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Collective behaviours of complicated ‘particles’ – such as flocks of birds, colonies of bacteria or robotic swarms – are difficult to predict, but a team of physicists and engineers from the Massachusetts and Georgia Institutes of Technology have proposed a new principle by which these ‘active matter’ systems can spontaneously order.
The systems do so without higher level instructions or even programmed interaction. The researchers demonstrated the principle in a variety of systems, including groups of periodically shape-changing robots called ‘smarticles’ – smart, active particles.
The theory, developed by Dr Pavel Chvykov at MIT while a student of Professor Jeremy England, now at GIT, posits that certain types of active matter with sufficiently ‘messy’ dynamics will spontaneously find what the researchers call ‘low rattling’ states.
“Rattling is when matter takes energy flowing into it and turns it into random motion,” said Prof. England. “Rattling can be greater either when the motion is more violent, or more random. Conversely, low rattling is either very slight or highly organised – or both.
“So, the idea is that if your 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. If you supply energy through forces with a particular pattern, this means the selected state will discover a way for the matter to move that finely matches that pattern.”
Smarticle robots helped the researchers test the theory. Working with Dr Pavel Chvykov from MIT, PhD students William Savoie and Akash Vardhan used three flapping smarticles enclosed in a ring to compare experiments to theory. The students observed that instead of displaying complicated dynamics and exploring the container completely, the robots would spontaneously self-organise into a few ‘dances’.
One dance consisted of three robots slapping each other's arms in sequence, for example. The dance could last for hundreds of flaps, but suddenly lose stability and be replaced by a dance with a different pattern.
Dr Chvykov then worked with engineers at Northwestern University, Professor Todd Murphey and PhD student Thomas Berrueta, who developed more refined smarticles. The improved smarticles allowed the researchers to test the limits of the theory, including how the types and number of dances varied for different arm flapping patterns, as well as how the dances could be controlled.
“By controlling sequences of low rattling states, we were able to make the system reach configurations that do useful work,” said Berrueta. The Northwestern University researchers said the findings could have broad practical implications for microrobotic swarms, active matter and metamaterials.
Prof. England said: “For robot swarms, it's about getting many adaptive and smart group behaviours that you can design to be realised in a single swarm, even though the individual robots are relatively cheap and computationally simple. For living cells and novel materials, it might be about understanding what the 'swarm' of atoms or proteins can get you, as far as new material or computational properties.”
The research was reported in the January 1, 2021 issue of Science.
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