Scalable target detection for large robot teams
Abstract
In this paper, we present an asynchronous display method, coined image queue, which allows operators to search through a large amount of data gathered by autonomous robot teams. We discuss and investigate the advantages of an asynchronous display for foraging tasks with emphasis on Urban Search and Rescue. The image queue approach mines video data to present the operator with a relevant and comprehensive view of the environment in order to identify targets of interest such as injured victims. It fills the gap for comprehensive and scalable displays to obtain a network-centric perspective for UGVs. We compared the image queue to a traditional synchronous display with live video feeds and found that the image queue reduces errors and operator's workload. Furthermore, it disentangles target detection from concurrent system operations and enables a call center approach to target detection. With such an approach we can scale up to very large multi-robot systems gathering huge amounts of data that is then distributed to multiple operators.
BibTeX
@conference{Wang-2011-7241,author = {Huadong Wang and Andreas Kolling and Nathan Brooks and Sean R. Owens and Shafiq Abedin and Paul Scerri and Pei-Ju Lee and Shih-Yi Chien and Michael Lewis and Katia Sycara},
title = {Scalable target detection for large robot teams},
booktitle = {Proceedings of 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI '11)},
year = {2011},
month = {March},
pages = {363 - 370},
}