The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Effectiveness of RoboLeader for Dynamic Re-Tasking in an Urban Environment
Abstract
RoboLeader is an intelligent agent that has the capabilities of coordinating a team of ground robots and revising route plans for the robots based on battlefield intelligence. Specifically, RoboLeader can support dynamic re-tasking based on battlefield developments as well as coordination between aerial and ground robots in pursuit of moving targets. In the current study, we manipulated the level of automation for RoboLeader as well as the presence of a visualization tool (which informed the participants about their target entrapment performance) in the RoboLeader user interface. Results showed that RoboLeader (Fully Automated condition) was more effective in encapsulating the moving targets than were the human operators (when they were either without assistance from RoboLeader or when they were partially assisted by RoboLeader). Participants successfully encapsulated the moving targets only 63% of the time in the Manual condition but 89% of the time when they were assisted by RoboLeader. Those participants who play video games frequently demonstrated significantly better encapsulation performance than did infrequent gamers; they also had better situation awareness of the mission environment. Visualization had little effect on participants’ performance. Finally, participants reported significantly higher workload when they were in the Manual condition than when they were assisted by RoboLeader.
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“(III) Experience (both quantity and quality) with computers that are similar or that utilize autonomous agents is associated with higher trust, performance, and multitasking performance.”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Chen, Barnes, Quinn, et al. (2011)
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“Attentional control and prior video game experience were positively related to human–autonomy multitasking performance (Chen, Barnes, Qu, et al., 2011, Chen, Barnes, Quinn, et al., 2011).”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Chen, Barnes, Quinn, et al., 2011
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“Attentional control and prior video game experience were positively related to human–autonomy multitasking performance (Chen, Barnes, Qu, et al., 2011, Chen, Barnes, Quinn, et al., 2011).”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Chen, Barnes, Quinn, et al., 2011
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Cites
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Human Performance Issues and User Interface Design for Teleoperated Robots
Chen, J. Y. C., Haas, E. C., & Barnes, M. J. (2007) · IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.905819 · Cited by 646
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Cited by
Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature
O’Neill, T., McNeese, N., Barron, A., & Schelble, B. (2022) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720820960865 · Cited by 695
Metadata
- Authors
- Chen, J. Y. C., Barnes, M. J., Quinn, S. A., & Plew, W.
- Affiliations
- U.S. Army Research Laboratory – Human Research & Engineering Directorate
- University of Central Florida – Institute for Simulation & Training
- Venue
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Publisher
- SAGE Publications
- Published
- 2011-09
- Volume
- 55
- Issue
- 1
- Pages
- 1501-1505
- ISSN
- 1071-1813
- 2169-5067
- Cited by
- 13
- References
- 2
- Language
- en
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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