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To err is robotic, to tolerate immunological: fault detection in multirobot systems

To err is robotic, to tolerate immunological: fault detection in multirobot systems
To err is robotic, to tolerate immunological: fault detection in multirobot systems
Fault detection and fault tolerance represent two of the most important and largely unsolved issues in the field of multirobot systems (MRS). Efficient, long-term operation requires an accurate, timely detection, and accommodation of abnormally behaving robots. Most existing approaches to fault-tolerance prescribe a characterization of normal robot behaviours, and train a model to recognize these behaviours. Behaviours unrecognized by the model are consequently labelled abnormal or faulty. MRS employing these models do not transition well to scenarios involving temporal variations in behaviour (e.g., online learning of new behaviours, or in response to environment perturbations). The vertebrate immune system is a complex distributed system capable of learning to tolerate the organism's tissues even when they change during puberty or metamorphosis, and to mount specific responses to invading pathogens, all without the need of a genetically hardwired characterization of normality. We present a generic abnormality detection approach based on a model of the adaptive immune system, and evaluate the approach in a swarm of robots. Our results reveal the robust detection of abnormal robots simulating common electro-mechanical and software faults, irrespective of temporal changes in swarm behaviour. Abnormality detection is shown to be scalable in terms of the number of robots in the swarm, and in terms of the size of the behaviour classification space.
1748-3182
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Lima, Pedro U.
c704e178-5c25-4f01-818c-46d3c0d53ed7
Carneiro, Jorge
cd7a4699-e04d-432f-b166-5aa894b89dac
Christensen, Anders Lyhne
423aa920-18cb-4913-8643-626db65a9222
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Lima, Pedro U.
c704e178-5c25-4f01-818c-46d3c0d53ed7
Carneiro, Jorge
cd7a4699-e04d-432f-b166-5aa894b89dac
Christensen, Anders Lyhne
423aa920-18cb-4913-8643-626db65a9222

Tarapore, Danesh, Lima, Pedro U., Carneiro, Jorge and Christensen, Anders Lyhne (2015) To err is robotic, to tolerate immunological: fault detection in multirobot systems. Bioinspiration & Biomimetics, 10 (1), [016014]. (doi:10.1088/1748-3190/10/1/016014).

Record type: Article

Abstract

Fault detection and fault tolerance represent two of the most important and largely unsolved issues in the field of multirobot systems (MRS). Efficient, long-term operation requires an accurate, timely detection, and accommodation of abnormally behaving robots. Most existing approaches to fault-tolerance prescribe a characterization of normal robot behaviours, and train a model to recognize these behaviours. Behaviours unrecognized by the model are consequently labelled abnormal or faulty. MRS employing these models do not transition well to scenarios involving temporal variations in behaviour (e.g., online learning of new behaviours, or in response to environment perturbations). The vertebrate immune system is a complex distributed system capable of learning to tolerate the organism's tissues even when they change during puberty or metamorphosis, and to mount specific responses to invading pathogens, all without the need of a genetically hardwired characterization of normality. We present a generic abnormality detection approach based on a model of the adaptive immune system, and evaluate the approach in a swarm of robots. Our results reveal the robust detection of abnormal robots simulating common electro-mechanical and software faults, irrespective of temporal changes in swarm behaviour. Abnormality detection is shown to be scalable in terms of the number of robots in the swarm, and in terms of the size of the behaviour classification space.

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bb_faultdetection - Accepted Manuscript
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Accepted/In Press date: 3 November 2014
e-pub ahead of print date: 2 February 2015
Published date: 2 February 2015
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 410783
URI: http://eprints.soton.ac.uk/id/eprint/410783
ISSN: 1748-3182
PURE UUID: e3b65816-b620-4275-b5a4-52ddcbbac578
ORCID for Danesh Tarapore: ORCID iD orcid.org/0000-0002-3226-6861

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Date deposited: 09 Jun 2017 09:38
Last modified: 16 Mar 2024 04:29

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Contributors

Author: Danesh Tarapore ORCID iD
Author: Pedro U. Lima
Author: Jorge Carneiro
Author: Anders Lyhne Christensen

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