article

Division of labor in a group of robots inspired by ants' foraging behavior

Authors:

Thomas H. Labella,

Jean-Louis DeneubourgAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 1, Issue 1

Pages 4 - 25

https://doi.org/10.1145/1152934.1152936

Published: 01 September 2006 Publication History

Abstract

In this article, we analyze the behavior of a group of robots involved in an object retrieval task. The robots' control system is inspired by a model of ants' foraging. This model emphasizes the role of learning in the individual. Individuals adapt to the environment using only locally available information. We show that a simple parameter adaptation is an effective way to improve the efficiency of the group and that it brings forth division of labor between the members of the group. Moreover, robots that are best at retrieving have a higher probability of becoming active retrievers. This selection of the best members does not use any explicit representation of individual capabilities. We analyze this system and point out its strengths and its weaknesses.

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https://doi.org/10.1177/10597123241309184
Li QZhang YHuang JYu LLuan X(2024)Group consensus of nonlinear semi-Markov multi-agent systems by a quantity limited controllerJournal of the Franklin Institute10.1016/j.jfranklin.2024.106917361:10(106917)Online publication date: Jul-2024
https://doi.org/10.1016/j.jfranklin.2024.106917
Pang BZhang ZSong YYuan XXu Q(2023)Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging RobotsApplied Sciences10.3390/app1316910713:16(9107)Online publication date: 10-Aug-2023
https://doi.org/10.3390/app13169107
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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 1, Issue 1

September 2006

114 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/1152934

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Copyright © 2006 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 September 2006

Published in TAAS Volume 1, Issue 1

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Cited By

Hayakawa TYasuda TMatsuno F(2025)Investigating dominant situation of resting behavior as a potential labor pool in robotic swarm for group foragingAdaptive Behavior10.1177/10597123241309184Online publication date: 4-Mar-2025
https://doi.org/10.1177/10597123241309184
Li QZhang YHuang JYu LLuan X(2024)Group consensus of nonlinear semi-Markov multi-agent systems by a quantity limited controllerJournal of the Franklin Institute10.1016/j.jfranklin.2024.106917361:10(106917)Online publication date: Jul-2024
https://doi.org/10.1016/j.jfranklin.2024.106917
Pang BZhang ZSong YYuan XXu Q(2023)Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging RobotsApplied Sciences10.3390/app1316910713:16(9107)Online publication date: 10-Aug-2023
https://doi.org/10.3390/app13169107
Debie EKasmarik KGarratt M(2023)Swarm Robotics: A Survey from a Multi-Tasking PerspectiveACM Computing Surveys10.1145/361165256:2(1-38)Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1145/3611652
Franci AGolubitsky MStewart IBizyaeva ALeonard N(2023)Breaking Indecision in Multiagent, Multioption DynamicsSIAM Journal on Applied Dynamical Systems10.1137/22M150782622:3(1780-1817)Online publication date: 26-Jul-2023
https://doi.org/10.1137/22M1507826
Harwell JSylvester AGini M(2023)An empirical characterization of ODE models of swarm behaviors in common foraging scenariosAutonomous Robots10.1007/s10514-023-10121-947:7(963-977)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10514-023-10121-9
Amaral CHübner JCranefield S(2023)Generating and choosing organisations for multi-agent systemsAutonomous Agents and Multi-Agent Systems10.1007/s10458-023-09623-837:2Online publication date: 24-Sep-2023
https://dl.acm.org/doi/10.1007/s10458-023-09623-8
Valentini GHamann HDorigo M(2022)Global-to-Local Design for Self-Organized Task Allocation in SwarmsIntelligent Computing10.34133/2022/97616942022Online publication date: Jan-2022
https://doi.org/10.34133/2022/9761694
Katada YHasegawa SYamashita KOkazaki NOhkura K(2022)Swarm Crawler Robots Using Lévy Flight for Targets Exploration in Large EnvironmentsRobotics10.3390/robotics1104007611:4(76)Online publication date: 22-Jul-2022
https://doi.org/10.3390/robotics11040076
Aina KAvinery RKuan HBetterton MGoodisman MGoldman D(2022)Toward Task Capable Active Matter: Learning to Avoid Clogging in Confined Collectives via CollisionsFrontiers in Physics10.3389/fphy.2022.73566710Online publication date: 9-Jun-2022
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