The transition from order and disorder has long been a scientific and engineering challenge. Statistical mechanics predict how groups of simple particles transition between order and disorder, for example, a collection of randomly colliding atoms freezing to form a uniform crystal lattice. The collective behavior of particles is more challenging to predict when the particles involved are complex and mobile such as the active matter in bird flocks, bacterial colonies, and robot swarms.
A team of physicists and engineers propose a new principle by which active matter systems can spontaneously order, without higher-level instructions or programmed interaction. The principle is demonstrated in such systems as groups of periodically shape-changing robots called “smarticles” (smart, active particles).
The theory, developed by Dr. Pavel Chvykov at Massachusetts Institute of Technology (MIT) while a student of Prof. Jeremy England, now a researcher in the School of Physics at Georgia Institute of Technology, posits that certain types of active matter with sufficiently messy dynamics will spontaneously find what the researchers refer to as “low rattling” states.
Rattling occurs when matter takes energy flowing into it and turns it into random motion. Rattling can be greater when motion is more violent, or more random. Low rattling is either very slightly or highly organized, or both. Basically, when the matter and energy source allow for the possibility of a low rattling state, the system will randomly rearrange until it finds that state and then gets stuck there. When energy is supplied through forces with a particular pattern, the selected state will discover a way for the matter to move that finely matches that pattern.
Chvykov and England developed numerous mathematical models to demonstrate the low rattling principle, but it wasn’t until they connected with Daniel Goldman, Dunn Family Professor of Physics at the Georgia Institute of Technology, that they could test their predictions.
Working with Chvykov, who visited Goldman’s lab, Ph.D. students William Savoie and Akash Vardhan used three flapping smarticles enclosed in a ring to compare experiments to theory. Instead of displaying complicated dynamics and exploring the container completely, the robots spontaneously self-organized into a few dances. One consisted of three robots slapping each other’s arms in sequence. These dances could persist for hundreds of flaps, but suddenly lose stability and be replaced by a dance of a different pattern.
At this stage, more refined and better-controlled smarticles were developed. By controlling sequences of low rattling states, the researchers were able to make the system reach configurations that perform useful work, which has potential use in microrobotic swarms, active matter, and metamaterials.
With robot swarms, it’s about creating adaptive and smart group behaviors that can be realized in a single swarm. For living cells and novel materials, it may potentially be about understanding what the swarm of atoms or proteins can deliver as far as new material or computational properties.
The research was reported in the January 1, 2021 issue of the journal Science.