RBF Network Based Feature-Level Data Fusion for Robotic Multi-sensor Gripper

Author

Sun, Hong ; Zhu, Hai-chuan ; Wu, Ting

Author_Institution

Dept. of Mech. & Electr. Eng., Anhui Univ. of Archit., Hefei, China

Volume

1

fYear

2009

fDate

24-26 April 2009

Firstpage

719

Lastpage

722

Abstract

There are many kinds of sensors equipped on the robot gripper, for example, force sensor, proximity sensor, displacement sensor and etc. They can all be utilized to determine the state of connection. However, due to measurement error, uncertain work environment, no data of any certain sensor is sufficient to determine the state of connection. In order to grasp objects safely and reliably, the information fusion should be carried out for the output data of multi-sensors. In this paper, we present a novel technique to do the feature-level data fusion by RBF network. According to the result of fusion, control system can fast and accurately determine the state of connection between gripper and objects in real-time.

Keywords

displacement control; force sensors; grippers; measurement errors; mobile robots; neurocontrollers; radial basis function networks; sensor fusion; uncertain systems; RBF network based feature-level data fusion; displacement sensor; force sensor; measurement error; proximity sensor; radial basis function network; robotic multisensor gripper; state-of-connection; uncertain work environment; walking robot; Clamps; Fingers; Force sensors; Grippers; Mechanical sensors; Radial basis function networks; Robot sensing systems; Safety; Sensor fusion; State estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on

Conference_Location

Sanya, Hainan

Print_ISBN

978-0-7695-3605-7

Type

conf

DOI

10.1109/CSO.2009.331

Filename

5193794