A methodology for deploying the max-sum algorithm and a case study on unmanned aerial vehicles
A methodology for deploying the max-sum algorithm and a case study on unmanned aerial vehicles
We present a methodology for the deployment of the max-sum algorithm, a well known decentralised algorithm for coordinating autonomous agents, for problems related to situational awareness. In these settings, unmanned autonomous vehicles are deployed to collect information about an unknown environment. Our methodology then helps identify the choices that need to be made to apply the algorithm to these problems. Next, we present a case study where the methodology is used to develop a system for disaster management in which a team of unmanned aerial vehicles coordinate to provide the first responders of the area of a disaster with live aerial imagery. To evaluate this system, we deploy it on two unmanned hexacopters in a variety of scenarios. Our tests show that the system performs well when confronted with the dynamism and the heterogeneity of the real world.
2275-2280
Delle Fave, Francesco Maria
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Farinelli, Alessandro
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Rogers, Alex
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Jennings, Nick
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2012
Delle Fave, Francesco Maria
1a71a79a-fb96-4bfc-9158-36f8dcb5d96f
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Delle Fave, Francesco Maria, Farinelli, Alessandro, Rogers, Alex and Jennings, Nick
(2012)
A methodology for deploying the max-sum algorithm and a case study on unmanned aerial vehicles.
IAAI 2012: The Twenty-Fourth Innovative Applications of Artificial Intelligence Conference, Toronto, Canada.
24 - 26 Jul 2012.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
We present a methodology for the deployment of the max-sum algorithm, a well known decentralised algorithm for coordinating autonomous agents, for problems related to situational awareness. In these settings, unmanned autonomous vehicles are deployed to collect information about an unknown environment. Our methodology then helps identify the choices that need to be made to apply the algorithm to these problems. Next, we present a case study where the methodology is used to develop a system for disaster management in which a team of unmanned aerial vehicles coordinate to provide the first responders of the area of a disaster with live aerial imagery. To evaluate this system, we deploy it on two unmanned hexacopters in a variety of scenarios. Our tests show that the system performs well when confronted with the dynamism and the heterogeneity of the real world.
Text
delle_fave_iaai_2012.pdf
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More information
Submitted date: March 2012
Published date: 2012
Venue - Dates:
IAAI 2012: The Twenty-Fourth Innovative Applications of Artificial Intelligence Conference, Toronto, Canada, 2012-07-24 - 2012-07-26
Organisations:
Agents, Interactions & Complexity
Identifiers
Local EPrints ID: 336266
URI: http://eprints.soton.ac.uk/id/eprint/336266
PURE UUID: dd7afc33-1eb6-4c76-8083-db121973b805
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Date deposited: 20 Mar 2012 13:44
Last modified: 14 Mar 2024 10:40
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Contributors
Author:
Francesco Maria Delle Fave
Author:
Alessandro Farinelli
Author:
Alex Rogers
Author:
Nick Jennings
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