Programming active cohesive granular matter with mechanically induced phase changes
Year: 2021
Type: article
Abstract: At the macroscale, controlling robotic swarms typically uses substantial memory, processing power, and coordination 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 particle 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 underlying the transition. These results reveal a fruitful interplay between algorithm design and active matter robophysics that can result in principles for programming collectives without the need for complex algorithms or capabilities.
Source: Science Advances
Authors: Shengkai Li, Bahnisikha Dutta, Sarah Cannon, Joshua J. Daymude, Ram Avinery, Enes Aydin, Andréa W. Richa, Daniel I. Goldman, Dana Randall
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,500
Awards: AiTF: Collaborative Research: Distributed and Stochastic Algorithms for Active Matter: Theory and Practice, AF: Small: Self-Organizing Particle Systems, Student Research Network in the Physics of Living Systems: Georgia Tech Node, G4072640781, AF: Small: Markov Chain Algorithms for Problems from Computer Science and Statistical Physics, PostDoctoral Research Fellowship, AiTF: Collaborative Research: Distributed and Stochastic Algorithms for Active Matter: Theory and Practice, G696514715, Building Your Home One Grain at Time: How Construction, Function and Robustness of Fire Ant Nests Depend on Ground Properties, AitF: Collaborative Research: A Distributed and Stochastic Algorithmic Framework for Active Matter, G8051917394, AitF: Collaborative Research: A Distributed and Stochastic Algorithmic Framework for Active Matter, EAGER: Biophysical Theory of Mitotic Spindle Length Instability and Self Assembly
OpenAlex ID: W3126576885