By Topic

Automated 3-D Micrograsping Tasks Performed by Vision-Based Control

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Lidai Wang ; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada ; Lu Ren ; James K. Mills ; William L. Cleghorn

We present a fully automated micrograsping methodology that uses a micro-robot and a microgripper to automatically grasp a micropart in three-dimensional (3-D) space. To accurately grasp a micropart in 3-D space, we propose a three-stage micrograsping strategy: (i) coarse alignment of a micropart with a microgripper in the image plane of a video camera system; (ii) alignment of the micropart with the microgripper in the direction normal to the image plane; (iii) fine alignment of the micropart with the microgripper in the image plane, until the micropart is completely grasped. Two different vision-based feedback controllers are employed to perform the coarse and fine alignment in the image plane. The vision-based feedback controller used for the fine alignment employs position feedback signals obtained from two special patterns, which can achieve submicron alignment accuracy. Fully automated micrograsping experiments are conducted on a microassembly robot. The experimental results show that the average alignment accuracy achieved during automated grasping is approximately ± 0.07 μm; the time to complete an automated micrograsping task is as short as 7.9 seconds; and the success rate is as high as 94%.

Published in:

IEEE Transactions on Automation Science and Engineering  (Volume:7 ,  Issue: 3 )