Autonomous Neural Network Controllers for Adaptive Material Handling

Abstract

For robots to be more useful in flexible manufacturing and service applications, the controllers must be able to handle more variable environments. On at least two levels, conventional methods in robot control have problems dealing with high variability. At the movement level, conventional dynamic control formulations cannot deal effectively with the highly variable dynamic inertial interactions between multijointed robots and payloads. At the task level, the initial and final positions for materials to be moved may change slightly but unexpectedly. We have developed autonomous neural network controllers that learn from their own experience to deal with environmental variability at these levels.

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Document Details

Document Type
Technical Report
Publication Date
Jul 30, 1993
Accession Number
ADA268908

Entities

People

  • James Kottas
  • Michael Kuperstein

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Accuracy
  • Assembly Lines
  • Calibration
  • Control Systems
  • Demonstrations
  • Environment
  • Errors
  • Feedback
  • Joints
  • Joints (Anatomy)
  • Manufacturing
  • Materials Handling
  • Measurement
  • Neural Networks
  • Orientation (Direction)
  • Product Development
  • Video Tapes

Readers

  • Robotics and Automation.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks
  • Autonomy
  • Autonomy - Autonomous System Control