FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets
- URL: http://arxiv.org/abs/2607.26423v1
- Date: Wed, 29 Jul 2026 03:09:10 GMT
- Title: FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets
- Abstract summary: We present FleetScape, a Mixed Reality (MR) sandtable system that externalizes layered real-time mission, safety, and environmental data.<n>We conduct a user study with six experienced drone pilots managing fleets of up to 15 drones.<n>Our findings show that FleetScape supports situational awareness through layered spatial representations and clarifies control mode transitions.
- Score: 7.75170515799838
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Flying Together: Human-Guided Immersive Shared Control for Aerial Robot Teams in Unknown Environments [12.913935671942774]
We present a Virtual Reality (VR)-based shared control framework for teams of drones operating in constrained and unknown environments.<n>A novel, user-guided motion-primitive-based planner computes continuous, collision-free trajectories while continuously integrating operator input.<n>The system supports mixed-reality operations with both physical and simulated drones, and implements a bilateral VR-based interface.
arXiv Detail & Related papers (2026-05-20T19:37:46Z) - MARS-Dragonfly: Agile and Robust Flight Control of Modular Aerial Robot Systems [21.471271822760276]
Modular Aerial Robot Systems (MARS) comprise multiple drone units with reconfigurable connected formations.<n>MARS rely on simplified quasi-static models and rule-based allocation, which generate discontinuous and unbounded motor commands.<n>We develop a compact mechanical system that enables passive docking, detection-free passive locking, and magnetic-assisted separation.
arXiv Detail & Related papers (2026-04-07T06:43:08Z) - Pro-HOI: Perceptive Root-guided Humanoid-Object Interaction [57.252599851031505]
We introduce Perceptive Root-guided Humanoid-Object Interaction, Pro-HOI.<n>Pro-HOI is a generalizable framework for robust humanoid loco-manipulation.<n> Empirical validation on a Unitree G1 robot demonstrates that Pro-HOI significantly outperforms baselines in generalization and robustness.
arXiv Detail & Related papers (2026-03-01T14:19:59Z) - Decentralized Multi-Robot Obstacle Detection and Tracking in a Maritime Scenario [1.1086440815804226]
This paper presents a decentralized multi-robot framework to detect and track floating containers using multiple UAVs cooperating with an autonomous surface vessel.<n>Each UAV runs a YOLOv8 detector augmented with stereo disparity and maintains per-target EKF tracks with uncertainty-aware data association.<n>An information-driven allocator assigns targets and selects UAV hover viewpoints by trading expected uncertainty reduction in travel effort and safety separation.
arXiv Detail & Related papers (2026-02-12T14:42:17Z) - HetroD: A High-Fidelity Drone Dataset and Benchmark for Autonomous Driving in Heterogeneous Traffic [49.31491001465465]
HetroD is a dataset and benchmark for developing autonomous driving systems in heterogeneous environments.<n>HetroD targets the critical challenge of navi- gating real-world heterogeneous traffic dominated by vulner- able road users (VRUs)
arXiv Detail & Related papers (2026-02-03T12:12:47Z) - μDopplerTag: CNN-Based Drone Recognition via Cooperative Micro-Doppler Tagging [0.0]
Current detection and classification technologies, including cameras, LiDAR, and conventional radar systems, often struggle to reliably identify and differentiate drones.<n>We propose a novel drone classification method based on artificial micro-Doppler signatures encoded by resonant electromagnetic stickers attached to drone blades.<n>We develop a tailored convolutional neural network (CNN) capable of processing raw radar signals, achieving high classification accuracy.
arXiv Detail & Related papers (2026-01-12T22:23:57Z) - Explainable AI-Enhanced Supervisory Control for Robust Multi-Agent Robotic Systems [0.0]
We present an explainable AI-enhanced supervisory control framework for multi-agent robotics.<n>We validated the approach in two contrasting domains, spacecraft formation flying and autonomous underwater vehicles.
arXiv Detail & Related papers (2025-09-18T23:59:13Z) - ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy [51.56484100374058]
ASTREA is the first agentic system executed on flight-heritage hardware for autonomous spacecraft operations.<n>We integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms.
arXiv Detail & Related papers (2025-09-16T08:52:13Z) - Multi-model fusion for Aerial Vision and Dialog Navigation based on
human attention aids [69.98258892165767]
We present an aerial navigation task for the 2023 ICCV Conversation History.
We propose an effective method of fusion training of Human Attention Aided Transformer model (HAA-Transformer) and Human Attention Aided LSTM (HAA-LSTM) models.
arXiv Detail & Related papers (2023-08-27T10:32:52Z) - Towards Cooperative Flight Control Using Visual-Attention [61.99121057062421]
We propose a vision-based air-guardian system to enable parallel autonomy between a pilot and a control system.
Our attention-based air-guardian system can balance the trade-off between its level of involvement in the flight and the pilot's expertise and attention.
arXiv Detail & Related papers (2022-12-21T15:31:47Z) - TransVisDrone: Spatio-Temporal Transformer for Vision-based
Drone-to-Drone Detection in Aerial Videos [57.92385818430939]
Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones.
Existing methods are computationally costly, follow non-end-to-end optimization, and have complex multi-stage pipelines, making them less suitable for real-time deployment on edge devices.
We propose a simple yet effective framework, itTransVisDrone, that provides an end-to-end solution with higher computational efficiency.
arXiv Detail & Related papers (2022-10-16T03:05:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.