A Stochastic Approach to Shortcut Bridging in Programmable Matter

TL;DR

This paper introduces a stochastic, distributed algorithm for particles in programmable matter to self-assemble bridges over gaps, balancing length and cost, inspired by army ant behavior, with proven near-optimality and robustness.

Contribution

It presents a novel stochastic algorithm for shortcut bridging in programmable matter, analyzing its optimality and biological plausibility through Markov chain techniques.

Findings

Achieves near-optimal balance between bridge length and cost.

Exhibits behavior similar to army ant bridging in simulations.

Demonstrates robustness and simplicity of the stochastic approach.

Abstract

In a self-organizing particle system, an abstraction of programmable matter, simple computational elements called particles with limited memory and communication self-organize to solve system-wide problems of movement, coordination, and configuration. In this paper, we consider a stochastic, distributed, local, asynchronous algorithm for "shortcut bridging", in which particles self-assemble bridges over gaps that simultaneously balance minimizing the length and cost of the bridge. Army ants of the genus Eciton have been observed exhibiting a similar behavior in their foraging trails, dynamically adjusting their bridges to satisfy an efficiency trade-off using local interactions. Using techniques from Markov chain analysis, we rigorously analyze our algorithm, show it achieves a near-optimal balance between the competing factors of path length and bridge cost, and prove that it exhibits a dependence on the angle of the gap being "shortcut" similar to that of the ant bridges. We also present simulation results that qualitatively compare our algorithm with the army ant bridging behavior. Our work gives a plausible explanation of how convergence to globally optimal configurations can be achieved via local interactions by simple organisms (e.g., ants) with some limited computational power and access to random bits. The proposed algorithm also demonstrates the robustness of the stochastic approach to algorithms for programmable matter, as it is a surprisingly simple extension of our previous stochastic algorithm for compression.