A Micro-Macro Model of Encounter-Driven Information Diffusion in Robot Swarms

TL;DR

This paper introduces a two-level model for understanding how information spreads in robot swarms where robots meet randomly without scheduling meetings, validated through extensive simulations.

Contribution

It presents a novel micro-macro model of encounter-driven information diffusion in robot swarms, based on first principles and validated by simulations.

Findings

Model accurately predicts information diffusion dynamics.

Swarm size and communication range significantly affect diffusion.

Environment and motion regimes influence diffusion efficiency.

Abstract

In this paper, we propose the problem of Encounter-Driven Information Diffusion (EDID). In EDID, robots are allowed to exchange information only upon meeting. Crucially, EDID assumes that the robots are not allowed to schedule their meetings. As such, the robots have no means to anticipate when, where, and who they will meet. As a step towards the design of storage and routing algorithms for EDID, in this paper we propose a model of information diffusion that captures the essential dynamics of EDID. The model is derived from first principles and is composed of two levels: a micro model, based on a generalization of the concept of `mean free path'; and a macro model, which captures the global dynamics of information diffusion. We validate the model through extensive robot simulations, in which we consider swarm size, communication range, environment size, and different random motion regimes. We conclude the paper with a discussion of the implications of this model on the algorithms that best support information diffusion according to the parameters of interest.