Distributed Transformations of Hamiltonian Shapes based on Line Moves

TL;DR

This paper presents a distributed algorithm for transforming connected shapes of agents on a grid into a target shape using line moves, achieving asymptotically optimal move complexity similar to centralized solutions.

Contribution

It introduces the first distributed, connectivity-preserving shape transformation algorithm utilizing line moves with optimal asymptotic move complexity.

Findings

Achieves $O(n \, \log n)$ move complexity, matching centralized algorithms.

Enables shape transformations while maintaining connectivity.

Solves the line formation problem for shapes with Hamiltonian paths.

Abstract

We consider a discrete system of $n$ simple indistinguishable devices, called \emph{agents}, forming a \emph{connected} shape $S_I$ on a two-dimensional square grid. Agents are equipped with a linear-strength mechanism, called a \emph{line move}, by which an agent can push a whole line of consecutive agents in one of the four directions in a single time-step. We study the problem of transforming an initial shape $S_I$ into a given target shape $S_F$ via a finite sequence of line moves in a distributed model, where each agent can observe the states of nearby agents in a Moore neighbourhood. Our main contribution is the first distributed connectivity-preserving transformation that exploits line moves within a total of $O(n \log_2 n)$ moves, which is asymptotically equivalent to that of the best-known centralised transformations. The algorithm solves the \emph{line formation problem} that allows agents to form a final straight line $S_L$, starting from any shape $ S_I $, whose \emph{associated graph} contains a Hamiltonian path.