Mobile RAM and Shape Formation by Programmable Particles

TL;DR

This paper demonstrates how simple, finite-state particles in a programmable matter model can simulate Turing machines and form complex, diverse shapes including fractals, expanding the possibilities of shape formation in distributed systems.

Contribution

It introduces a method for weak particles to simulate Turing-complete entities and provides a comprehensive, generalized solution for forming complex shapes, surpassing previous limitations.

Findings

Particles can simulate Turing machines.

New algorithms form complex shapes like fractals.

Complete characterization of shape formability based on initial configuration.

Abstract

We investigate computational issues in the distributed model Amoebots of programmable matter. In this model, the computational entities, called particles, are anonymous finite-state machines that operate and move on an hexagonal tasselation of the plane. In this paper we show how a constant number of such weak particles can simulate a powerful Turing-complete entity that is able to move on the plane while computing. We then show an application of our tool to the classical Shape-Formation problem, providing a new and much more general distributed solution protocol. Indeed, the existing algorithms would allow to form only shapes made of arrangements of segments and triangles. Our algorithm allows the particles to form more abstract and general connected shapes, including circles and spirals, as well as fractal objects of non-integer dimension, such as the Sierpinski triangle or the Koch snowflake. In lieu of the existing limitation on the formability of a shape depending on the symmetry of the initial configuration of the particles, our result provides a complete characterization of the connected shapes that can be formed by an initially simply connected set of particles. Furthermore, in the case of non-connected shapes, we give almost-matching necessary and sufficient conditions for their formability.