Autonomous Trajectory Tracking of Bioinspired Soft Microrollers Through a Novel Direction-Speed Decoupled Control Strategy

Microrollers hold great potential for biomedical applications for its mobility and environmental adaptivity. Autonomous trajectory tracking is crucial for microrollers to conduct medical tasks precisely and safely in complex unstructured physiological environments but remains challenging. In this article, we propose a novel direction-speed decoupled control strategy for autonomous trajectory tracking of microrollers. A modified kinematic model based on extended high-order states is proposed to capture the dynamic characteristics of microrollers, enhancing adaptability to unstructured environments. Decoupled direction and speed controllers adjusting the lateral and longitudinal motions of microrollers are designed based on separate error states to offer improved autonomy and performance in trajectory tracking. A real-time online parameter identification approach is formulated that skips tedious preliminary trials and could generally be used for other variations of microrollers and environments. The trajectory tracking performance of the proposed control strategy is evaluated experimentally on bioinspired magnetic microrollers (BMMs) in biological-like environments. The BMM successfully navigates through cell clusters and reaches the target destination, showing the potential of the proposed control strategy in real physiological environments.
Note to Practitioners
—This article is motivated by the growing interest in enhancing the autonomy of microrollers in biomedical applications. Controlling the microrollers along an intended trajectory is crucial for applications such as targeted drug delivery and minimally invasive surgery. Traditional control strategies focus solely on spatial path following, disregarding the motion speed of the microrollers. Moreover, kinematic model generally neglects the high-order states for simplification, which cannot facilitate microllers to track rapid changing trajectories in unstructured environments. The identification in real-time and amidst unknown disturbances is also crucial for microrollers to operate efficiently in complex physiological environments. However, existing control strategies rely heavily on preliminary experiments to obtain kinematic parameters for each form of microrollers and environments, which is impractical for real-world applications. To address these challenges, we propose a novel control strategy encompassing an extended state kinematic model, decoupled direction and speed controllers, and an online approach to updating kinematic parameters. Precise trajectory tracking containing direction and speed of BMM is achieved in a biological-like environment consisting of real cell clusters, showing improved performances compared to conventional control methods, and can be adapted to other forms of microrollers without preliminary trials.