Directing Chemotaxis-Based Spatial Self-Organization via Biased, Random Initial Conditions

TL;DR

This paper presents a method to control the self-organization of chemotaxis-inspired agents by initializing their positions with biased randomness, enabling reliable formation of specific shapes from random starting points.

Contribution

It introduces a novel approach using biased initial conditions based on statistical moments to steer agent-based self-organization toward desired configurations.

Findings

Successfully directed agents to form specific shapes from random initial states.

Controlled statistical moments of initial conditions lead to robust shape formation.

Method demonstrates potential for programmable self-assembly in biological and synthetic systems.

Abstract

Inspired by the chemotaxis interaction of living cells, we have developed an agent-based approach for self-organizing shape formation. Since all our simulations begin with a different uniform random configuration and our agents move stochastically, it has been observed that the self-organization process may form two or more stable final configurations. These differing configurations may be characterized via statistical moments of the agents' locations. In order to direct the agents to robustly form one specific configuration, we generate biased initial conditions whose statistical moments are related to moments of the desired configuration. With this approach, we are able to successfully direct the aggregating swarms to produced a desired macroscopic shape, starting from randomized initial conditions with controlled statistical properties.