RoboVisAR : Immersive Authoring of Condition-based AR Robot Visualisations
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We introduce RoboVisAR, an immersive augmented reality (AR) authoring tool for in-situ robot visualisations. AR robot visualisations, such as the robot's movement path, status, and safety zones, have been shown to benefit human-robot collaboration. However, their creation requires extensive skills in both robotics and AR programming. To address this, RoboVisAR allows users to create custom AR robot visualisations without programming. By recording an example robot behaviour, users can design, combine, and test visualisations in-situ within a mixed reality environment. RoboVisAR currently supports six types of visualisations (Path, Point of Interest, Safety Zone, Robot State, Message, Force/Torque) and four types of conditions for when they are displayed (Robot State, Proximity, Box, Force/Torque). With this tool, users can easily present different visualisations on demand and make them context-aware to avoid visual clutter. An expert user study with three participants suggests that users appreciate the customizability of the visualisations, which could easily be authored in less than ten minutes.
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SKOVHUS LUNDING, Rasmus, Mille SKOVHUS LUNDING, Tiare FEUCHTNER, Marianne GRAVES PETERSEN, Kaj GRØNBÆK, Ryo SUZUKI, 2024. RoboVisAR : Immersive Authoring of Condition-based AR Robot Visualisations. HRI '24 : ACM/IEEE International Conference on Human-Robot Interaction. Boulder, CO, USA, 11. März 2024 - 15. März 2024. In: GROLLMAN, Dan, ed., Elizabeth BROADBENT, ed.. HRI '24 : Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. New York, NY: ACM, 2024, pp. 462-471. ISBN 979-8-4007-0322-5. Available under: doi: 10.1145/3610977.3634972BibTex
@inproceedings{SkovhusLunding2024-03-11RoboV-69712, year={2024}, doi={10.1145/3610977.3634972}, title={RoboVisAR : Immersive Authoring of Condition-based AR Robot Visualisations}, isbn={979-8-4007-0322-5}, publisher={ACM}, address={New York, NY}, booktitle={HRI '24 : Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction}, pages={462--471}, editor={Grollman, Dan and Broadbent, Elizabeth}, author={Skovhus Lunding, Rasmus and Skovhus Lunding, Mille and Feuchtner, Tiare and Graves Petersen, Marianne and Grønbæk, Kaj and Suzuki, Ryo} }
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