Collaborative Computation in Self-Organizing Particle Systems

TL;DR

This paper explores the computational capabilities of self-organizing particle systems, extending their use beyond shape formation to perform complex tasks like counting, matrix operations, and image processing, demonstrating their versatility and potential applications.

Contribution

It introduces methods for implementing advanced computational tasks in self-organizing particle systems, surpassing previous shape-focused research and enabling broader practical applications.

Findings

Implemented counters and matrix-vector multiplication in particle systems

Demonstrated image processing algorithms like color transformation and edge detection

Showed systems can be more efficient and adaptable than traditional hardware

Abstract

Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including smart paint and coating materials for engineering or programmable cells for medical uses. Previous research using this model has focused on shape formation and other spatial configuration problems (e.g., coating and compression). In this work we study foundational computational tasks that exceed the capabilities of the individual constant size memory of a particle, such as implementing a counter and matrix-vector multiplication. These tasks represent new ways to use these self-organizing systems, which, in conjunction with previous shape and configuration work, make the systems useful for a wider variety of tasks. They can also leverage the distributed and dynamic nature of the self-organizing system to be more efficient and adaptable than on traditional linear computing hardware. Finally, we demonstrate applications of similar types of computations with self-organizing systems to image processing, with implementations of image color transformation and edge detection algorithms.