Workload, awareness and automation in multiple-robot supervision

Using a single human to supervise multiple robots helps to address manpower constraints while deriving the benefits of multiple-robot deployment such as efficiency and improved system reliability. However, it can also induce high supervisor workload and diminish situation awareness. This article exp...

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Bibliographic Details
Main Authors: Wong, Choon Yue, Seet, Gerald
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2018
Subjects:
Online Access:https://hdl.handle.net/10356/88641
http://hdl.handle.net/10220/45867
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Institution: Nanyang Technological University
Language: English
Description
Summary:Using a single human to supervise multiple robots helps to address manpower constraints while deriving the benefits of multiple-robot deployment such as efficiency and improved system reliability. However, it can also induce high supervisor workload and diminish situation awareness. This article explains workload and situation awareness. It reviews various studies related to human–robot systems to illustrate the effects and causes of workload and diminished situation awareness in such systems. The article reviews and discusses the application of automation to address workload and situation-awareness concerns. It also presents the issues that the use of automation can cause, highlighting that automation must be applied with care. The article advocates the consideration of sliding autonomy for four aspects of task execution: information acquisition, information analysis, decision selection and action implementation. It additionally encourages the appreciation for recognized methods of applying and triggering automation. The hope is for robots to be equipped with adjustable autonomy across multiple aspects of task performance to create robotic systems with highly flexible autonomy configurations. While robots from such systems may have the flexibility to deal with numerous situation requirements, the research challenge is understanding if and how such flexibility will affect human workload.