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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Fault Detection in a Swarm of Physical Robots Based on Behavioral Outlier Detection

Tarapore, Danesh, Timmis, Jonathan and Christensen, Anders Lyhne (2019) Fault Detection in a Swarm of Physical Robots Based on Behavioral Outlier Detection. IEEE Transactions on Robotics, 35 (6). pp. 1516-1522. ISSN 1552-3098

Item Type: Article


The ability to reliably detect faults is essential in many real-world tasks that robot swarms have the potential to perform. Most studies on fault detection in swarm robotics have been conducted exclusively in simulation, and they have focused on a single type of fault or a specific task.In a series of previous studies, we have developed a robust fault-detection approach in which robots in a swarm learn to distinguish between normal and faulty behaviors online. In this paper, we assess the performance of our fault-detection approach on a swarm of seven physical mobile robots.We experiment with three classic swarm robotics tasks and consider several types of faults in both sensors and actuators. Experimental results show that the robots are able to reliably detect the presence of hardware faults in one another even when the swarm behavior is changed during operation. This paper is thus an important step toward making robot swarms sufficiently reliable and dependable for real-world applications.

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Depositing User: Leah Maughan


Item ID: 11522
Identification Number:
ISSN: 1552-3098
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Users with ORCIDS

ORCID for Jonathan Timmis: ORCID iD

Catalogue record

Date Deposited: 10 Feb 2020 12:07
Last Modified: 04 Sep 2020 14:15


Author: Jonathan Timmis ORCID iD
Author: Danesh Tarapore
Author: Anders Lyhne Christensen

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Faculty of Technology

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