Operator Performance in Multi Maritime Unmanned Air Vehicle Control

Abstract

Under contract by the Royal Netherlands Navy, an exploratory study was conducted concerning the man-machine interface and task characteristics for controlling Maritime Unmanned Air Vehicles (MUAVs). This report describes a simulator experiment investigating how effective operators can track a moving target in a multi MUAV supervisory control task, under different verbal/cognitive workload conditions. Results of the experiment show that moderate and even high workload conditions did not affect the mean target coverage of the sensor image. This was about 100% at image update frequencies of 4 Hz and more, decreasing to about 60% at 1 Hz update frequency. This may be explained by the fact that, in the latter case, the tracking task became more discontinuous, which demanded more attention for anticipation. Since the subjects were instructed to give primary attention to the verbal/cognitive task, this extra attention was not fully available, in particular not during high workload conditions. This resulted in an increase of the mean viewing (tracking) error. It is suggested to focus further research on ways to improve operator performance and awareness at low image update rates, for instance by integrating synthetic information on orientation and MUAV status into the sensor image ("synthetic image augmentation").

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Document Details

Document Type
Technical Report
Publication Date
Dec 14, 1995
Accession Number
ADA309623

Entities

People

  • L. Van Breda

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Aircrafts
  • Cognitive Workload
  • Control Systems
  • Frequency
  • Human Factors Engineering
  • Human Systems Integration
  • Human-Machine Interfaces
  • Information Processing
  • Models
  • Moving Targets
  • Pattern Recognition
  • Recognition
  • Simulators
  • Supervisory Control
  • Task Performance And Analysis
  • Unmanned Aerial Vehicles
  • User Interface

Readers

  • Aerial Unmanned Vehicle Swarm Micro Periodontal Dentistry.
  • Aviation Science / Aeronautics.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • Autonomy
  • Autonomy - Human-Robot Interaction