Coordinating human-UAV teams in disaster response
Coordinating human-UAV teams in disaster response
We consider a disaster response scenario where emergency responders have to complete rescue tasks in dynamic and uncertain environment with the assistance of multiple UAVs to collect information about the disaster space. To capture the uncertainty and partial observability of the domain, we model this problem as a POMDP. However, the resulting model is computationally intractable and cannot be solved by most existing POMDP solvers due to the large state and action spaces. By exploiting the problem structure we propose a novel online planning algorithm to solve this model. Specifically, we generate plans for the responders based on Monte-Carlo simulations and compute actions for the UAVs according to the value of information. Our empirical results confirm that our algorithm significantly outperforms the state-of-the-art both in time and solution quality.
1-7
Wu, Feng
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Ramchurn, Sarvapali
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Chen, Xiaoping
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Wu, Feng
b79f9800-2819-40c8-96e7-3ad85f866f5e
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Chen, Xiaoping
3256467f-026f-4cea-beb6-20948f6f4d93
Wu, Feng, Ramchurn, Sarvapali and Chen, Xiaoping
(2016)
Coordinating human-UAV teams in disaster response.
International Joint Conference on Artificial Intelligence (IJCAI-16), New York, New York, United States.
09 - 15 Jul 2016.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
We consider a disaster response scenario where emergency responders have to complete rescue tasks in dynamic and uncertain environment with the assistance of multiple UAVs to collect information about the disaster space. To capture the uncertainty and partial observability of the domain, we model this problem as a POMDP. However, the resulting model is computationally intractable and cannot be solved by most existing POMDP solvers due to the large state and action spaces. By exploiting the problem structure we propose a novel online planning algorithm to solve this model. Specifically, we generate plans for the responders based on Monte-Carlo simulations and compute actions for the UAVs according to the value of information. Our empirical results confirm that our algorithm significantly outperforms the state-of-the-art both in time and solution quality.
Text
13587-59023-1-SM.pdf
- Accepted Manuscript
More information
e-pub ahead of print date: April 2016
Venue - Dates:
International Joint Conference on Artificial Intelligence (IJCAI-16), New York, New York, United States, 2016-07-09 - 2016-07-15
Organisations:
Agents, Interactions & Complexity
Identifiers
Local EPrints ID: 393725
URI: http://eprints.soton.ac.uk/id/eprint/393725
PURE UUID: 1f9bb050-e9aa-4d17-95ff-5c766da835cc
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Date deposited: 03 May 2016 11:18
Last modified: 15 Mar 2024 03:22
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Contributors
Author:
Feng Wu
Author:
Sarvapali Ramchurn
Author:
Xiaoping Chen
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