Autonomous Vision-Based Magnetic Microrobotic Pushing of Micro-Objects and Cells

TL;DR

This work presents a vision-based, model-free microrobotic pushing algorithm that autonomously transports micro-objects and cells using a rotating magnetic field to steer a spherical magnetic microrobot. A guiding corridor constrains the object, while spin-induced micro-vortices readjust the object toward a predefined trajectory; the approach is validated through passive particle and single-cell experiments, reporting completion times and mean absolute errors across actuation frequencies and corridor widths. The results show faster actuation reduces travel time, tighter corridors improve accuracy, and the method maintains cell viability, demonstrating potential for biomedical applications. The study contributes a robust, data-driven control framework for microscale manipulation with implications for tissue engineering, drug delivery, and synthetic biology, and outlines clear paths for future improvements including reinforcement learning and multi-object manipulation.

Abstract

Accurate and autonomous transportation of micro-objects and biological cells can enable significant advances in a wide variety of research disciplines. Here, we present a novel, vision-based, model-free microrobotic pushing algorithm for the autonomous manipulation of micro objects and biological cells. The algorithm adjusts the axis of a rotating magnetic field that in turn controls the heading angle and spin axis of a spherical Janus rolling microrobot. We introduce the concept of a microrobotic guiding corridor to constrain the object and to avoid pushing failures. We then show that employing only two simple conditions, the microrobot is able to successfully and autonomously push microscale objects along predefined trajectories. We evaluate the performance of the algorithm by measuring the mean absolute error and completion time relative to a desired path at different actuation frequencies and guiding corridor widths. Finally, we demonstrate biomedical applicability by autonomously transporting a single biological cell, highlighting the methods potential for applications in tissue engineering, drug delivery and synthetic biology.

Figures (8)

• Figure 1: Graphical Abstract
• Figure 2: Experimental Setup. a) Arduino control module and 3D Helmholtz coil system mounted on a Zeiss Axiovert 200 inverted microscope. b) System information flowchart.
• Figure 3: a) Procedure for fabricating magnetic rolling microrobots using electron beam vapor deposition. b) Image and positional tracking coordinate system in black. Actuation coordinate system in blue. Heading angle $\alpha$, attitude angle $\gamma$ and resulting axis of rotation labeled for different actuation modes. c) Brightfield, robot mask and cell mask screenshots. d) Speed vs frequency graph of 10 $\mu$m magnetic rolling microrobot in DI on a glass microscope slide. The step-out frequency was measured to be approximately 60Hz.
• Figure 4: Schematic of pushing algorithm
• Figure 5: a) Pushing completion times in seconds following a circular trajectory of 100 nodes vs the rotating magnetic field frequency at different corridor widths. b) Mean absolute error ($\mu$m) between the actual path and the desired path vs rotating magnetic field frequency (Hz) and 3 different corridor widths. c) Illustrative bright-field microscopy screenshots of different corridor widths with respect to a 10 $\mu$m microrobot and a 10 $\mu$m passive particle.