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Coordinating human-UAV teams in disaster response

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
b79f9800-2819-40c8-96e7-3ad85f866f5e
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Chen, Xiaoping
3256467f-026f-4cea-beb6-20948f6f4d93
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), United States. 09 - 15 Jul 2016. pp. 1-7.

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.

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More information

e-pub ahead of print date: April 2016
Venue - Dates: International Joint Conference on Artificial Intelligence (IJCAI-16), United States, 2016-07-09 - 2016-07-15
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 393725
URI: https://eprints.soton.ac.uk/id/eprint/393725
PURE UUID: 1f9bb050-e9aa-4d17-95ff-5c766da835cc
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 03 May 2016 11:18
Last modified: 06 Jun 2018 12:42

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Contributors

Author: Feng Wu
Author: Sarvapali Ramchurn ORCID iD
Author: Xiaoping Chen

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