The University of Southampton
University of Southampton Institutional Repository

SubCDM: collective decision-making with a swarm subset

SubCDM: collective decision-making with a swarm subset
SubCDM: collective decision-making with a swarm subset
Collective decision-making is a key function of autonomous robot swarms, enabling them to reach a consensus on actions based on environmental features. Existing strategies require the participation of all robots in the decision-making process, which is resource-intensive and prevents the swarm from allocating the robots to any other tasks. We propose Subset-Based Collective Decision-Making (SubCDM), which enables decisions using only a swarm subset. The construction of the subset is dynamic and decentralized, relying solely on local information. Our method allows the swarm to adaptively determine the size of the subset for accurate decision-making, depending on the difficulty of reaching a consensus. Simulation results using one hundred robots show that our approach achieves accuracy comparable to using the entire swarm while reducing the number of robots required to perform collective decision-making, making it a resource-efficient solution for collective decision-making in swarm robotics.
Swarm robotics, Collective decision making
Fuady, Samratul
d9aebeac-289e-4583-a6ac-aa11a82ff45f
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Soorati, Mohammad
35fe6bbb-ce52-4c21-a46e-9bb0e31d246c
Fuady, Samratul
d9aebeac-289e-4583-a6ac-aa11a82ff45f
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Soorati, Mohammad
35fe6bbb-ce52-4c21-a46e-9bb0e31d246c

Fuady, Samratul, Tarapore, Danesh and Soorati, Mohammad (2025) SubCDM: collective decision-making with a swarm subset. 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, , Hangzhou, China. 19 - 25 Oct 2025. 6 pp . (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

Collective decision-making is a key function of autonomous robot swarms, enabling them to reach a consensus on actions based on environmental features. Existing strategies require the participation of all robots in the decision-making process, which is resource-intensive and prevents the swarm from allocating the robots to any other tasks. We propose Subset-Based Collective Decision-Making (SubCDM), which enables decisions using only a swarm subset. The construction of the subset is dynamic and decentralized, relying solely on local information. Our method allows the swarm to adaptively determine the size of the subset for accurate decision-making, depending on the difficulty of reaching a consensus. Simulation results using one hundred robots show that our approach achieves accuracy comparable to using the entire swarm while reducing the number of robots required to perform collective decision-making, making it a resource-efficient solution for collective decision-making in swarm robotics.

Text
IROS_2025_SubCDM - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (2MB)

More information

Accepted/In Press date: 16 June 2025
Venue - Dates: 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, , Hangzhou, China, 2025-10-19 - 2025-10-25
Keywords: Swarm robotics, Collective decision making

Identifiers

Local EPrints ID: 504977
URI: http://eprints.soton.ac.uk/id/eprint/504977
PURE UUID: cc4f095b-5cd8-4ca5-b3c2-349804a5e6bd
ORCID for Danesh Tarapore: ORCID iD orcid.org/0000-0002-3226-6861
ORCID for Mohammad Soorati: ORCID iD orcid.org/0000-0001-6954-1284

Catalogue record

Date deposited: 23 Sep 2025 16:56
Last modified: 24 Sep 2025 02:01

Export record

Contributors

Author: Samratul Fuady
Author: Danesh Tarapore ORCID iD
Author: Mohammad Soorati ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×