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This paper presents our work over the last decade in developing functional microrobotic systems, which include wireless actuation of microrobots to traverse complex surfaces, addition of sensing capabilities, and independent actuation of swarms of microrobots. We will discuss our work on the design, fabrication, and testing of a number of different mobile microrobots that are able to achieve these goals. These microrobots include the microscale magnetorestrictive asymmetric bimorph microrobot ( μ MAB), our first attempt at magnetic actuation in the microscale; the microscale tumbling microrobot ( μ TUM), our microrobot capable of traversing complex surfaces in both wet and dry conditions; and the micro-force sensing magnetic microrobot ( μ FSMM), which is capable of real-time micro-force sensing feedback to the user as well as intuitive wireless actuation. Additionally, we will present our latest results on using local magnetic field actuation for independent control of multiple microrobots in the same workspace for microassembly tasks.
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Accelerating the Design of Self-Guided Microrobots in Time-Varying Magnetic Fields
Mobile robots combine sensory information with mechanical actuation to move autonomously through structured environments and perform specific tasks. The miniaturization of such robots to the size of living cells is actively pursued for applications in biomedicine, materials science, and environmental sustainability. Existing microrobots based on field-driven particles rely on knowledge of the particle position and the target destination to control particle motion through fluid environments. Often, however, these external control strategies are challenged by limited information and global actuation where a common field directs multiple robots with unknown positions. In this Perspective, we discuss how time-varying magnetic fields can be used to encode the self-guided behaviors of magnetic particles conditioned on local environmental cues. Programming these behaviors is framed as a design problem: we seek to identify the design variables (e.g., particle shape, magnetization, elasticity, stimuli-response) that achieve the desired performance in a given environment. We discuss strategies for accelerating the design process using automated experiments, computational models, statistical inference, and machine learning approaches. Based on the current understanding of field-driven particle dynamics and existing capabilities for particle fabrication and actuation, we argue that self-guided microrobots with potentially transformative capabilities are close at hand.
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- Award ID(s):
- 2153202
- PAR ID:
- 10502006
- Publisher / Repository:
- ACS
- Date Published:
- Journal Name:
- JACS Au
- Volume:
- 3
- Issue:
- 3
- ISSN:
- 2691-3704
- Page Range / eLocation ID:
- 611 to 627
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1021/jacsau.2c00499
