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1. Collaborative multi-robot multi-human teams in search and rescue.

   Williams, R. (April 2020, Proceedings of the International ISCRAM Conference)

   Robots such as unmanned aerial vehicles (UAVs) deployed for search and rescue (SAR) can explore areas where human searchers cannot easily go and gather information on scales that can transform SAR strategy. Multi-UAV teams therefore have the potential to transform SAR by augmenting the capabilities of human teams and providing information that would otherwise be inaccessible. Our research aims to develop new theory and technologies for field deploying autonomous UAVs and managing multi-UAV teams working in concert with multi-human teams for SAR. Specifically, in this paper we summarize our work in progress towards these goals, including: (1) a multi-UAV search path planner that adapts to human behavior; (2) an in-field distributed computing prototype that supports multi-UAV computation and communication; (3) behavioral modeling that fields spatially localized predictions of lost person location; and (4) an interface between human searchers and UAVs that facilitates human-UAV interaction over a wide range of autonomy. 

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2. How Are You Feeling?: Characterization and Analysis of Human Factors in Multi-Human Multi-Robot Collaborative Tasks

   Modery, Garrett; Zhu, Michelle; Tuininga, Amy; Wang, Weitian (September 2025, IEEE)
3. ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility

   https://doi.org/10.1371/journal.pone.0255259  

   Li, Zhenlong; Huang, Xiao; Hu, Tao; Ning, Huan; Ye, Xinyue; Huang, Binghu; Li, Xiaoming (August 2021, PLOS ONE)

   Yang, Chaowei (Ed.)

   In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics. 

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4. TIP: A Trust Inference and Propagation Model in Multi-Human Multi-Robot Teams

   https://doi.org/10.1145/3568294.3580164  

   Guo, Yaohui; Yang, X. Jessie; Shi, Cong (January 2023, HRI '23: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction)

   Trust has been identified as a central factor for effective human-robot teaming. Existing literature on trust modeling predominantly focuses on dyadic human-autonomy teams where one human agent interacts with one robot. There is little, if not no, research on trust modeling in teams consisting of multiple human agents and multiple robotic agents. To fill this research gap, we present the trust inference and propagation (TIP) model for trust modeling in multi-human multi-robot teams. We assert that in a multi-human multi-robot team, there exist two types of experiences that any human agent has with any robot: direct and indirect experiences. The TIP model presents a novel mathematical framework that explicitly accounts for both types of experiences. To evaluate the model, we conducted a human-subject experiment with 15 pairs of participants (N=30). Each pair performed a search and detection task with two drones. Results show that our TIP model successfully captured the underlying trust dynamics and significantly outperformed a baseline model. To the best of our knowledge, the TIP model is the first mathematical framework for computational trust modeling in multi-human multi-robot teams. 

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5. Human adaptation to latency in teleoperated multi-robot human-agent search and rescue teams

   https://doi.org/10.1016/j.autcon.2018.12.012  

   Khasawneh, Amro; Rogers, Hunter; Bertrand, Jeffery; Madathil, Kapil Chalil; Gramopadhye, Anand (March 2019, Automation in Construction)