Collective Behaviors of Active Matter Learning from Natural Taxes Across Scales

Fengtong Ji, Yilin Wu, Martin Pumera, Li Zhang

Research output: Contribution to journalReview articlepeer-review

1 Citation (Scopus)

Abstract

Taxis orientation is common in microorganisms, and it provides feasible strategies to operate active colloids as small-scale robots. Collective taxes involve numerous units that collectively perform taxis motion, whereby the collective cooperation between individuals enables the group to perform efficiently, adaptively, and robustly. Hence, analyzing and designing collectives is crucial for developing and advancing microswarm toward practical or clinical applications. In this review, natural taxis behaviors are categorized and synthetic microrobotic collectives are discussed as bio-inspired realizations, aiming at closing the gap between taxis strategies of living creatures and those of functional active microswarms. As collective behaviors emerge within a group, the global taxis to external stimuli guides the group to conduct overall tasks, whereas the local taxis between individuals induces synchronization and global patterns. By encoding the local orientations and programming the global stimuli, various paradigms can be introduced for coordinating and controlling such collective microrobots, from the viewpoints of fundamental science and practical applications. Therefore, by discussing the key points and difficulties associated with collective taxes of different paradigms, this review potentially offers insights into mimicking natural collective behaviors and constructing intelligent microrobotic systems for on-demand control and preassigned tasks.

Original languageEnglish
JournalAdvanced Materials
DOIs
Publication statusAccepted/In press - 2022

Bibliographical note

Funding Information:
The research work is financially supported by the Hong Kong Research Grants Council (RGC) with project Nos. GRF14300621, E‐CUHK401/20, RGC14303918 and RFS2021‐4S04 and the Research Fellow Scheme (RFS) with project No. RFS2122‐4S03; the ITF project with Project No. MRP/036/18X funded by the HKSAR Innovation and Technology Commission (ITC); and the Croucher Foundation Grant with Ref. No. CAS20403, and the CUHK internal grants. L.Z. also thanks the support from the SIAT‐CUHK Joint Laboratory of Robotics and Intelligent Systems and the Multi‐scale Medical Robotics Center (MRC), InnoHK, at the Hong Kong Science Park. F.J. would like to thank Zhengxin Yang and Neng Xia for their help in obtaining the copyright of figures. The authors thank the reviewers’ comments on improving this review.

Publisher Copyright:
© 2022 Wiley-VCH GmbH.

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering

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