Congestion and Scalability in Robot Swarms: a Study on Collective Decision Making

TL;DR

This study investigates how movement and communication congestion affect the scalability of robot swarms during collective decision-making, highlighting that division of labor with local communication enhances scalability.

Contribution

It provides an empirical analysis of congestion effects on swarm decision-making and demonstrates that division of labor with versioned communication improves scalability.

Findings

Division of Labor reduces congestion effects.

Versioned local communication enhances scalability.

High robot density impacts decision accuracy.

Abstract

One of the most important promises of decentralized systems is scalability, which is often assumed to be present in robot swarm systems without being contested. Simple limitations, such as movement congestion and communication conflicts, can drastically affect scalability. In this work, we study the effects of congestion in a binary collective decision-making task. We evaluate the impact of two types of congestion (communication and movement) when using three different techniques for the task: Honey Bee inspired, Stigmergy based, and Division of Labor. We deploy up to 150 robots in a physics-based simulator performing a sampling mission in an arena with variable levels of robot density, applying the three techniques. Our results suggest that applying Division of Labor coupled with versioned local communication helps to scale the system by minimizing congestion.