TY - GEN
T1 - A Lagrangian network for multifingered hand grasping force optimization
AU - Tang, Wai Sum
AU - Wang, Jun
PY - 2002
Y1 - 2002
N2 - In this paper, a Lagrangian network which is developed from the Lagrange multiplier method, is proposed for multifingered hand grasping force optimization. The Lagrangian network is a recurrent neural network and is shown to be capable of taking into account the nonlinearity of the friction constraints between contacts. By giving the external load and the finger joint torque limits to the neural network, it would asymptotically converge to a set of optimal grasping forces. Simulation results show that the proposed approach would give a better quality of optimal grasping force compared to other approaches in the literature.
AB - In this paper, a Lagrangian network which is developed from the Lagrange multiplier method, is proposed for multifingered hand grasping force optimization. The Lagrangian network is a recurrent neural network and is shown to be capable of taking into account the nonlinearity of the friction constraints between contacts. By giving the external load and the finger joint torque limits to the neural network, it would asymptotically converge to a set of optimal grasping forces. Simulation results show that the proposed approach would give a better quality of optimal grasping force compared to other approaches in the literature.
UR - http://www.scopus.com/inward/record.url?scp=0036078785&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0036078785&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
VL - 1
SP - 177
EP - 182
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 -