We investigate the capabilities and properties of distributed systems that consist of myriads of simple computational particles. These particle systems are able to self-organize in order to solve their designated tasks without any central control. Self-organizing particle systems have many interesting applications like coating objects for monitoring and repair purposes and forming nano-scale devices for surgery and molecular-scale electronic structures. The notion of programmable matter is tightly interwoven with the term self-organizing particle systems, which has the ability to change its physical properties (shape, density, moduli, conductivity, optical properties, etc.) in a programmable fashion.
The Amoebot Model. Our work aims to build a theoretical foundation for self-organizing particle systems that allows rigorous algorithmic research. To this end, we proposed the amoebot model for self-organizing particle systems in 2014 as a framework that captures many underlying assumptions and physical properties of particle systems in general. We’ve since used the amoebot model to develop provably efficient algorithms for behaviors such as shape formation, coating, and leader election. A detailed description of the model can be found here.
