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This research addresses the critical human-in-the-loop challenge of supervising scalable drone swarms by introducing a Mixed Reality sandtable interface. The study demonstrates how spatial interaction techniques can enhance situational awareness during complex missions but also identifies cognitive limits that emerge as fleet sizes grow beyond current thresholds. These findings offer essential design implications for building next-generation control systems capable of managing autonomous robotic fleets safely and effectively.
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FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets

arXiv3 min read

arXiv:2607.26423v1 Announce Type: cross Abstract: As autonomous drone deployments scale from individual units to coordinated swarms, the human operator's role shifts from direct piloting to high-level supervision. Current interfaces often treat multi-drone control as a scaled-up version of single-drone operation. We instead investigate how reframing fleet supervision as spatial interaction can better support the spatial, temporal, and safety demands of complex missions. We present FleetScape, a Mixed Reality (MR) sandtable system that externalizes layered real-time mission, safety, and environmental data while enabling fluid transitions between manual intervention and autonomous supervision. We developed a high-fidelity building inspection simulation that generates and streams synchronized multi-drone and environmental data for MR visualizations. We used this prototype to conduct a user study with six experienced drone pilots managing fleets of up to 15 drones. Our findings show that FleetScape supports situational awareness through layered spatial representations and clarifies control mode transitions. However, a limit to situational awareness was observed as fleet size increases, leading to different supervisory strategies. Finally, we derive design implications for supporting scalable drone fleet supervision.

Abstract:As autonomous drone deployments scale from individual units to coordinated swarms, the human operator's role shifts from direct piloting to high-level supervision. Current interfaces often treat multi-drone control as a scaled-up version of single-drone operation. We instead investigate how reframing fleet supervision as spatial interaction can better support the spatial, temporal, and safety demands of complex missions. We present FleetScape, a Mixed Reality (MR) sandtable system that externalizes layered real-time mission, safety, and environmental data while enabling fluid transitions between manual intervention and autonomous supervision. We developed a high-fidelity building inspection simulation that generates and streams synchronized multi-drone and environmental data for MR visualizations. We used this prototype to conduct a user study with six experienced drone pilots managing fleets of up to 15 drones. Our findings show that FleetScape supports situational awareness through layered spatial representations and clarifies control mode transitions. However, a limit to situational awareness was observed as fleet size increases, leading to different supervisory strategies. Finally, we derive design implications for supporting scalable drone fleet supervision.

Human-Computer Interaction (cs.HC); Robotics (cs.RO)

arXiv:2607.26423 [cs.HC]

(or arXiv:2607.26423v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2607.26423 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1145/3830398.3830597 Focus to learn more DOI(s) linking to related resources

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Full text under the publisher's open licence · 276 words

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