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Factored Monte-Carlo tree search for coordinating UAVs in disaster response

Factored Monte-Carlo tree search for coordinating UAVs in disaster response
Factored Monte-Carlo tree search for coordinating UAVs in disaster response
The coordination of multiple Unmanned Aerial Vehicles (UAVs) to carry out 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. However, an increase in the availability of real-time data about a disaster from sources such as crowd reports or satellites presents a valuable source of information to drive the planning of UAV flight paths over a space in order to discover people who are in danger. Nevertheless challenges remain when planning over the very large action spaces that result. To this end, we introduce the survivor discovery problem and present as our solution, the first example of a factored coordinated Monte Carlo tree search algorithm to perform decentralised path planning for multiple coordinated UAVs. Our evaluation against standard benchmarks show that our algorithm, Co-MCTS, is able to find more casualties faster than standard approaches by 10% or more on simulations with real-world data from the 2010 Haiti earthquake.
6-15
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
Komenda,, Antonín
Shani, Guy
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
Komenda,, Antonín
Shani, Guy

Baker, Chris, Ramchurn, Gopal, Teacy, Luke and Jennings, Nicholas (2016) Factored Monte-Carlo tree search for coordinating UAVs in disaster response. Komenda,, Antonín and Shani, Guy (eds.) In ICAPS Proceedings of the 4th Workshop on Distributed and Multi-Agent Planning (DMAP-2016). pp. 6-15 .

Record type: Conference or Workshop Item (Paper)

Abstract

The coordination of multiple Unmanned Aerial Vehicles (UAVs) to carry out 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. However, an increase in the availability of real-time data about a disaster from sources such as crowd reports or satellites presents a valuable source of information to drive the planning of UAV flight paths over a space in order to discover people who are in danger. Nevertheless challenges remain when planning over the very large action spaces that result. To this end, we introduce the survivor discovery problem and present as our solution, the first example of a factored coordinated Monte Carlo tree search algorithm to perform decentralised path planning for multiple coordinated UAVs. Our evaluation against standard benchmarks show that our algorithm, Co-MCTS, is able to find more casualties faster than standard approaches by 10% or more on simulations with real-world data from the 2010 Haiti earthquake.

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More information

Accepted/In Press date: 1 April 2016
e-pub ahead of print date: 13 June 2016
Published date: June 2016
Venue - Dates: Distributed and Multi-Agent Planning, United Kingdom, 2016-06-14 - 2016-06-14
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 393649
URI: https://eprints.soton.ac.uk/id/eprint/393649
PURE UUID: 43150875-1cd3-441c-b58d-3f731cfec5e1
ORCID for Gopal Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 14 Jun 2016 15:43
Last modified: 14 Mar 2019 01:42

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Contributors

Author: Chris Baker
Author: Gopal Ramchurn ORCID iD
Author: Luke Teacy
Author: Nicholas Jennings
Editor: Antonín Komenda,
Editor: Guy Shani

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