Please use this identifier to cite or link to this item:
Title: Toward automatic robot programming: learning human skill from visual data
Keywords: Kalman Filters
Feature Extraction
Higher Order Statistics
Image Sequences
Robot Programming
Splines (Mathematics)
Issue Date: 2000
Publisher: IEEE
Citation: IEEE Transactions on Systems, Man, and Cybernetics Part B 30(1), 180-85
Abstract: We propose a novel approach to program a robot by demonstrating the task multiple number of times in front of a binocular vision system. We track artificially-induced features appearing in the image plane due to nonimpedimental color stickers attached at different fingertips and wrist joint, in a simultaneous feature detection and tracking framework. A Kalman filter does the tracking by recursively predicting the tentative feature location and a higher order statistics (HOS)-based data clustering algorithm extracts the feature. A fast and efficient algorithm for the vision system thus developed processes a binocular video sequence to obtain the trajectories and the orientation information of the end effector from the images of a human hand. The concept of trajectory bundle is introduced to avoid singularities and to obtain an optimal path.
ISSN: 1083-4419
Appears in Collections:Article

Files in This Item:
File Description SizeFormat 
17934136.62 kBUnknownView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.