DocumentCode
2334603
Title
An artificial neural network approach for motion coordination of hyper-redundant articulated systems
Author
Carrera, Jonathan ; Mayorga, Rene V.
fYear
2007
fDate
Oct. 29 2007-Nov. 2 2007
Firstpage
3974
Lastpage
3979
Abstract
In this article an Artificial Neural Network (ANN) approach for the motion coordination of hyper- redundant systems is presented. In particular, the approach is exemplified for the posture optimization of articulated systems under a framework conducing to the fast/efficient computation of an under-laying inverse continuous time-variant function. The ANN approach consists on the fast computation of the inverse function and an associated null space vector derived from also some novel geometrical concepts.
Keywords
motion control; neurocontrollers; redundant manipulators; time-varying systems; artificial neural network; hyper-redundant articulated systems; inverse continuous time-variant function; motion coordination; posture optimization; Animation; Artificial neural networks; Biological system modeling; End effectors; Humans; Intelligent robots; Notice of Violation; Robot kinematics; USA Councils; Weight control;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on
Conference_Location
San Diego, CA
Print_ISBN
978-1-4244-0912-9
Electronic_ISBN
978-1-4244-0912-9
Type
conf
DOI
10.1109/IROS.2007.4399069
Filename
4399069
Link To Document