Planning search and rescue missions for UAV teams
File(s)
Published version
Author(s)
Jennings, N
Baker, CAB
Ramchurn, SD
Teacy, WLT
Type
Conference 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 re-
mote sensing, or manned reconnaissance. In particular, such inform-
ation 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 plan-
ning over the very large action spaces that result. To overcome these,
we introduce the survivor discovery problem and present as our solu-
tion, 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.
(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 re-
mote sensing, or manned reconnaissance. In particular, such inform-
ation 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 plan-
ning over the very large action spaces that result. To overcome these,
we introduce the survivor discovery problem and present as our solu-
tion, 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.
Date Issued
2016-09-01
Date Acceptance
2016-08-01
Citation
Proceedings of the 22nd European Conference on Artificial Intelligence, 2016, pp.1777-1782
ISBN
978-1-61499-672-9
Publisher
IOS Press
Start Page
1777
End Page
1782
Journal / Book Title
Proceedings of the 22nd European Conference on Artificial Intelligence
Copyright Statement
© 2016 The Authors and IOS Press.
This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
Source
Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI 2016)
Publication Status
Published
Start Date
2016-08-29
Finish Date
2016-09-02
Coverage Spatial
The Hague, Holland