A study of error diversity in robotic swarms for task partitioning in foraging tasks

Edgar Buchanan*, Kieran Alden, Andrew Pomfret, Jon Timmis, Andy M. Tyrrell*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Often in swarm robotics, an assumption is made that all robots in the swarm behave the same and will have a similar (if not the same) error model. However, in reality, this is not the case, and this lack of uniformity in the error model, and other operations, can lead to various emergent behaviors. This paper considers the impact of the error model and compares robots in a swarm that operate using the same error model (uniform error) against each robot in the swarm having a different error model (thus introducing error diversity). Experiments are presented in the context of a foraging task. Simulation and physical experimental results show the importance of the error model and diversity in achieving the expected swarm behavior.

Original languageEnglish
Article number904341
JournalFrontiers in Robotics and AI
Volume9
DOIs
Publication statusPublished - 04 Jan 2023
Externally publishedYes

Keywords

  • error diversity
  • fault tolerance
  • foraging
  • swarm robotics
  • task partitioning

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