Title :
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
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;
Conference_Titel :
Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on
Conference_Location :
Sanya, Hainan
Print_ISBN :
978-0-7695-3605-7
DOI :
10.1109/CSO.2009.331
