Untethered Small-Scale Machines for Microrobotic Manipulation : From Individual and Multiple to Collective Machines

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

27 Scopus Citations
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Author(s)

  • Qianqian Wang
  • Jiangfan Yu
  • Ji Lang
  • Zhiyang Lyu
  • Yunfei Chen
  • Li Zhang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)13081–13109
Journal / PublicationACS Nano
Volume17
Issue number14
Online published13 Jul 2023
Publication statusPublished - 25 Jul 2023

Abstract

Untethered small-scale machines (USSMs) that can actively adjust their motion, deformation, and collective states in response to external stimuli have gained enormous interest in various manipulation, sensing, and biomedical applications. Because they can be efficiently operated in confined and tortuous environments, USSMs are capable of conducting wireless microrobotic manipulation tasks that tethered machines find hard to achieve. Over the past decade of development, significant research progress has been achieved in designing USSM-based manipulation strategies, which are enabled by investigating machine-object, machine-environment, and machine-machine interactions. This review summarizes the latest developments in USSMs for microrobotic manipulation by utilizing individual machines, coordinating multiple machines, and inducing collective behaviors. Providing recent studies and relevant applications in microrobotic and biomedical areas, we also discuss the challenges and future perspectives facing USSMs-based intelligent manipulation systems to achieve manipulation in complex environments with imaging-guided processes and increasing autonomy levels. © 2023 American Chemical Society

Research Area(s)

  • adaptive control, automation at small scales, autonomous system, environment adaptability, micro/nanorobot, microrobotic manipulation, motion control, small-scale machine, wireless actuation

Citation Format(s)

Untethered Small-Scale Machines for Microrobotic Manipulation: From Individual and Multiple to Collective Machines. / Wang, Qianqian; Zhang, Jiachen; Yu, Jiangfan et al.
In: ACS Nano, Vol. 17, No. 14, 25.07.2023, p. 13081–13109.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review