TY - GEN
T1 - Two recurrent neural networks for grasping force optimization of multi-fingered robotic hands
AU - Fok, Lo-Ming
AU - Wang, Jun
PY - 2002
Y1 - 2002
N2 - In this paper, two recurrent neural networks are proposed for grasping force optimization of multi-fingered robotic hands. The neural networks are shown to be capable to optimize the norm of grasping force subject to the friction cone constraint and balance the external force applied to an object. A three-finger example is discussed to demonstrate the optimality of the neural network models.
AB - In this paper, two recurrent neural networks are proposed for grasping force optimization of multi-fingered robotic hands. The neural networks are shown to be capable to optimize the norm of grasping force subject to the friction cone constraint and balance the external force applied to an object. A three-finger example is discussed to demonstrate the optimality of the neural network models.
UR - https://www.scopus.com/pages/publications/0036075984
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0036075984&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
VL - 1
SP - 35
EP - 40
BT - Proceedings of the International Joint Conference on Neural Networks
T2 - 2002 International Joint Conference on Neural Networks (IJCNN '02)
Y2 - 12 May 2002 through 17 May 2002
ER -