TagTeam: Towards Wearable-Assisted, Implicit Guidance for Human–Drone Teams
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Date
2022-08-10
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Citation of Original Publication
Jayarajah, Kasthuri, Aryya Gangopadhyay, and Nicholas Waytowich. “TagTeam: Towards Wearable-Assisted, Implicit Guidance for Human-Drone Teams.” In Proceedings of the 1st ACM Workshop on Smart Wearable Systems and Applications, 13–18. SmartWear ’22. New York, NY, USA: Association for Computing Machinery, 2022. https://doi.org/10.1145/3556560.3560715.
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Public Domain Mark 1.0
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Abstract
The availability of sensor-rich smart wearables and tiny,
yet capable, unmanned vehicles such as nano quadcopters,
opens up opportunities for a novel class of highly interactive, attention-shared human–machine teams. Reliable, lightweight, yet passive exchange of intent, data and inferences
within such human–machine teams make them suitable for
scenarios such as search-and-rescue with significantly improved performance in terms of speed, accuracy and semantic awareness. In this paper, we articulate a vision for
such human–drone teams and key technical capabilities such
teams must encompass. We present TagTeam, an early prototype of such a team and share promising demonstration of
a key capability (i.e., motion awareness).