Programming Active Cohesive Granular Matter with Mechanically Induced Phase Changes

TL;DR

This paper demonstrates how simple, minimal-control robotic particles can self-organize into cohesive groups through phase transitions driven by physical interactions, enabling collective tasks without complex algorithms.

Contribution

It introduces a theoretical framework and an experimental robotic system that leverage physics-based phase changes for controlling active granular matter at the microscale.

Findings

Robots transition from dispersed to aggregated phases with increased attraction.

Aggregated groups can transport impurities in the environment.

Minimal sensing and control suffice for emergent collective behaviors.

Abstract

Active matter physics and swarm robotics have provided powerful tools for the study and control of ensembles driven by internal sources. At the macroscale, controlling swarms typically utilizes significant 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 that can leverage physics of interactions and thus can be utilized 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, to test theoretical predictions. We consider the problems of aggregation, dispersion, and collective transport. As predicted by the theory, as a parameter representing interparticle attraction increases, the robots transition from a dispersed phase to an aggregated one, forming a dense, compact collective. When aggregated, the collective can transport non-robot "impurities" in their environment, thus performing an emergent task driven by the physics underlying the transition. These results point to a fruitful interplay between algorithm design and active matter robophysics that can result in new nonequilibrium physics and principles for programming collectives without the need for complex algorithms or capabilities.