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.
» Read on@inproceedings{WRCijcai16,
address = {New York, USA},
author = {Feng Wu and Sarvapali D. Ramchurn and Xiaoping Chen},
booktitle = {Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI)},
month = {July},
pages = {524-530},
title = {Coordinating Human-UAV Teams in Disaster Response},
year = {2016}
}