Hierarchies define the scalability of robot swarms

TL;DR

This paper investigates how hierarchical structures in robot swarms enhance scalability and effectiveness in complex, resource-limited environments, supported by simulations and physical experiments.

Contribution

It provides empirical evidence that hierarchical organization improves swarm performance in large and unstructured environments, challenging the traditional egalitarian paradigm.

Findings

Hierarchical swarms outperform egalitarian ones in complex environments.

Egalitarian swarms excel in environments matching their sensing capabilities.

Hierarchies enable better scalability and resource efficiency.

Abstract

The emerging behaviors of swarms have fascinated scientists and gathered significant interest in the field of robotics. Traditionally, swarms are viewed as egalitarian, with robots sharing identical roles and capabilities. However, recent findings highlight the importance of hierarchy for deploying robot swarms more effectively in diverse scenarios. Despite nature's preference for hierarchies, the robotics field has clung to the egalitarian model, partly due to a lack of empirical evidence for the conditions favoring hierarchies. Our research demonstrates that while egalitarian swarms excel in environments proportionate to their collective sensing abilities, they struggle in larger or more complex settings. Hierarchical swarms, conversely, extend their sensing reach efficiently, proving successful in larger, more unstructured environments with fewer resources. We validated these concepts through simulations and physical robot experiments, using a complex radiation cleanup task. This study paves the way for developing adaptable, hierarchical swarm systems applicable in areas like planetary exploration and autonomous vehicles. Moreover, these insights could deepen our understanding of hierarchical structures in biological organisms.