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Conference proceeding
Fan-Out Revisited: The Impact of the Human Element on Scalability of Human Multi-Robot Teams
2025 IEEE International Conference on Robotics and Automation (ICRA) DOI: 10.1109/ICRA55743.2025 19-23 May 2025, pp.4280-4286
IEEE International Conference on Robotics and Automation (ICRA 2025) (Atlanta, Georgia, USA, 05/18/2025–05/22/2025)
09/02/2025
Web of Science ID: WOS:001582497400383
Abstract
This paper introduces a novel fan-out model that improves accuracy over previous models. The commonly used models rely on neglect time, the time an agent operates independently, which confounds both human and robot abilities. The proposed model separates neglect time into two functionally distinct concepts: the time a robot can operate self-sufficiently, and the time a human estimates the robot can do so. Previous research indicates fan-out is often overestimated. This work explains why robot ability provides an upper bound to fan-out, but that actual achieved fan-out is influenced by both the human and robot abilities. We conduct a study to validate this new model and show improved performance over the two most common fan-out models. The results show that both previous models overestimate as predicted. Using the new fan-out model, we show that as the difference between human estimation and robot abilities grows, the actual fan-out will fall further from the upper bound potential fan-out. By including assessments of both the robotic and human elements, the new model provides a more nuanced understanding of the dynamics at play and the factors involved in scaling Human Multi-Robot Teams.
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Details
- Title
- Fan-Out Revisited
- Publication Details
- 2025 IEEE International Conference on Robotics and Automation (ICRA) DOI: 10.1109/ICRA55743.2025 19-23 May 2025, pp.4280-4286
- Resource Type
- Conference proceeding
- Conference
- IEEE International Conference on Robotics and Automation (ICRA 2025) (Atlanta, Georgia, USA, 05/18/2025–05/22/2025)
- Publisher
- IEEE; IEEE Explore
- Number of pages
- 7
- Copyright
- © 2025, IEEE
- Identifiers
- WOS:001582497400383; 99381474081806600
- Academic Unit
- Intelligent Systems and Robotics
- Language
- English