A Study of Human-Agent Collaboration for Multi-UAV Task Allocation in Dynamic Environments

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We consider a setting where a team of humans oversee the coordination of multiple Unmanned Aerial Vehicles (UAVs) to perform a number of search tasks in dynamic environments that may cause the UAVs to drop out. Hence, we develop a set of multi-UAV supervisory control interfaces and a multi-agent coordination algorithm to support human decision making in this setting. To elucidate the resulting interactional issues, we compare manual and mixed-initiative task allocation in both static and dynamic environments in lab studies with 40 participants and observe that our mixed-initiative system results in lower workloads and better performance in re-planning tasks than one which only involves manual task allocation. Our analysis points to new insights into the way humans appropriate flexible autonomy.

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@inproceedings{RFIijcai15,
 address = {Buenos Aires, Argentina},
 author = {Sarvapali D. Ramchurn and Joel E. Fischer and Yuki Ikuno and Feng Wu and Jack Flann and Antony Waldock},
 booktitle = {Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI)},
 month = {July},
 pages = {1184-1192},
 title = {A Study of Human-Agent Collaboration for Multi-UAV Task Allocation in Dynamic Environments},
 year = {2015}
}