Automated Assembly Using Feature Localization

Item

Title
en_US Automated Assembly Using Feature Localization
Creator
en_US Gordon, Steven Jeffrey
Date
2004-10-20T20:02:58Z
Date Available
2004-10-20T20:02:58Z
Date Issued
en_US 1986-12-01
Identifier
en_US AITR-932
uri
http://hdl.handle.net/1721.1/6860
Abstract
en_US Automated assembly of mechanical devices is studies by researching methods of operating assembly equipment in a variable manner; that is, systems which may be configured to perform many different assembly operations are studied. The general parts assembly operation involves the removal of alignment errors within some tolerance and without damaging the parts. Two methods for eliminating alignment errors are discussed: a priori suppression and measurement and removal. Both methods are studied with the more novel measurement and removal technique being studied in greater detail. During the study of this technique, a fast and accurate six degree-of-freedom position sensor based on a light-stripe vision technique was developed. Specifications for the sensor were derived from an assembly-system error analysis. Studies on extracting accurate information from the sensor by optimally reducing redundant information, filtering quantization noise, and careful calibration procedures were performed. Prototype assembly systems for both error elimination techniques were implemented and used to assemble several products. The assembly system based on the a priori suppression technique uses a number of mechanical assembly tools and software systems which extend the capabilities of industrial robots. The need for the tools was determined through an assembly task analysis of several consumer and automotive products. The assembly system based on the measurement and removal technique used the six degree-of-freedom position sensor to measure part misalignments. Robot commands for aligning the parts were automatically calculated based on the sensor data and executed.
Extent
en_US 279 p.
35053969 bytes
28370075 bytes
Format
application/postscript
application/pdf
Language
en_US
Relation
en_US AITR-932
Item sets
AI Technical Reports (1964 - 2004)