Programming active cohesive granular matter with mechanically induced phase changes

Shengkai Li, Bahnisikha Dutta, Sarah Cannon, Joshua J. Daymude, Ram Avinery, Enes Aydin, Andréa W. Richa, Daniel I. Goldman, Dana Randall

Research output: Contribution to journalArticlepeer-review

Abstract

At the macroscale, controlling robotic swarms typically uses substantial memory, processing power, and coordina tion unavailable at the microscale, e.g., for colloidal robots, which could be useful for fighting disease, fabricating intelligent textiles, and designing nanocomputers. To develop principles that can leverage physical interactions and thus be used across scales, we take a two-pronged approach: A theoretical abstraction of self-organizing parti cle systems and an experimental robot system of active cohesive granular matter that intentionally lacks digital electronic computation and communication, using minimal (or no) sensing and control. As predicted by theory, as interparticle attraction increases, the collective transitions from dispersed to a compact phase. When aggregated, the collective can transport non-robot "impurities,"thus performing an emergent task driven by the physics under lying the transition. These results reveal a fruitful interplay between algorithm design and active matter robophys ics that can result in principles for programming collectives without the need for complex algorithms or capabilities.

Original languageEnglish (US)
Article numbereabe8494
JournalScience Advances
Volume7
Issue number17
DOIs
StatePublished - Apr 21 2021

ASJC Scopus subject areas

  • General

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