Two recurrent neural networks for grasping force optimization of multi-fingered robotic hands

Lo Ming Fok, Jun Wang

Research output: Contribution to conferencePaperpeer-review

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages35-40
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

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