DocumentCode
42790
Title
Dynamic Operator Overload: A Model for Predicting Workload During Supervisory Control
Author
Breslow, Leonard A. ; Gartenberg, Daniel ; McCurry, J. Malcolm ; Trafton, J. Gregory
Author_Institution
Naval Res. Lab., Washington, DC, USA
Volume
44
Issue
1
fYear
2014
fDate
Feb. 2014
Firstpage
30
Lastpage
40
Abstract
Crandall and Cummings & Mitchell introduced fan-out as a measure of the maximum number of robots a single human operator can supervise in a given single-human-multiple-robot system. Fan-out is based on the time constraints imposed by limitations of the robots and of the supervisor, e.g., limitations in attention. Adapting their work, we introduced a dynamic model of operator overload that predicts failures in supervisory control in real time, based on fluctuations in time constraints and in the supervisor´s allocation of attention, as assessed by eye fixations. Operator overload was assessed by damage incurred by unmanned aerial vehicles when they traversed hazard areas. The model generalized well to variants of the baseline task. We then incorporated the model into the system where it predicted in real time, when an operator would fail to prevent vehicle damage and alerted the operator to the threat at those times. These model-based adaptive cues reduced the damage rate by one-half relative to a control condition with no cues.
Keywords
autonomous aerial vehicles; cognition; control engineering computing; human-robot interaction; multi-robot systems; dynamic model; dynamic operator overload; eye fixations; failure prediction; model-based adaptive cue; single human operator; single-human-multiple-robot system; supervisor attention allocation; supervisory control; time constraints; traversed hazard areas; unmanned aerial vehicles; vehicle damage; workload prediction; Payloads; Predictive models; Robots; Supervisory control; Vehicle dynamics; Vehicles; Visualization; Cognition; human-robot interaction; multi-robot systems; predictive models; unmanned aerial vehicles;
fLanguage
English
Journal_Title
Human-Machine Systems, IEEE Transactions on
Publisher
ieee
ISSN
2168-2291
Type
jour
DOI
10.1109/TSMC.2013.2293317
Filename
6697874
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