Adaptive micro-locomotion in a dynamically changing environment via context detection

Zou, Zonghao; Liu, Yuexin; Tsang, Alan C.H.; Young, Y.-N.; Pak, On Shun

doi:10.1016/j.cnsns.2023.107666

Cited by 2 publications

(1 citation statement)

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“…It gives microrobots greater decision-making power and solves many challenges. Through deep learning models, microrobots can automatically identify and track targets, enhancing collaboration and monitoring capabilities [24]. The use of machine learning algorithms to achieve the autonomous navigation of microrobots, including obstacle avoidance, path planning, and other functions, improves their movement ability and the ability to adapt to complex environments [25,26].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Microrobots Powered by Multi-Physics Field for Biomedical and Environmental Applications

Teng,

Qiao,

et al. 2024

Micromachines

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Microrobots powered by multi-physics fields are becoming a hotspot for micro–nano manufacturing. Due to the small size of microrobots, they can easily enter small spaces that are difficult for ordinary robots to reach and perform a variety of special tasks. This gives microrobots a broad application prospect in many fields. This paper describes the materials, structures, and driving principles of microrobots in detail and analyzes the advantages and limitations of their driving methods in depth. In addition, the paper discusses the detailed categorization of the action forms of microrobots and explores their diversified motion modes and their applicable scenarios. Finally, the article highlights the wide range of applications of microrobots in the fields of biomedicine and environmental protection, emphasizing their great potential for solving real-world problems and advancing scientific progress.

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Section: Introductionmentioning

confidence: 99%

Recent Advances in Microrobots Powered by Multi-Physics Field for Biomedical and Environmental Applications

Teng,

Qiao,

et al. 2024

Micromachines

View full text Add to dashboard Cite

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Automated Magnetic Microrobot Control: From Mathematical Modeling to Machine Learning

Li,

Huo,

Chu

et al. 2024

Mathematics

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Microscale robotics has emerged as a transformative field, offering unparalleled opportunities for innovation and advancement in various fields. Owing to the distinctive benefits of wireless operation and a heightened level of safety, magnetic actuation has emerged as a widely adopted technique in the field of microrobotics. However, factors such as Brownian motion, fluid dynamic flows, and various nonlinear forces introduce uncertainties in the motion of micro/nanoscale robots, making it challenging to achieve precise navigational control in complex environments. This paper presents an extensive review encompassing the trajectory from theoretical foundations of the generation and modeling of magnetic fields as well as magnetic field-actuation modeling to motion control methods of magnetic microrobots. We introduce traditional control methods and the learning-based control approaches for robotic systems at the micro/nanoscale, and then these methods are compared. Unlike the conventional navigation methods based on precise mathematical models, the learning-based control and navigation approaches can directly learn control signals for the actuation systems from data and without relying on precise models. This endows the micro/nanorobots with high adaptability to dynamic and complex environments whose models are difficult/impossible to obtain. We hope that this review can provide insights and guidance for researchers interested in automated magnetic microrobot control.

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Adaptive micro-locomotion in a dynamically changing environment via context detection

Cited by 2 publications

References 45 publications

Recent Advances in Microrobots Powered by Multi-Physics Field for Biomedical and Environmental Applications

Recent Advances in Microrobots Powered by Multi-Physics Field for Biomedical and Environmental Applications

Automated Magnetic Microrobot Control: From Mathematical Modeling to Machine Learning

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