Concepts for Improved Automated Laboratory Productivity.

Abstract

The use of automated laboratory techniques is rapidly increasing. Significant changes are occurring both in how tasks are accomplished and in which tasks are practical and cost effective. While the automation of a given task does not inherently dictate the use of some form of computer, the greater system flexibility achieved through software control, coupled with the recent drastic reduction in computer hardware costs, have already made this approach to automation extremely popular. The vast proliferation of computational hardware does not solve all of the problems in laboratory automation--FAR FROM IT. Two major problem areas arise, development of suitable function systems to conduct the desired chemistry and development of the proper software. Today in many cases workers have resorted to mimicking human manipulation of samples through the use of robotics. While this approach is viable for some situations, it is far far from optimal for many other applications. Laboratory automation today often involves the use of instruments designed to perform a specific task (i.e., sample preparation and analysis) on a high work load. However, there is a trend toward increasing flexibility through multi-task capability.

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Document Details

Document Type
Technical Report
Publication Date
May 26, 1987
Accession Number
ADA183215

Entities

People

  • M. Bonner Denton

Organizations

  • University of Arizona

Tags

Communities of Interest

  • Autonomy
  • Engineered Resilient Systems
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Atomic Spectroscopy
  • Automation
  • Chemistry
  • Computers
  • Control Systems
  • Detection
  • Detectors
  • Engineering
  • Focal Planes
  • Geometry
  • Military Research
  • Photographic Emulsions
  • Photomultiplier Tubes
  • Productivity
  • Quantum Efficiency
  • Spectra
  • Spectroscopy

Fields of Study

  • Computer science

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Economics
  • Neural Network Machine Learning.

Technology Areas

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