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

arXiv: https://arxiv.org/abs/2302.13629 · HTML: https://mlsanity.com/paper/2302.13629/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [Optimal Network Topology for Effective Collective Response](https://mlsanity.com/paper/1807.04631/) (2018)
- [Cooperative Pollution Source Localization and Cleanup with a Bio-inspired Swarm Robot Aggregation](https://mlsanity.com/paper/1907.09585/) (2019)
- [Speed-vs-Accuracy Tradeoff in Collective Estimation: An Adaptive Exploration-Exploitation Case](https://mlsanity.com/paper/2201.07123/) (2022)
- [Collective Decision-Making in Ideal Networks: The Speed-Accuracy Tradeoff](https://mlsanity.com/paper/1402.3634/) (2014)
- [CIMAX: Collective Information Maximization in Robotic Swarms Using Local Communication](https://mlsanity.com/paper/1903.05444/) (2019)
- [Self-organized Collective Motion with a Simulated Real Robot Swarm](https://mlsanity.com/paper/1904.03230/) (2019)
- [Effect of Swarm Density on Collective Tracking Performance](https://mlsanity.com/paper/2301.10692/) (2023)

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

- [Hearing the shape of an arena with spectral swarm robotics](https://mlsanity.com/paper/2403.17147/) (2024)

## Abstract (arXiv)

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.