Control and Autonomy of Microrobots: Recent Progress and Perspective

Jiang, Jialin; Yang, Zhengxin; Ferreira, Antoine; Zhang, Zifeng

doi:10.1002/aisy.202100279

Cited by 76 publications

(52 citation statements)

References 260 publications

(341 reference statements)

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“…As outlined above, control and automation of acoustic robotic systems has remained a fundamental challenge, [70] Adv. Mater B-E) Respectively represent the velocity responses from PZTs 1-4, and were used to determine each PZT's resonance frequency, which corresponds to the frequency having the highest velocity-weighted sample density.…”

Section: Global Dynamicsmentioning

confidence: 99%

Ultrasound Microrobots with Reinforcement Learning

Schrage

Medany

Ahmed

2023

Adv Materials Technologies

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Ultrasound is an attractive modality for controlling micro/nanorobots due to penetrating deep into tissue, not being affected by the opaque nature of animal bodies, and generating a broad range of forces. However, ultrasound microrobots have poor navigation capabilities and the number of parameters involved in its motion make it extremely challenging for a human controller to accurately predict and manually correct the microrobot's position in real time. Herein, reinforcement learning control strategy is implemented to learn microrobot dynamics by accurately identifying micro/nanorobots (object detection and tracking) and manipulating them with ultrasound. This work demonstrates autonomous navigation of ultrasound microrobots in a fluidic environment. The propulsion strategy relies on the combined action of the primary and secondary radiation forces. Microswarms are formed through the secondary acoustic radiation force, while the primary acoustic radiation force guides the microrobots along a desired trajectory. Microrobots are trained using more than 100 000 images to study their unexpected dynamics. The control of the microrobots is validated, illustrating a good level of robustness and providing the microrobots with computational intelligence that enables them to navigate independently in an unstructured environment without outside assistance.

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Section: Global Dynamicsmentioning

confidence: 99%

Ultrasound Microrobots with Reinforcement Learning

Schrage

Medany

Ahmed

2023

Adv Materials Technologies

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“…For magnetic microrobots, these key elements—sensor, actuator, controller—are often external to the particle itself ( Figure 2 a,b). 76 Using microscopy information (sensor), a computer algorithm (controller) alters the driving magnetic field (actuator) to achieve the desired particle response. Where feasible, these micron-scale robots based on external control systems enable useful capabilities such as drug delivery, 33 colloidal assembly, 11 , 77 cargo capture, 78 and multimodal locomotion.…”

Section: Inverse Problem: Designing Self-guided Mi...mentioning

confidence: 99%

“…Their ability to conduct the appropriate action given relevant sensory information is determined by their control systemthat is, by the “brains” of the robot. For magnetic microrobots, these key elementssensor, actuator, controllerare often external to the particle itself (Figure a,b) . Using microscopy information (sensor), a computer algorithm (controller) alters the driving magnetic field (actuator) to achieve the desired particle response.…”

Section: Inverse Problem: Designing Self-guided Microrobotsmentioning

confidence: 99%

Accelerating the Design of Self-Guided Microrobots in Time-Varying Magnetic Fields

Dhatt-Gauthier

Livitz

et al. 2023

JACS Au

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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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“…However, micromotors typically move in random trajectories because of thermal fluctuations, while applications in complex environments require them to move along predefined and precise paths. The development of steering strategies is thus a key element in micromotor research. − …”

Section: Introductionmentioning

confidence: 99%

Steering Micromotors via Reprogrammable Optoelectronic Paths

Chen

El‐Sayed

et al. 2023

ACS Nano

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Steering micromotors is important for using them in practical applications and as model systems for active matter. This functionality often requires magnetic materials in the micromotor, taxis behavior of the micromotor, or the use of specifically designed physical boundaries. Here, we develop an optoelectronic strategy that steers micromotors with programmable light patterns. In this strategy, light illumination turns hydrogenated amorphous silicon conductive, generating local electric field maxima at the edge of the light pattern that attracts micromotors via positive dielectrophoresis. As an example, metallo-dielectric Janus microspheres that self-propelled under alternating current electric fields were steered by static light patterns along customized paths and through complex microstructures. Their long-term directionality was also rectified by ratchet-shaped light patterns. Furthermore, dynamic light patterns that varied in space and time enabled more advanced motion controls such as multiple motion modes, parallel control of multiple micromotors, and the collection and transport of motor swarms. This optoelectronic steering strategy is highly versatile and compatible with a variety of micromotors, and thus it possesses the potential for their programmable control in complex environments.

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Control and Autonomy of Microrobots: Recent Progress and Perspective

Cited by 76 publications

References 260 publications

Ultrasound Microrobots with Reinforcement Learning

Ultrasound Microrobots with Reinforcement Learning

Accelerating the Design of Self-Guided Microrobots in Time-Varying Magnetic Fields

Steering Micromotors via Reprogrammable Optoelectronic Paths

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