Omnisurface: Common reality for intuitive human-robot collaboration

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Authors
Zaman, Akhlak Uz
Li, Hui
Yan, Fujian
Zhang, Yinlong
He, Hongsheng
Advisors
Issue Date
2025-02-07
Type
Conference paper
Keywords
Common reality , Distortion correction , Human-robot teaming , RGB-D sensor
Research Projects
Organizational Units
Journal Issue
Citation
Zaman, A.U., Li, H., Yan, F., Zhang, Y., He, H. (2025). Omnisurface: Common Reality for Intuitive Human-Robot Collaboration. In: Li, H., et al. Social Robotics. ICSR + InnoBiz 2024. Lecture Notes in Computer Science(), vol 15170. Springer, Singapore. https://doi.org/10.1007/978-981-96-1151-5_34
Abstract

Effective communication and information projection are essential for human-robot teaming. The projection of images on non-planar surfaces using a conventional projector is challenging due to the inherent problem of distortion. The projection distortion occurs due to the variations in depth across the surface of the teaming workspace. As a result, the projected image, information, or symbols lose their original shape and create confusion during human-robot teaming. In this paper, we presented an innovative approach to perform distortion-free projections in the teaming workspace. A pre-warped image is constructed based on the surface geometry that the projector displays and accurately replicates the original projection image. Beyond the technical achievement, this research highlights the social acceptance of improved spatial augmented reality in human-robot teams. It fosters better teamwork, trust, and efficiency by enabling more intuitive and reliable interactions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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Publisher
Springer Science and Business Media Deutschland GmbH
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Book Title
Series
16th International Conference on Social Robotics, ICSR + InnoBiz 2024
25 September 2024 through 28 September 2024
Shenzhen
326439
PubMed ID
ISSN
03029743
EISSN