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Investigating the Role of Uncertainty in Scalability of Human Multi-Robot Teams
Conference proceeding   Peer reviewed

Investigating the Role of Uncertainty in Scalability of Human Multi-Robot Teams

Lawrence Dale Perkins, Hakki Erhan Sevil and Matthew Johnson
2025 34th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp.2155-2161
IEEE RO-MAN
IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2025) (Eindhoven, Netherlands, 08/24/2025–08/28/2025)
11/03/2025
DOI: https://doi.org/10.1109/RO-MAN63969.2025.11217559
Web of Science ID: WOS:001672967200291

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Abstract

Scalability of human multi-robot teams is quickly becoming a crucial area of research as autonomous systems become more capable and sophisticated. A key research challenge is developing predictive measures of scalability, such as fan-out. This paper presents the results from a study that confirms the improved accuracy of a novel fan-out model over two previous models. It utilizes a new test domain to assess scalability and investigate the role of uncertainty through a variety of complexities driven by environmental factors, robot behaviors, and human-robot interactions. Our analysis highlights potential enhancements to optimize model accuracy across all the models. Finally, we show that when calibrating for measurement error, the new model is bounded, which sets it apart from previous models that are unbounded. The new model provides a more nuanced understanding of the dynamics at play and the factors involved in scaling Human Multi-Robot Teams under uncertainty.

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