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Planning search and rescue missions for UAV teams

Planning search and rescue missions for UAV teams
Planning search and rescue missions for UAV teams
The coordination of multiple Unmanned Aerial Vehicles (UAVs) to carry out aerial surveys is a major challenge for emergency responders. In particular, UAVs have to fly over kilometre-scale areas while trying to discover casualties as quickly as possible. To aid in this process, it is desirable to exploit the increasing availability of data about a disaster from sources such as crowd reports, satellite remote sensing, or manned reconnaissance. In particular, such information can be a valuable resource to drive the planning of UAV flight paths over a space in order to discover people who are in danger. However challenges of computational tractability remain when planning over the very large action spaces that result. To overcome these, we introduce the survivor discovery problem and present as our solution, the first example of a continuous factored coordinated Monte Carlo tree search algorithm. Our evaluation against state of the art benchmarks show that our algorithm, Co-CMCTS, is able to localise more casualties faster than standard approaches by 7% or more on simulations with real-world data.
1777 - 1782
IOS Press
Baker, Chris
154c2ad7-51c1-41ff-91ea-c598512eab36
Ramchurn, Gopal
1d62ae2a-a498-444e-912d-a6082d3aaea3
Teacy, Luke
5f962a10-9ab5-4b19-8016-cc72588bdc6a
Jennings, Nicholas
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Baker, Chris
154c2ad7-51c1-41ff-91ea-c598512eab36
Ramchurn, Gopal
1d62ae2a-a498-444e-912d-a6082d3aaea3
Teacy, Luke
5f962a10-9ab5-4b19-8016-cc72588bdc6a
Jennings, Nicholas
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Baker, Chris, Ramchurn, Gopal, Teacy, Luke and Jennings, Nicholas (2016) Planning search and rescue missions for UAV teams. In ECAI 2016. vol. 285, IOS Press. 6 pp, 1777 - 1782. (In Press) (doi:10.3233/978-1-61499-672-9-1777).

Record type: Conference or Workshop Item (Paper)

Abstract

The coordination of multiple Unmanned Aerial Vehicles (UAVs) to carry out aerial surveys is a major challenge for emergency responders. In particular, UAVs have to fly over kilometre-scale areas while trying to discover casualties as quickly as possible. To aid in this process, it is desirable to exploit the increasing availability of data about a disaster from sources such as crowd reports, satellite remote sensing, or manned reconnaissance. In particular, such information can be a valuable resource to drive the planning of UAV flight paths over a space in order to discover people who are in danger. However challenges of computational tractability remain when planning over the very large action spaces that result. To overcome these, we introduce the survivor discovery problem and present as our solution, the first example of a continuous factored coordinated Monte Carlo tree search algorithm. Our evaluation against state of the art benchmarks show that our algorithm, Co-CMCTS, is able to localise more casualties faster than standard approaches by 7% or more on simulations with real-world data.

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More information

Accepted/In Press date: 10 June 2016
Venue - Dates: PAIS 2016: Conference on Prestigious Applications of Intelligent Systems at ECAI 2016, Netherlands, 2016-08-31 - 2016-09-02
Related URLs:
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 396996
URI: https://eprints.soton.ac.uk/id/eprint/396996
PURE UUID: bf9c6a7b-0b17-4bca-9657-e626f013f304
ORCID for Gopal Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 13 Jul 2016 10:36
Last modified: 19 Oct 2018 00:33

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Contributors

Author: Chris Baker
Author: Gopal Ramchurn ORCID iD
Author: Luke Teacy
Author: Nicholas Jennings

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