Collective Decision Making in Communication-Constrained Environments

TL;DR

This paper addresses collective decision-making in robot swarms operating in environments with limited communication, proposing a decentralized algorithm that enhances decision speed without losing accuracy.

Contribution

It introduces a novel decentralized algorithm that maps environmental features to improve collective decisions under communication constraints without prior environmental knowledge.

Findings

Decision speed increased at least 3 times in communication-limited environments.

The algorithm maintains decision accuracy despite communication constraints.

Environmental awareness improves convergence in swarm decision-making.

Abstract

One of the main tasks for autonomous robot swarms is to collectively decide on the best available option. Achieving that requires a high quality communication between the agents that may not be always available in a real world environment. In this paper we introduce the communication-constrained collective decision-making problem where some areas of the environment limit the agents' ability to communicate, either by reducing success rate or blocking the communication channels. We propose a decentralised algorithm for mapping environmental features for robot swarms as well as improving collective decision making in communication-limited environments without prior knowledge of the communication landscape. Our results show that making a collective aware of the communication environment can improve the speed of convergence in the presence of communication limitations, at least 3 times faster, without sacrificing accuracy.