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Enhancing the effectiveness of Human-Robot teaming with a closed-loop system

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Date Issued:
2015
Abstract/Description:
With the development in robotics and the increasing deployment of robots, human-robot teams are set to be a mainstay in the future. However, our understanding of the effectiveness and impact of this new form of teaming is limited. Previous experience with technology and automa-tion has shown that technological aids do not always result in the intended consequences of im-proved performance and alleviation of workload and stress. No doubt a large part of this is due to the fact that the relationships among taskload, workload and performance are complex as hu-man operators interact dynamically with tasks and technology. Measures of workload are also varied and differentially sensitive. There is also the added challenge posed by multi-tasking envi-ronments which typify most real-world situations. Given all this, efforts in designing technologi-cal aids, such as an adaptive robot aid in the context of human-robot teaming, would require a workload model that reflects the intricate relationship between taskload and the individual opera-tor's experience of workload. Such a model can then be used to drive a closed-loop system on which adaptive robot aiding can be based. The present research sought to investigate the effec-tiveness of a closed-loop system, based on a model of workload, in enhancing performance in a simulated military mission involving a human-robot team. Results showed that adaptive robot aid driven by workload needs as assessed by physiological measures resulted in greater improve-ments in performance compared to robot aid that was imposed by the system.
Title: Enhancing the effectiveness of Human-Robot teaming with a closed-loop system.
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Name(s): Teo, Grace, Author
Szalma, James, Committee Chair
Jentsch, Florian, Committee Member
Hancock, Peter, Committee Member
Matthews, Gerald, Committee Member
Reinerman, Lauren, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2015
Publisher: University of Central Florida
Language(s): English
Abstract/Description: With the development in robotics and the increasing deployment of robots, human-robot teams are set to be a mainstay in the future. However, our understanding of the effectiveness and impact of this new form of teaming is limited. Previous experience with technology and automa-tion has shown that technological aids do not always result in the intended consequences of im-proved performance and alleviation of workload and stress. No doubt a large part of this is due to the fact that the relationships among taskload, workload and performance are complex as hu-man operators interact dynamically with tasks and technology. Measures of workload are also varied and differentially sensitive. There is also the added challenge posed by multi-tasking envi-ronments which typify most real-world situations. Given all this, efforts in designing technologi-cal aids, such as an adaptive robot aid in the context of human-robot teaming, would require a workload model that reflects the intricate relationship between taskload and the individual opera-tor's experience of workload. Such a model can then be used to drive a closed-loop system on which adaptive robot aiding can be based. The present research sought to investigate the effec-tiveness of a closed-loop system, based on a model of workload, in enhancing performance in a simulated military mission involving a human-robot team. Results showed that adaptive robot aid driven by workload needs as assessed by physiological measures resulted in greater improve-ments in performance compared to robot aid that was imposed by the system.
Identifier: CFE0006403 (IID), ucf:51485 (fedora)
Note(s): 2015-12-01
Ph.D.
Sciences, Psychology
Doctoral
This record was generated from author submitted information.
Subject(s): workload -- physiological measures -- closed-loop -- adaptive aiding -- human-robot
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0006403
Restrictions on Access: campus 2021-06-15
Host Institution: UCF

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