An improved neural network based fuzzy self-adaptive KALMAN filter and its application in cone picking robot

Aimed to improve the working efficiency of cone picking robot and release workers from heavy manual labor, a novel RBF neural network based fuzzy self-adaptive Kalman filter is presented in the paper. The position and object input voltage are taken as the inputs of the RBF neural network model. Cons...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 1; pp. 573 - 577
Main Authors Xiu-Rong Guo, Feng-Hu Wang, Dan-Feng Du, Xiu-Li Guo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Automatic control
Cone picking robot
Control systems
Cybernetics
Forestry
Fuzzy neural networks
Fuzzy self-adaptive KALMAN filter
Hydraulic drive
Machine learning
Manipulators
Neural networks
RBFNN (radial basis function neural network) controller
Robotics and automation
Service robots
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Summary:Aimed to improve the working efficiency of cone picking robot and release workers from heavy manual labor, a novel RBF neural network based fuzzy self-adaptive Kalman filter is presented in the paper. The position and object input voltage are taken as the inputs of the RBF neural network model. Consider that the traditional BP algorithm has shortcomings of converging slowly and easily trapping a local minimum value, a combination learning algorithm using fuzzy self-adaptive Kalman filter is adopted to train the neural network. The sample data obtained from the 3D laser scanner and sensors located on the cone picking robot. Experimental results show that it will enable the training process with an overall accuracy and rapid convergence speed. The application of the technology in cone picking robot automatic control system proves it is an effective method and has certain project value.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212508