The Information-Cost-Reward framework for understanding robot swarm foraging

Lenka Pitonakova*, Richard Crowder, Seth Bullock

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

17 Citations (Scopus)
291 Downloads (Pure)


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.

Original languageEnglish
Number of pages26
JournalSwarm Intelligence
Early online date17 Nov 2017
Publication statusE-pub ahead of print - 17 Nov 2017


  • Foraging
  • Information flow
  • Modelling
  • Swarm robotics


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