Local Stochastic Algorithms for Alignment in Self-Organizing Particle Systems
Hridesh Kedia, Shunhao Oh, Dana Randall

TL;DR
This paper introduces local stochastic algorithms for alignment in self-organizing particle systems on 2D lattices, enabling collective orientation control inspired by statistical physics models, with applications in programmable matter.
Contribution
It provides the first rigorous analysis of alignment behavior in particle systems using local stochastic rules inspired by Potts and clock models.
Findings
Particles can be made to align along a single dominant direction.
The system can maintain non-alignment with nearly equal distribution of orientations.
Parameters can be tuned to control particle clustering and orientation states.
Abstract
We present local distributed, stochastic algorithms for \emph{alignment} in self-organizing particle systems (SOPS) on two-dimensional lattices, where particles occupy unique sites on the lattice, and particles can make spatial moves to neighboring sites if they are unoccupied. Such models are abstractions of programmable matter, composed of individual computational particles with limited memory, strictly local communication abilities, and modest computational capabilities. We consider oriented particle systems, where particles are assigned a vector pointing in one of directions, and each particle can compute the angle between its direction and the direction of any neighboring particle, although without knowledge of global orientation with respect to a fixed underlying coordinate system. Particles move stochastically, with each particle able to either modify its direction or make a…
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