Picking Parts Out of a Bin,

Abstract

One of the remaining obstacles to the widespread application of industrial robots is their inability to deal with parts that are not precisely positioned. In the case of manual assembly, components are often presented in bins. Current automated systems, on the other hand, require separate feeders which present the parts with carefully controlled position and attitude. Here we show how results in machine vision provide techniques for automatically directing a mechanical manipulator to pick one object at a time out of a pile. The attitude of the object to be picked up is determined using a histogram of the orientations of visible surface patches. Surface orientation, in turn, is determined using photometric stereo applied to multiple images. These images are taken with the same camera but differing lighting. The resulting needle map, giving the orientations of surface patches, is used to create an orientation histogram which is a discrete approximation to the extended Gaussian image. This can be matched against a synthetic orientation histogram obtained from prototypical models of the objects to be manipulated. Such models may be obtained from computer aided design (CAD) databases. The method thus requires that the shape of the objects be described, but it is not restricted to particular types of objects. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1983
Accession Number
ADA139257

Entities

People

  • B. K. P. Horn
  • K. Ikeuchi

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Advanced Electronics
  • Air Platforms
  • Autonomy
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Artificial Intelligence
  • Computer Science
  • Computer Vision
  • Computer-Aided Design
  • Computers
  • Contracts
  • Control Systems
  • Department Of Defense
  • Geometry
  • Image Processing
  • Image Recognition
  • Materials
  • Military Research
  • Object Recognition
  • Recognition
  • Three Dimensional

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Autonomy