases in a minimally invasive manner. However, critical issues, such as motion tracking, biocompatibility, biodegradation, and diagnostic/therapeutic effects, need to be resolved to allow preclinical in vivo development and clinical trials. We report biohybrid magnetic robots endowed with multifunctional capabilities by integrating desired structural and functional attributes from a biological matrix and an engineered coating. Helical microswimmers were fabricated from Spirulina microalgae via a facile dip-coating process in magnetite (Fe₃O₄) suspensions, superparamagnetic, and equipped with robust navigation capability in various biofluids. The innate properties of the microalgae allowed in vivo fluorescence imaging and remote diagnostic sensing without the need for any surface modification. Furthermore, in vivo magnetic resonance imaging tracked a swarm of microswimmers inside rodent stomachs, a deep organ where fluorescence-based imaging ceased to work because of its penetration limitation. Meanwhile, the microswimmers were able to degrade and exhibited selective cytotoxicity to cancer cell lines, subject to the thickness of the Fe₃O₄ coating, which could be tailored via the dip-coating process. The biohybrid microrobots reported herein represent a microrobotic platform that could be further developed for in vivo imaging–guided therapy and a proof of concept for the engineering of multifunctional microrobotic and nanorobotic devices.

Magnetically actuated small-scale robots have great potential for numerous applications in remote, confined, or enclosed environments. Multiple small-scale robots enable cooperation and increase the operating efficiency. However, independent control of multiple magnetic small-scale robots is a great challenge, because the robots receive identical control inputs from the same external magnetic field. In this article, we propose a novel strategy of completely decoupled independent control of magnetically actuated flexible swimming millirobots. A flexible millirobot shows a crawling motion on a flat plane within an oscillating magnetic field. Millirobots with different magnetization directions have the same velocity response curve to the oscillating magnetic field but with a difference of phase. We designed and fabricated a group of up to four heterogeneous millirobots with identical geometries and different magnetization directions. According to their velocity response curves, an optimal direction of oscillating magnetic field is calculated to induce a desired velocity vector for the millirobot group, one of which is nonzero and the others are approximately zero. The strategy is verified by experiments of independent position control of up to four millirobots and independent path following control of up to three millirobots with small errors. We further expect that with this independent control strategy, the millirobots will be able to cooperate to finish complicated tasks.

Untethered microrobots have attracted extensive attention due to their potential for biomedical applications and micromanipulation at the small scale. Soft microrobots are of great research importance because of their highly deformable ability to achieve not only multiple locomotion mechanisms but also minimal invasion to the environment. However, the existing microrobots are still limited in their ability to locomote and cross obstacles in unstructured environments compared to conventional legged robots. Nature provides much inspiration for developing miniature robots. Here, we propose a bionic quadruped soft thin-film microrobot with a nonmagnetic soft body and 4 magnetic flexible legs. The quadruped soft microrobot can achieve multiple controllable locomotion modes in the external magnetic field. The experiment demonstrated the robot’s excellent obstacle-crossing ability by walking on the surface with steps and moving in the bottom of a stomach model with gullies. In particular, by controlling the conical angle of the external conical magnetic field, microbeads gripping, transportation, and release of the microrobot were demonstrated. In the future, the quadruped microrobot with excellent obstacle-crossing and gripping capabilities will be relevant for biomedical applications and micromanipulation.

Magnetically actuated microswimmers have attracted researchers to investigate their swimming characteristics and controlled actuation. Although plenty of studies on actuating helical microswimmers have been carried out, robust closed-loop controls should be still explored for practical applications. In this paper, we proposed a data-driven model-free method using Image-Based Visual Servoing (IBVS), which uses features directly extracted in the image space as feedbacks. The IBVS method can eliminate camera calibration errors. We have demonstrated with experiments that the proposed IBVS method can enable velocity-independent path following of an arbitrarily given path on the plane, which permits a better experience of user interaction. The proposed control method is successfully applied to obstacle avoidance tasks and has the potential for the application in complex circumstances. This approach is promising for biomedical applications.

As the controller parameter adjustment process is simplified significantly by using learning algorithms, the studies about learning-based control attract a lot of interest in recent years. This article focuses on the intelligent servo control problem using learning from desired demonstrations. Compared with the previous studies about the learning-based servo control, a control policy using the broad learning system (BLS) is developed and first applied to a microrobotic system, since the advantages of the BLS, such as simple structure and no-requirement for retraining when new demos’ data is provided. Then, the Lyapunov theory is skillfully combined with the complex learning algorithm to derive the controller parameters’ constraints. Thus, the final control policy not only can obtain the movement skills of the desired demonstrations but also have the strong ability of generalization and error convergence. Finally, simulation and experimental examples verify the effectiveness of the proposed strategy using MATLAB and a microswimmer trajectory tracking system.