bio-inspired-algorithms

In this dissertation defense, I treat three questions on emergent behavior in programmable matter: What are the minimum individual capabilities necessary to achieve a collective behavior? How can existing algorithms be enhanced to capture realistic assumptions? How can digital algorithms be translated for simple, analog systems?

The world is filled with systems of entities that collaborate in motion, both natural and engineered, that are capable of sophisticated emergent behavior arising from simple interactions of their members. This PhD dissertation collects my work on the amoebot model, stateful and stochastic distributed algorithms for programmable matter, and translations to active granular matter.

In this talk, we present our algorithm for energy distribution in programmable matter. We also detail our self-stabilizing extension of the amoebot model’s well-established spanning forest primitive. We conclude by showing how these algorithms generalize previous work to also consider energy constraints.

We extend the amoebot model to consider energy constraints and introduce an algorithm that distributes energy to all particles that need it to participate in the system’s collective behavior. This generalizes existing algorithms in the amoebot catalogue to also respect energy constraints.