• 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