A Lagrangian network for multifingered hand grasping force optimization

Wai Sum Tang, Jun Wang

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages177-182
Number of pages6
Publication statusPublished - 2002
Externally publishedYes
Event2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States
Duration: 12 May 200217 May 2002

Conference

Conference2002 International Joint Conference on Neural Networks (IJCNN '02)
Country/TerritoryUnited States
CityHonolulu, HI
Period12/05/0217/05/02

Fingerprint

Dive into the research topics of 'A Lagrangian network for multifingered hand grasping force optimization'. Together they form a unique fingerprint.

Cite this