# Estimation of continuous environments by robot swarms: Correlated networks and decision-making

## TL;DR

This paper presents a decentralized control algorithm enabling robot swarms to explore unbounded environments, reach consensus on environmental features, and adapt their network topology dynamically, demonstrated through real-world experiments.

## Contribution

It introduces a novel approach for continuous environment estimation in robot swarms, considering the causal loop between network topology and decision-making, validated by real-world experiments.

## Key findings

- Higher precision in environmental feature estimation compared to control
- Effective consensus achievement in real-world swarm experiments
- Dynamic network topology influences convergence time

## Abstract

Collective decision-making is an essential capability of large-scale multi-robot systems to establish autonomy on the swarm level. A large portion of literature on collective decision-making in swarm robotics focuses on discrete decisions selecting from a limited number of options. Here we assign a decentralized robot system with the task of exploring an unbounded environment, finding consensus on the mean of a measurable environmental feature, and aggregating at areas where that value is measured (e.g., a contour line). A unique quality of this task is a causal loop between the robots' dynamic network topology and their decision-making. For example, the network's mean node degree influences time to convergence while the currently agreed-on mean value influences the swarm's aggregation location, hence, also the network structure as well as the precision error. We propose a control algorithm and study it in real-world robot swarm experiments in different environments. We show that our approach is effective and achieves higher precision than a control experiment. We anticipate applications, for example, in containing pollution with surface vehicles.