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Fault detection in a swarm of physical robots based on behavioral outlier detection

Fault detection in a swarm of physical robots based on behavioral outlier detection
Fault detection in a swarm of physical robots based on behavioral outlier detection
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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Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Timmis, Jon
b68f4b8e-6192-4caf-858d-8185f6e7c66f
Anders Lyhne, Christensen
97736755-1c9b-4472-99b4-98e1b4a9c647
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Timmis, Jon
b68f4b8e-6192-4caf-858d-8185f6e7c66f
Anders Lyhne, Christensen
97736755-1c9b-4472-99b4-98e1b4a9c647

Tarapore, Danesh, Timmis, Jon and Anders Lyhne, Christensen (2019) Fault detection in a swarm of physical robots based on behavioral outlier detection. IEEE Transactions on Robotics, 1-7. (doi:10.1109/TRO.2019.2929015).

Record type: Article

Abstract

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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More information

Accepted/In Press date: 4 July 2019
Published date: 5 August 2019

Identifiers

Local EPrints ID: 433404
URI: https://eprints.soton.ac.uk/id/eprint/433404
PURE UUID: 0318fa9d-0efc-40a7-95b8-6bf08c2b429e
ORCID for Danesh Tarapore: ORCID iD orcid.org/0000-0002-3226-6861

Catalogue record

Date deposited: 20 Aug 2019 16:30
Last modified: 21 Aug 2019 00:26

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