Seedling: Smarticle ensembles, a testbed for the least rattling framework
Abstract
This project seeks to test in systematic laboratory experiments the recent Òleast rattlingÓ theoretical framework from Prof. Jeremy EnglandÕs group at MIT for predicting asymptotic behavior of nonequilibrium (damped/driven) active matter systems. Prof. Daniel GoldmanÕs group at Georgia Tech has developed an active matter system composed of ÒsmarticlesÓ (smart, active particles), simple, low cost, programmable three-link, two motor robots. These robots can perform self deformations enabling individuals to locomote and to repel/attract each other via (dis)entanglement of arms, thereby allowing on-demand formation of gas, fluid, and solid-like states. However, achieving a given ÒmacroscopicÓ state from a set of changes to the individuals Òmicroscopic statesÓ remains a challenge. The least rattling framework is based on the assumption of strong time-scale separation between different classes of degrees of freedom in the motion of active systems. We posit that such separation exists in certain smarticle ensembles, namely confined collectives which can locomote (ÒsupersmaticlesÓ). We will test the least rattling framework via studies of supersmarticle mobility in the presence of gradients in smarticle activity, with the hope that the framework can more broadly provide insight into supersmarticle directional transport for parameters like smarticle and confining ring masses. If successful, our efforts will provide a proof of principle for the theoretical proposal that certain many-body driven systems settle into absorbing dynamical attractors which contain correlated, regular internal motions which reduce force fluctuations and effectively ÒfreezeÓ slow degrees of freedom in place. In robot collectives where the relationship between structure and external driving can be defined arbitrarily, such a demonstration would open the door to the design and control of adaptive properties the active matter many-particle ensembles.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 08, 2019
- Source ID
- W911NF1810101
Entities
People
- Daniel Goldman
Organizations
- Army Contracting Command
- Georgia Tech Research Corporation
- United States Army