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

arXiv: https://arxiv.org/abs/2307.08568 · HTML: https://mlsanity.com/paper/2307.08568/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2023.

## Built on by (most cited first, 1 of 1)

- [The Hive Mind is a Single Reinforcement Learning Agent](https://mlsanity.com/paper/2410.17517/) (2024)

## Abstract (arXiv)

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.