Pith. sign in

REVIEW 3 major objections 3 minor 52 references

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

T0 review · 3 major / 3 minor · reviewed 2026-08-01 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Reframing drone fleet supervision as spatial interaction on a mixed-reality sandtable supports situational awareness up to about 10 drones, beyond which awareness degrades and operators fall back to reactive monitoring.

desk verdict Solid exploratory system paper with a real design contribution, but the SAGAT scoring appendix has an internal ambiguity that makes some reported SA numbers unverifiable; the qualitative strategy-shift finding holds up. read the letter →

arxiv 2607.26423 v1 pith:L3NBMS26 submitted 2026-07-29 cs.HC cs.RO

Peisen Xu , Jérémie Garcia , Peter Cleveland , Ooi Wei Tsang , Christophe Jouffrais This is my paper
classification cs.HCcs.RO
keywords MixedRealitySpatialInterfacesHuman-DroneInteractionMulti-dronesupervisionSituationalawarenessSandtableSwarmroboticsFan-out
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that supervising a drone fleet is fundamentally a spatial task, and that a 3D mixed-reality sandtable—a miniature of the operational environment that layers mission, safety, and environmental data in one place—can support an operator's situational awareness better than scaled-up single-drone interfaces. To test this, the authors built a building-inspection simulation and ran a study with six professional pilots managing fleets of 5, 10, and 15 drones. They found that pilots could maintain awareness and effective control up to roughly 10 drones; at 15 drones, awareness and per-drone productivity dropped, and pilots shifted from proactive optimization to reactive, exception-based monitoring. If the finding transfers to the field, it sets a practical scaling boundary for single-operator drone fleet supervision and motivates design changes—adaptive visualizations, grouping, pre-mission planning—beyond that boundary.

What carries the argument

The central mechanism is the spatially grounded sandtable: a 3D miniature of the mission environment into which all data layers are projected, so the operator perceives relationships between drones, tasks, and constraints directly instead of through mental rotation. Supporting it are focus-aware drone miniatures (detailed for the selected drone, simplified for the rest), layered visualizations (building boundaries, waypoints, point clouds), and a control-transition design that keeps mode state visually consistent across the sandtable, minimap, and status board. The fan-out metric—activity time divided by interaction time—serves as the predictive scaling tool, yielding the ~10.6 drone estimat

What would settle it

A field test with real drones, real latency, and real GPS/wind conditions—keeping the same autonomy and interface—would settle the claim: if operators maintain drone-level awareness past 10 drones, the limit is a simulation artifact; if awareness collapses earlier, the ceiling is lower. Measuring true interaction times in the field and recomputing the fan-out ratio would directly test the 10.6 estimate.

Watch

Extended reading notes

Core claim

FleetScape's central claim is that fleet supervision can be restructured as spatial interaction: rather than juggling separate camera feeds, telemetry panels, and 2D maps, an operator works with a unified 3D sandtable where drones, waypoints, building boundaries, point clouds, and alerts sit at their true positions. The design allows fluid shifts between global supervision, waypoint replanning, and manual control of one drone without losing mission context. In a study with six expert pilots, drone-level (safety) awareness fell from 0.86 at 5 drones to 0.61 at 15, and per-drone throughput from 39.6 to 32.1 covered waypoints, while mission-level awareness held fairly steady. The authors read t

Load-bearing premise

The load-bearing premise is that the simulated building-inspection mission is representative enough of real drone-fleet supervision that the observed awareness decline and the ~10 drone boundary will transfer to physical operations with real latency, communication constraints, and risk.

Editorial extensions

If this is right

  • A single operator can maintain situational awareness and effective control over a fleet of 5–10 drones when supervision is grounded in a spatial sandtable.
  • Mission-level awareness (which buildings are covered) survives fleet growth, but drone-level safety awareness declines, so interfaces must treat the two as separate design targets.
  • Beyond about 10 drones, per-drone inspection throughput drops and operators switch from proactive optimization to reactive, exception-based monitoring.
  • Scaling past this range requires adaptive visualizations, control groups, abstraction, and pre-mission planning, rather than only better real-time interaction techniques.
  • The fan-out estimate of approximately 10.6 gives a concrete, reusable upper bound for single-operator supervision under reliable autonomy, with 5–10 drones as the conservative practical range.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A physical deployment with real latency, communication dropouts, and actual risk would likely push the practical ceiling below 10 drones; the paper itself describes 10.6 as an upper bound from simulation.
  • The observed shift from proactive optimization to reactive, exception-based monitoring may be a general signature of cognitive saturation, suggesting that scalable interfaces should be designed for exception handling (prioritization, grouping, abstraction) rather than expecting continuous spatial optimization.
  • The divergence between stable mission-level awareness and falling drone-level awareness implies future systems could separate the two roles: a global spatial view for survey and a separate automated attention system for individual drone safety.
  • A direct testable extension: repeat the same task with novice operators or with different GPS-error event rates; the ~10 boundary likely shifts with training and scenario stress, which would refine the design guidance.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 3 minor

Summary. The paper presents FleetScape, a mixed-reality sandtable interface for supervising fleets of autonomous drones in a building-inspection task. The system combines a 3D sandtable with a minimap, status board, and per-drone control panel to support spatial reasoning and transitions among manual control, semi-autonomous route planning, and autonomous inspection. The authors report an exploratory user study with six experienced drone pilots operating fleets of 5, 10, and 15 drones in a Unity-based simulator. They find that mission perception improves with fleet size while mission comprehension and drone-status situational awareness decline, and they propose a fan-out-derived estimate of about 10 drones as the practical upper bound for single-operator spatial supervision. The paper also derives qualitative design implications around adaptive visualization, control grouping, and pre-mission planning.

Significance. If the results are reliable, the paper makes a useful design contribution by framing drone-fleet supervision as a spatial-interaction problem and by providing a concrete scaling boundary for one operator. The use of professional pilots, the transparently described simulation, the SAGAT freezes grounded in simulator ground truth, and the explicit fan-out calculation are strengths. The manuscript also candidly acknowledges its exploratory nature and the simulated setting. However, the central empirical claim — the decline in situational awareness and the 'around 10 drones' limit — rests on the SAGAT scores, and the scoring protocol as written does not fit all of the reported questions. The study is also too small and too underpowered to support strong quantitative claims without inferential statistics. The design implications remain valuable even if the precise number is treated as provisional.

major comments (3)
  1. [Appendix B.4 / Table 3] The scoring protocol in Eq. (4) applies the Jaccard index to a ground-truth set G and a response set R for perception and comprehension questions. However, Table 3 contains count questions such as 'How many buildings have finished surface(s)?', 'How many drone(s) is in one of the stoppage statuses...', and 'how many drone(s) are likely to stop the automation within the next 1 minute?'. If participants answered these with a numeral, Eq. (4) is undefined. If they were instead asked to enumerate the set of buildings/drones, that response format is not reported. Since the drone-status SA decline (0.86→0.63→0.61) and mission-comprehension decline (0.85→0.58) in Fig. 4 are the primary quantitative support for the ~10-drone limit, please specify the response format per question and the exact scoring rule used. If counts were converted to sets, describe the conversion and justify it.
  2. [§4.2.1 / Fig. 4] With n=6 and the reported standard deviations being large (e.g., mission comprehension SDs of 0.28 and 0.26; drone comprehension SD of 0.33 in the 15-drone condition), the directional statements 'decreased', 'dropped', and 'a limit was observed' are not backed by inferential statistics. The paper itself labels the study exploratory, yet the abstract and conclusion present 'around 10 drones' as an empirical finding. Please either provide appropriate statistical tests or effect sizes with a clear caveat about the small sample, or rephrase the central scaling claim as a descriptive observation to be confirmed in larger studies.
  3. [§4.1.2 / Appendix C.1 / §6] The fan-out estimate of FO≈10.6 is computed from interaction and activity times measured in the same prototype and simulation, and it is used to select the 5/10/15 fleet-size conditions. The Discussion and Limitations then invoke this estimate as supporting the observed 5–10 range. This is not a fully independent validation. Please clarify that the SAGAT-based SA decline is the primary evidence for the scaling limit and that the fan-out calculation is a design heuristic. Also clarify whether the timing data in Appendix C.1 come from the trial-stage pilots or the final participants, since the current text is ambiguous.
minor comments (3)
  1. [§4.2.1] The text refers to 'Per-drone performance (Figure 4.E)', but the Figure 4 caption lists per-drone productivity as panel (C), with Bedford workload as (D) and SART as (E). Please correct the cross-reference.
  2. [Appendix C.1] The line 'IT negligible: SA takeoff≈5s' is unclear. If takeoff time is negligible, why is 5 s listed? Please clarify the notation and the units for the rates (s⁻¹).
  3. [Appendix B.4] The projection-question scoring is described as binary and based on 'coherence between the answer and the reason given'. This is underspecified. Please define what counts as coherent, especially for the mission-projection question ('which building will be finished next?').

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the ~10-drone SA limit is grounded in direct SAGAT/workload measurements; fan-out and self-citations are not load-bearing reductions.

full rationale

FleetScape's central claim is an empirical limit on single-operator fleet supervision (~10 drones). The support is direct measurement: SAGAT drone/safety SA declines from 0.86 to 0.63/0.61 (Fig. 4B), mission comprehension declines from 0.85 to 0.58, SART/Bedford move monotonically, and interviews report loss of detailed awareness at 15 drones. None of these quantities is obtained by plugging the claimed conclusion back into a fitted parameter. The one calculation that might look circular is Appendix C.1: it uses trial-pilot interaction times and designed event rates to compute FO = AT/IT ≈ 10.60 and then selects conditions 5/10/15. However, the paper uses this only as a design-sizing heuristic, not as the evidence for the observed SA limit; Section 6 explicitly calls the 10.6 estimate a simulation-based upper bound and treats the observed 5–10 range as the more conservative result. The SAGAT, workload, and qualitative data were free to contradict the fan-out estimate, so this is not a fitted-input-called-prediction reduction. Self-citations (SafeSpect [48], Garcia in [31]) are used for design lineage, positioning, and adapted safety-first drone miniatures; they are not invoked as a theorem or as external proof of the central result, and no uniqueness theorem is imported. Two non-circularity validity concerns should be flagged for the authors: Appendix B.4 specifies Jaccard scoring over sets, while Table 3 includes 'How many...' count queries, so the reported SAGAT scores are not reproducible as written; and Section 6 concedes a small sample and simulation abstraction. These affect generalizability and evidential strength, not the circularity of the derivation. Score 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claims rest on the methodological assumptions of the evaluation (validity of SAGAT, representativeness of simulation) and on hand-tuned simulation parameters that shape safety-event workload. No new physical or conceptual entities are postulated; FleetScape is a software artifact. The fan-out calculation is the closest thing to a fitted quantity and it is openly reported.

free parameters (3)
  • Fan-out interaction-time estimates = IT_takeoff≈5 s, IT_replan≈50.94 s, IT_manual≈38.08 s
    Measured from three pilot studies in the same simulated system; used in Appendix C.1 to compute FO≈10.6, which guided choice of 5/10/15 conditions and is cited as an upper-bound estimate in Limitations.
  • Fan-out event rates = takeoff 1/526 s per drone, GPS error 0.4/420 s per drone, replan 0.4/420 s per drone
    Assumed per-drone rates in the 7-minute simulated mission; they determine the fan-out estimate and influence how much safety-monitoring workload pilots face.
  • Navigation uncertainty constants = δ=0.3 m, ℓmax=10, abnormal threshold=1.0 m, slew rate=1.0 m/s, update interval=0.5 s
    Hand-chosen parameters in Appendix A.2 for the GPS-loss model; they control the frequency and severity of safety events and therefore affect drone-status SA scores.
assumptions (4)
  • domain assumption SAGAT, SART, Bedford Workload Scale, and TAM are valid measures of SA, workload, and acceptance in this MR fleet setting.
    Section 4.1.3; the paper relies on established instruments without independent validation in this specific MR multi-drone context.
  • domain assumption The simulated inspection scenario with scripted GPS errors is a faithful proxy for real building-inspection fleet missions.
    Sections 3.6 and 6; the authors assume the simulated latency/risk environment generalizes to physical deployments, while acknowledging real-world complexities.
  • ad hoc to paper The GPS-loss random-walk model (Eq. 1–3) produces representative navigation uncertainty.
    Appendix A.2; ℓmax, δ, and slew parameters are hand-chosen, with no calibration to physical GPS data.
  • standard math Fan-out formula AT/IT predicts controller capacity for this interface.
    Appendix C.1, following Olsen & Wood [29]; the formula is standard, but the component times are measured on the same prototype.

how reviews work

0 comments
Cite this review

Pith. "Pith review of FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets." pith.science (2026). https://pith.science/paper/L3NBMS26

@misc{pith2026260726423,
  author       = {Pith},
  title        = {Pith review of: FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/L3NBMS26}},
  note         = {Machine review of arXiv:2607.26423}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2607.26423 by the authors.

Figure 1
Figure 1. Overview of FleetScape. (1) A MR interface featuring a 3D Sandtable that enables spatial interaction and supports [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Interface components of FleetScape. A. Fleet Status Board; B. Minimap; C. Drone Control Panel. Sandtable with D. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. During SAGAT pauses, the interface was hidden to prevent [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Technology Acceptance Model (TAM) questionnaire [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Examples of spatial layout. (a) A typical layout most [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Autonomous inspection flow for each drone: queue [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: The full decision tree diagram for the Bedford Work [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

52 extracted references · 3 canonical work pages

  1. [1]

    Abraham, Benjamin Burger, Chichi Christine, Luke Fraser, John M

    Ankit Agrawal, Sophia J. Abraham, Benjamin Burger, Chichi Christine, Luke Fraser, John M. Hoeksema, Sarah Hwang, Elizabeth Travnik, Shreya Kumar, Walter Scheirer, Jane Cleland-Huang, Michael Vierhauser, Ryan Bauer, and Steve Cox. 2020. The Next Generation of Human-Drone Partnerships: Co-Designing an Emergency Response System. InProceedings of the 2020 CHI...

    arXiv 2020

  2. [2]

    2015.The augmented reality sandtable (ARES)

    Charles R Amburn, Nathan L Vey, Michael W Boyce, and Jerry R Mize. 2015.The augmented reality sandtable (ARES). Technical Report ARL-SR-0340. U.S. Army Research Laboratory

    2015

  3. [3]

    Vanessa V Aubuchon, Kelley E Hashemi, R Jay Shively, and Jacob M Wishart

  4. [4]

    Lonni Besançon, Anders Ynnerman, Daniel F Keefe, Lingyun Yu, and Tobias Isenberg. 2021. The state of the art of spatial interfaces for 3D visualization. In Computer Graphics Forum, Vol. 40. Wiley Online Library, 293–326

    2021

  5. [5]

    Marios M Polycarpou, Xinhang Xu, et al

    Muqing Cao, Thien-Minh Nguyen, Shenghai Yuan, Andreas Anastasiou, Ange- los Zacharia, Savvas Papaioannou, Panayiotis Kolios, Christos G Panayiotou, UIST ’26, November 02–05, 2026, Detroit, MI, USA Xu et al. Marios M Polycarpou, Xinhang Xu, et al. 2025. Cooperative aerial robot inspec- tion challenge: A benchmark for heterogeneous multi-uav planning and le...

    arXiv 2026

  6. [6]

    Jessie YC Chen and Michael J Barnes. 2014. Human–agent teaming for multirobot control: A review of human factors issues.IEEE Transactions on Human-Machine Systems44, 1 (2014), 13–29

    2014

  7. [7]

    Linfeng Chen, Kazuki Takashima, Kazuyuki Fujita, and Yoshifumi Kitamura. 2021. Pinpointfly: An egocentric position-control drone interface using mobile ar. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–13

    2021

  8. [8]

    Kwangsu Cho, Minhee Cho, and Jongwoo Jeon. 2017. Fly a drone safely: Eval- uation of an embodied egocentric drone controller interface.Interacting with computers29, 3 (2017), 345–354

    2017

Show all 52 references
  1. [9]

    Mary L Cummings, Sylvain Bruni, S Mercier, and PJ Mitchell. 2007. Automation architecture for single operator, multiple UAV command and control.The Inter- national Command and Control Journal1, 2 (2007). http://www.dodccrp.org/ files/IC2J_v1n2_01_Cummings.pdf

    2007

  2. [10]

    Mary L Cummings, Jonathan P How, Andrew Whitten, and Olivier Toupet. 2011. The impact of human–automation collaboration in decentralized multiple un- manned vehicle control.Proc. IEEE100, 3 (2011), 660–671

    2011

  3. [11]

    M. L. Cummings and P. J. Mitchell. 2008. Predicting Controller Capacity in Supervisory Control of Multiple UAVs.IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans38, 2 (2008), 451–460. doi:10.1109/ TSMCA.2007.914757

    2008

  4. [12]

    Fred D Davis, Richard P Bagozzi, and Paul R Warshaw. 1989. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models.Management Science35, 8 (1989), 982–1003. https://doi.org/10.1287/mnsc.35.8.982

    1989 doi

  5. [13]

    Mica R Endsley. 1988. Situation awareness global assessment technique (SAGAT). InProceedings of the IEEE 1988 national aerospace and electronics conference. IEEE, 789–795

    1988

  6. [14]

    Mica R. Endsley. 1995. Toward a Theory of Situation Aware- ness in Dynamic Systems.Human Factors37, 1 (1995), 32–

    1995

  7. [15]

    Mica R Endsley. 2017. Direct measurement of situation awareness: Validity and use of SAGAT. InSituational awareness. Routledge, 129–156

    2017

  8. [16]

    Mica R Endsley. 2017. From here to autonomy: lessons learned from human– automation research.Human factors59, 1 (2017), 5–27

    2017

  9. [17]

    Mica R Endsley and Esin O Kiris. 1995. The out-of-the-loop performance problem and level of control in automation.Human Factors37, 2 (1995), 381–394. doi:10. 1518/001872095779064555

    1995

  10. [18]

    Scott A Green, Mark Billinghurst, XiaoQi Chen, and J Geoffrey Chase. 2008. Human-robot collaboration: A literature review and augmented reality approach in design.International journal of advanced robotic systems5, 1 (2008), 1

    2008

  11. [19]

    Ben Grindley, Katie J Parnell, Tom Cherrett, Jim Scanlan, and Katherine L Plant. 2024. Understanding the human factors challenge of handover be- tween levels of automation for uncrewed air systems: A systematic litera- ture review.Transportation Planning and Technology48, 6 (2...

    2024

  12. [20]

    Skov, and Timothy R

    Maria-Theresa Oanh Hoang, Niels van Berkel, Mikael B. Skov, and Timothy R. Merritt. 2023. Challenges and Requirements in Multi-Drone Interfaces. InEx- tended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany)(CHI EA ’23). Association f...

    2023

  13. [21]

    Amy Hocraffer and Chang S Nam. 2017. A meta-analysis of human-system inter- faces in unmanned aerial vehicle (UAV) swarm management.Applied ergonomics 58 (2017), 66–80

    2017

  14. [22]

    Zsolt Kira and Mitchell A. Potter. 2009. Exerting Human Control over Decentral- ized Robot Swarms. In2009 4th International Conference on Autonomous Robots and Agents. IEEE, 566–571. doi:10.1109/ICARA.2000.4803934

    2009

  15. [23]

    B. B. Kocer, H. Stedman, P. Kulik, I. Caves, N. Van Zalk, V. M. Pawar, and M. Kovac

  16. [24]

    Andreas Kolling, Phillip Walker, Nilanjan Chakraborty, Katia Sycara, and Michael Lewis. 2015. Human interaction with robot swarms: A survey.IEEE Transactions on Human-Machine Systems46, 1 (2015), 9–26

    2015

  17. [25]

    Nico Li, Stephen Cartwright, Aditya Shekhar Nittala, Ehud Sharlin, and Mario Costa Sousa. 2015. Flying frustum: A spatial interface for enhancing human-uav awareness. InProceedings of the 3rd International Conference on Human-Agent Interaction. 27–31

    2015

  18. [26]

    In2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

    Immersive View and Interface Design for Teleoperated Aerial Manipulation. In2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 4919–4926. doi:10.1109/IROS47612.2022.9981656

    2022

  19. [27]

    2026.City Pack - Modular and Tileable

    Mindravel Interactive. 2026.City Pack - Modular and Tileable. https://assetstore.unity.com/packages/3d/environments/city-pack-modular- and-tileable-83163 Unity Asset Store package

    2026

  20. [28]

    Suphakit Niwattanakul, Jatsada Singthongchai, Ekkachai Naenudorn, and Su- pachanun Wanapu. 2013. Using of Jaccard coefficient for keywords similarity. InProceedings of the international multiconference of engineers and computer scientists, Vol. 1. 380–384

    2013

  21. [29]

    Hing, Hasan Ayaz, Patricia A

    Justin Menda, James T. Hing, Hasan Ayaz, Patricia A. Shewokis, Kurtulus Izze- toglu, Banu Onaral, and Paul Oh. 2011. Optical Brain Imaging to Enhance UAV Operator Training, Evaluation, and Interface Development.Journal of Intelligent & Robotic Systems61 (2011), 423–443. doi:10...

    2011 doi

  22. [30]

    Ben Piper, Carlo Ratti, and Hiroshi Ishii. 2002. Illuminating clay: a 3-D tangible interface for landscape analysis. InProceedings of the SIGCHI conference on Human factors in computing systems. 355–362

    2002

  23. [31]

    Balita Heriniaina Rakotonarivo, Nicolas Drougard, Stéphane Conversy, and Jérémie Garcia. 2023. Cleared for safe take-off? improving the usability of mission preparation to mitigate the safety risks of drone operations. InProceed- ings of the 2023 CHI Conference on Human Factor...

    2023

  24. [32]

    Dan R Olsen Jr and Stephen Bart Wood. 2004. Fan-out: Measuring human control of multiple robots. InProceedings of the SIGCHI conference on Human factors in computing systems. 231–238

    2004

  25. [33]

    1990.A subjective rating scale for assessing pilot workload in flight: A decade of practical use

    Alan H Roscoe and Georges A Ellis. 1990.A subjective rating scale for assessing pilot workload in flight: A decade of practical use. Technical Report

    1990

  26. [34]

    Tareq Salem, Mihai Dragomir, and Eric Chatelet. 2024. Strategic integration of drone technology and digital twins for optimal construction project management. Applied Sciences14, 11 (2024), 4787

    2024

  27. [35]

    Olivares- Méndez, Jaime Del Cerro, and Antonio Barrientos

    Juan Jesús Roldán, Elena Peña Tapia, Andrés Martín-Barrio, Miguel A. Olivares- Méndez, Jaime Del Cerro, and Antonio Barrientos. 2017. Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction.Sensors17, 8 (2017), 1720. doi:10.33...

    2017 doi

  28. [36]

    Roger N Shepard and Jacqueline Metzler. 1971. Mental rotation of three- dimensional objects.Science171, 3972 (1971), 701–703

    1971

  29. [37]

    Singapore Polytechnic. 2025. CAAS Approved Unmanned Aircraft Pilot Licence (UAPL) Flying Training & Assessment (Helicopter). https://www.sp.edu.sg/pace/courses/all-courses/course-details/caas- approved-unmanned-aircraft-pilot-licence-(uapl)-flying-training-assessment- (helicop...

    2025

  30. [38]

    Nadine B Sarter and David D Woods. 1995. How in the world did we ever get into that mode? Mode error and awareness in supervisory control.Human Factors37, 1 (1995), 5–19. doi:10.1518/001872095779049516

    1995 doi

  31. [39]

    Yi Tan, Youde Zheng, Wen Yi, Shenghan Li, Penglu Chen, Ruying Cai, and Dianwei Song. 2025. Intelligent inspection of building exterior walls using UAV and mixed reality based on man-machine-environment system engineering.Automation in Construction177 (2025), 106344

    2025

  32. [40]

    Richard M Taylor. 2017. Situational awareness rating technique (SART): The development of a tool for aircrew systems design. InSituational awareness. Routledge, 111–128

    2017

  33. [41]

    Kushagra Srivastava, Dhruv Patel, Aditya Kumar Jha, Mohhit Kumar Jha, Jaskirat Singh, Ravi Kiran Sarvadevabhatla, Pradeep Kumar Ramancharla, Harikumar Kandath, and K Madhava Krishna. 2022. UAV-Based Visual Remote Sensing for Automated Building Inspection. InEuropean Conference...

    2022

  34. [42]

    Samuel Truman, Nicolas Rapp, Daniel Roth, and Sebastian von Mammen. 2018. Rethinking real-time strategy games for virtual reality. InProceedings of the 13th International Conference on the Foundations of Digital Games. 1–6

    2018

  35. [43]

    2018.Unity Drone Simulator

    UAVs-at-Berkeley. 2018.Unity Drone Simulator. https://github.com/UAVs-at- Berkeley/UnityDroneSim GitHub repository; no official release published

    2018

  36. [44]

    Melanie Tory, Arthur E Kirkpatrick, M Stella Atkins, and Torsten Moller. 2005. Visualization task performance with 2D, 3D, and combination displays.IEEE transactions on visualization and computer graphics12, 1 (2005), 2–13

    2005

  37. [45]

    Phillip Walker, Joshua Hamell, Christopher A Miller, Jack Ladwig, Helen Wauck, and Peter K Keller. 2024. Immersive interaction interface (I3): a virtual reality swarm control interface.IEEE Transactions on Field Robotics1 (2024), 424–445

    2024

  38. [46]

    Michelle Q Wang Baldonado, Allison Woodruff, and Allan Kuchinsky. 2000. Guidelines for using multiple views in information visualization. InProceedings of the working conference on Advanced visual interfaces. 110–119

    2000

  39. [47]

    Victor Victor, Tania Krisanty, Matthew McGinity, Stefan Gumhold, and Uwe Aßmann. 2025. IGUANA: Immersive Guidance, Navigation, and Control for Consumer UAV. InProceedings of the 2025 31st ACM Symposium on Virtual Reality Software and Technology. 1–11. doi:10.1145/3756884.3766033

    2025

  40. [48]

    Peisen Xu, Jérémie Garcia, Wei Tsang Ooi, and Christophe Jouffrais. 2025. Safe- Spect: Safety-First Augmented Reality Heads-up Display for Drone Inspections. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Mac...

    2025

  41. [49]

    In Transit

    Eddie Q Yan, Jeff Huang, and Gifford K Cheung. 2015. Masters of control: Be- havioral patterns of simultaneous unit group manipulation in starcraft 2. In Proceedings of the 33rd annual ACM conference on human factors in computing systems. 3711–3720. FleetScape: A Mixed Reality...

    2015

  42. [50]

    Simon Whalley and Yan-yuan Tay. 2022. Buzzing City: Developing UAS in Urban Singapore. https://aais.org.sg/wp-content/uploads/2022/04/ASV15N1-Feature- Buzzing-City.pdf Association of Aerospace Industries (Singapore)

    2022

  43. [64]

    arXiv:https://doi.org/10.1518/001872095779049543 doi:10.1518/ 001872095779049543

    doi

  44. [2022]

    In AIAA A viation 2022 Forum

    Multi-vehicle (m: N) operations in the NAS-NASA’s research plans. In AIAA A viation 2022 Forum. 3758

    2022

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.