Massive Self-Assembly in Grid Environments

TL;DR

This paper introduces a novel computational method for large-scale self-assembly of connected shapes in grid environments, inspired by natural phototaxis, utilizing a dynamic artificial light field to coordinate agent movement.

Contribution

It presents a new self-assembly mechanism that combines efficiency, scalability, and stability through a feedback-driven artificial light field in grid environments.

Findings

Demonstrates effective formation of connected shapes at large scales

Shows improved stability and scalability over previous methods

Provides insights into bio-inspired self-assembly processes

Abstract

Self-assembly plays an essential role in many natural processes, involving the formation and evolution of living or non-living structures, and shows potential applications in many emerging domains. In existing research and practice, there still lacks an ideal self-assembly mechanism that manifests efficiency, scalability, and stability at the same time. Inspired by phototaxis observed in nature, we propose a computational approach for massive self-assembly of connected shapes in grid environments. The key component of this approach is an artificial light field superimposed on a grid environment, which is determined by the positions of all agents and at the same time drives all agents to change their positions, forming a dynamic mutual feedback process. This work advances the understanding and potential applications of self-assembly.