TagTeam: Towards Wearable-Assisted, Implicit Guidance for Human–Drone Teams

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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.


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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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).