DocumentCode :
3186044
Title :
Jacobian Learning Methods for Tasks Sequencing in Visual Servoing
Author :
Mansard, Nicolas ; Lopes, Manuel ; Santos-Victor, Jose ; Chaumette, Francois
Author_Institution :
IRISA-INRIA, Rennes
fYear :
2006
fDate :
9-15 Oct. 2006
Firstpage :
4284
Lastpage :
4290
Abstract :
In this paper, the coupling between Jacobian learning and task sequencing through the redundancy approach is studied. It is well known that visual servoing is robust to modeling errors in the Jacobian matrices. This justifies why Jacobian estimation does not usually degrade the system convergence. However, we show that this is not true any more when the redundancy formalism is used. In this case the Jacobian matrix is also necessary to compute projection operators for task decomposition, which is quite sensitive to errors. We show that learning improves the servoing performance, when task sequencing is used. Conversely, sequencing improves the convergence of learning, especially for tasks involving several degrees of freedom. Eye-in-hand and eye-to-hand experiments have been performed on two robots with six degrees of freedom
Keywords :
Jacobian matrices; learning (artificial intelligence); motion control; path planning; redundancy; robots; visual servoing; Jacobian learning method; Jacobian matrices; redundancy approach; robot motion; tasks sequencing; visual servoing; Calibration; Cameras; Convergence; Jacobian matrices; Learning systems; Redundancy; Robot sensing systems; Robot vision systems; Robustness; Visual servoing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-0258-1
Electronic_ISBN :
1-4244-0259-X
Type :
conf
DOI :
10.1109/IROS.2006.281958
Filename :
4059085
Link To Document :
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