Collective self-detection scheme for adaptive error detection in a foraging swarm of robots

Hui Keng Lau*, Jon Timmis, Iain Bate

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

5 Citations (SciVal)


In this paper we present a collective detection scheme using receptor density algorithm to self-detect certain types of failure in swarm robotic systems. Key to any fault-tolerant system, is its ability to be robust to failure and have appropriate mechanisms to cope with a variety of such failures. In this work we present an error detection scheme based on T-cell signalling in which robots in a swarm collaborate by exchanging information with respect to performance on a given task, and self-detect errors within an individual. While this study is focused on deployment in a swarm robotic context, it is possible that our approach could possibly be generalized to a wider variety of multi-agent systems.

Original languageEnglish
Title of host publicationArtificial Immune Systems - 10th International Conference, ICARIS 2011, Proceedings
PublisherSpringer Nature
Number of pages14
ISBN (Print)9783642223709
Publication statusPublished - 2011
Event10th International Conference on Artificial Immune Systems, ICARIS 2011 - Cambridge, United Kingdom of Great Britain and Northern Ireland
Duration: 18 Jul 201121 Jul 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6825 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Artificial Immune Systems, ICARIS 2011
Country/TerritoryUnited Kingdom of Great Britain and Northern Ireland
Period18 Jul 201121 Jul 2011


  • collective detection scheme
  • error detection
  • receptor density algorithm
  • self-detection
  • swarm robotics

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