DocumentCode
383169
Title
Data fusion for compliant motion tasks based on human skills
Author
Cortesão, Rui ; Koeppe, Ralf ; Nunes, Urbano ; Hirzinger, Gerd
Author_Institution
Electr. & Comput. Eng. Dept., Coimbra Univ., Portugal
Volume
2
fYear
2002
fDate
2002
Firstpage
1529
Abstract
The paper discusses new developments of the data fusion paradigm due to Cortesao and Koeppe (1999, 2000). A bank of Kalman filters is analyzed in the fusion process. Experiments for a robotic compliant motion task (peg-in-hole) emerged from human skills are reported. Stereo vision and pose sense are fused to execute the task. Feedforward artificial neural networks (ANNs) are trained to transfer human skills to robotic manipulators.
Keywords
Kalman filters; assembling; compliance control; feedforward neural nets; filtering theory; manipulators; robot vision; sensor fusion; stereo image processing; Kalman filters; data fusion; feedforward ANN; feedforward artificial neural networks; human skills; peg-in-hole insertion; pose sensing; robotic compliant motion task; stereo vision; Aerospace engineering; Humans; Information filtering; Information filters; Mechatronics; Robot sensing systems; Sensor phenomena and characterization; State estimation; Stereo vision; Stochastic resonance;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Robots and Systems, 2002. IEEE/RSJ International Conference on
Print_ISBN
0-7803-7398-7
Type
conf
DOI
10.1109/IRDS.2002.1043972
Filename
1043972
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