FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets

[2607.26423] FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets Skip to main content Search Submit Donate Log in Computer Science > Human-Computer Interaction arXiv:2607.26423 (cs) [Submitted on 29 Jul 2026 ( v1 ), last revised 28 Aug 2026 (this version, v2)] Title: FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets Authors: Peisen Xu , Jérémie Garcia , Peter Cleveland , Wei Tsang Ooi , Christophe Jouffrais View PDF HTML (experimental) 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. Comments: UIST 2026 Subjects: Human-Computer Interaction (cs.HC) ; Robotics (cs.RO) MSC classes: 68U35 Cite as: arXiv:2607.26423 [cs.HC] (or arXiv:2607.26423v2 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2607.26423 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1145/3830398.3830597 Focus to learn more DOI(s) linking to related resources Submission history From: Peisen Xu [ view email ] [v1] Wed, 29 Jul 2026 03:09:10 UTC (1,780 KB) [v2] Fri, 28 Aug 2026 02:39:47 UTC (1,780 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.HC < prev | next > new | recent | 2026-07 Change to browse by: cs cs.RO References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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