The Research on the Static Calibration of Fingertip Force Sensor for Underwater Dexterous Hand on RBF Neural Network

Abstract:

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The fingertip force sensor is the key for the complex task of the dexterous underwater hand, in order to safely grasp an unknown object using the dexterous underwater hand and accurately perceive its position in the fingers, a sensor should be developed, which can detect the force and position simultaneously. Furthermore, this sensor should be used underwater. It is difficult to employ the accustomed calibration method for the characteristic of the fingertip force sensor, and the accustomed method is not able to assure the precision. A calibration method based on RBF (Radial-Basis Function) neural network is introduced. Furthermore, the calibration system and program are also designed. The calibration experiment of the sensor is carried out. The results show the nonlinear calibration method based on RBF neural network assure the precision of the sensor, which meets the demand of research on the underwater dexterous hand.

Info:

Periodical:

Edited by:

Kai Cheng, Yingxue Yao and Liang Zhou

Pages:

267-270

DOI:

10.4028/www.scientific.net/AMM.10-12.267

Citation:

P. Jia et al., "The Research on the Static Calibration of Fingertip Force Sensor for Underwater Dexterous Hand on RBF Neural Network", Applied Mechanics and Materials, Vols. 10-12, pp. 267-270, 2008

Online since:

December 2007

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Price:

$35.00

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