Dynamic Operator Overload Estimation during Supervisory Control of Multiple UAVs

Abstract

Crandall et al. 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, 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, assessed by eye fixations. Operator overload was assessed by damage incurred by vehicles when they traversed hazard areas. The model generalized well to different tasks. 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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2014
Accession Number
ADA619173

Entities

People

  • Daniel Gartenberg
  • J. Gregory Trafton
  • J. M. Mccurry
  • Leonard A. Breslow

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Aircrafts
  • Cognition
  • Cognitive Systems Engineering
  • Control Systems
  • Data Analysis
  • Data Sets
  • Eye Movements
  • Human-Machine Interaction
  • Human-Machine Systems
  • Overload
  • Predictive Modeling
  • Probability
  • Psychology
  • Simulations
  • Supervisors
  • Supervisory Control
  • Unmanned Aerial Vehicles

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Human-Robot Interaction