Leader Election and Shape Formation with Self-Organizing Programmable Matter

TL;DR

This paper explores algorithms for leader election and shape formation in programmable matter systems, demonstrating feasible solutions with minimal memory and analyzing their limitations within a geometric amoebot model.

Contribution

It introduces efficient local-control algorithms for leader election and line formation in self-organizing programmable matter using a generalized amoebot model.

Findings

Algorithms require only constant memory per particle.

Leader election and line formation are feasible within the model.

Limitations of the amoebot model are discussed.

Abstract

We consider programmable matter consisting of simple computational elements, called particles, that can establish and release bonds and can actively move in a self-organized way, and we investigate the feasibility of solving fundamental problems relevant for programmable matter. As a suitable model for such self-organizing particle systems, we will use a generalization of the geometric amoebot model first proposed in SPAA 2014. Based on the geometric model, we present efficient local-control algorithms for leader election and line formation requiring only particles with constant size memory, and we also discuss the limitations of solving these problems within the general amoebot model.