Close menu

SURE

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

Void reduction in self-healing swarms

Eliot, Neil, Kendall, David, Moon, Alun, Brockway, Michael and Amos, Martyn (2019) Void reduction in self-healing swarms. Artificial Life Conference Proceedings, 9. pp. 87-94.

Item Type: Article

Abstract

Swarms consist of many agents that interact according to a
simple set of rules, giving rise to emergent global behaviours.
In this paper, we consider swarms of mobile robots or drones.
Swarms can be tolerant of faults that may occur for many
reasons, such as resource exhaustion, component failure, or
disruption from an external event. The loss of agents reduces
the size of a swarm, and may create an irregular structure in
the swarm topology. A swarm’s structure can also be irregular due to initial conditions, or the existence of an obstacle.
These changes in the structure or size of a swarm do not stop
it from functioning, but may adversely affect its efficiency or
effectiveness. In this paper, we describe a self-healing mechanism to counter the effect of agent loss or structural irregularity. This method is based on the reduction of concave
regions at swarm perimeter regions. Importantly, this method
requires no expensive communication infrastructure, relying
only on agent proximity information. We illustrate the application of our method to the problem of surrounding an oil
slick, and show that void reduction is necessary for full and
close containment, before concluding with a brief discussion
of its potential uses in other domains.

[img]
Preview
PDF
isal_a_00146.pdf - Published Version

Download (2MB) | Preview

More Information

Related URLs:
Depositing User: Neil Eliot

Identifiers

Item ID: 17507
Identification Number: https://doi.org/10.1162/isal_a_00146
URI: http://sure.sunderland.ac.uk/id/eprint/17507
Official URL: https://direct.mit.edu/isal/isal2019/volume/31

Users with ORCIDS

ORCID for Neil Eliot: ORCID iD orcid.org/0000-0002-7591-7783

Catalogue record

Date Deposited: 19 Apr 2024 14:27
Last Modified: 19 Apr 2024 14:30

Contributors

Author: Neil Eliot ORCID iD
Author: David Kendall
Author: Alun Moon
Author: Michael Brockway
Author: Martyn Amos

University Divisions

Faculty of Technology > School of Computer Science

Subjects

Computing > Artificial Intelligence

Actions (login required)

View Item View Item