Programming Active Cohesive Granular Matter with Mechanically Induced Phase Changes

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.

References in corpus (7)

• Wet Granular Materials
• The Jamming Transition in Granular Systems
• Active matter
• Phase Diagram of Vertically Shaken Granular Matter
• Emergent oscillations assist obstacle negotiation during ant cooperative transport
• A cohesive granular material with tunable elasticity
• Brownian motion of a circle swimmer in a harmonic trap

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• Deadlock and Noise in Self-Organized Aggregation Without Computation
• Micellization in active matter of asymmetric self-propelled particles: Experiments
• Inertial Spinner Swarm Experiments: Spin Pumping, Entropy Oscillations and Spin Frustration
• Kinetic theory of decentralized learning for smart active matter
• Topological signatures of collective dynamics and turbulent-like energy cascades in apolar active granular
• Informational Memory Shapes Collective Behavior in Intelligent Swarms