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The information-cost-reward framework for understanding robot swarm foraging

The information-cost-reward framework for understanding robot swarm foraging
The information-cost-reward framework for understanding robot swarm foraging
Demand for autonomous swarms, where robots can cooperate with each other without human intervention, is set to grow rapidly in the near future. Currently, one of the main challenges in swarm robotics is understanding how the behaviour of individual robots leads to an observed emergent collective performance. In this paper, a novel approach to understanding robot swarms that perform foraging is proposed in the form of the Information-Cost-Reward (ICR) framework. The framework relates the way in which robots obtain and share information (about where work needs to be done) to the swarm’s ability to exploit that information in order to obtain reward efficiently in the context of a particular task and environment. The ICR framework can be applied to analyse underlying mechanisms that lead to observed swarm performance, as well as to inform hypotheses about the suitability of a particular robot control strategy for new swarm missions. Additionally, the information-centred understanding that the framework offers paves a way towards a new swarm design methodology where general principles of collective robot behaviour guide algorithm design.
Swarm robotics, Foraging, Modelling, Information flow
1935-3812
71–96
Pitonakova, Lenka
ef806152-a9c0-4075-806d-c75f0d3f7bbb
Crowder, Richard
ddeb646d-cc9e-487b-bd84-e1726d3ac023
Bullock, Seth
eeb8c2f8-dd55-4ddf-aa8d-24d77b6fe1b3
Pitonakova, Lenka
ef806152-a9c0-4075-806d-c75f0d3f7bbb
Crowder, Richard
ddeb646d-cc9e-487b-bd84-e1726d3ac023
Bullock, Seth
eeb8c2f8-dd55-4ddf-aa8d-24d77b6fe1b3

Pitonakova, Lenka, Crowder, Richard and Bullock, Seth (2018) The information-cost-reward framework for understanding robot swarm foraging Swarm Intelligence, 12, (1), 71–96. (doi:10.1007/s11721-017-0148-3).

Record type: Article

Abstract

Demand for autonomous swarms, where robots can cooperate with each other without human intervention, is set to grow rapidly in the near future. Currently, one of the main challenges in swarm robotics is understanding how the behaviour of individual robots leads to an observed emergent collective performance. In this paper, a novel approach to understanding robot swarms that perform foraging is proposed in the form of the Information-Cost-Reward (ICR) framework. The framework relates the way in which robots obtain and share information (about where work needs to be done) to the swarm’s ability to exploit that information in order to obtain reward efficiently in the context of a particular task and environment. The ICR framework can be applied to analyse underlying mechanisms that lead to observed swarm performance, as well as to inform hypotheses about the suitability of a particular robot control strategy for new swarm missions. Additionally, the information-centred understanding that the framework offers paves a way towards a new swarm design methodology where general principles of collective robot behaviour guide algorithm design.

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Accepted/In Press date: 9 November 2017
e-pub ahead of print date: 17 November 2017
Published date: March 2018
Related URLs:
Keywords: Swarm robotics, Foraging, Modelling, Information flow

Identifiers

Local EPrints ID: 415979
URI: https://eprints.soton.ac.uk/id/eprint/415979
ISSN: 1935-3812
PURE UUID: b46f394c-216b-447a-9a3c-7ec4469d35fd
ORCID for Lenka Pitonakova: ORCID iD orcid.org/0000-0003-3633-7302

Catalogue record

Date deposited: 29 Nov 2017 17:30
Last modified: 23 Feb 2018 17:31

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Contributors

Author: Lenka Pitonakova ORCID iD
Author: Richard Crowder
Author: Seth Bullock

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