Quantifying Performance Degradation due to the Human-Machine Interface of Telemanipulators

Abstract

As a first step in evaluating operator interfaces to telerobots, task performance data were collected from humans hands-on, with and without the subjects wearing a unilateral exoskeletal device. These baseline studies show the degradation in task performance caused solely by the exoskeleton, exclusive of any slave robotic system. This subsystems-level approach to performance measurement is motivated by the increasing modularity among robotic designs, and the need to quantify the performance degradation caused by each subsystem. The experiments described in this paper show that the unilateral exoskeleton decreases the human's available information capacity by approximately 36 percent, depending on the subject and the difficulty of the task. This decrease in available information capacity is similar when viewing the peg-into-hole task using the one dimension of Fitts' Law or when breaking this task into the two tasks of ballistic motion and accurate positioning. Future work involves evaluating the teleoperated performance of these tasks, plus similar hands-on and teleoperated experiments with the operator wearing a bilateral exoskeleton. This matrix of experiments can be repeated for other telerobotic interfaces to understand the benefits and limitations of the variety of available human machine interfaces.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1990
Accession Number
ADA232814

Entities

People

  • Daniel W. Repperger
  • Steven J. Remis

Organizations

  • Armstrong Laboratory

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Air Force Facilities
  • Biomedical Research
  • Channel Capacity
  • Control Systems
  • Data Analysis
  • Exoskeleton
  • Human Supervisory Control
  • Human-Machine Interfaces
  • Information Processing
  • Machines
  • Reaction Time
  • Robots
  • Standards
  • Task Performance And Analysis
  • Two Dimensional

Readers

  • Instructional Design and Training Evaluation.
  • Robotics and Automation.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Autonomy
  • Autonomy - Human-Robot Interaction