Neural Network Based Visual Servo Control Under the Condition of Heavy Loading

2021 ◽  
Author(s):  
Shenghao Tong ◽  
Ke Zhang ◽  
Huaitao Shi ◽  
Jinbao Zhao ◽  
Jie Sun
Keyword(s):  
Neural Network ◽  
Servo Control ◽  
Visual Servo ◽  
Visual Servo Control

Robots visual servo control with features constraint employing Kalman-neural-network filtering scheme

2015 ◽  
Vol 151 ◽  
pp. 268-277 ◽  
Author(s):  
Xungao Zhong ◽  
Xunyu Zhong ◽  
Xiafu Peng
Keyword(s):  
Neural Network ◽  
Servo Control ◽  
Visual Servo ◽  
Visual Servo Control

Neural network based visual servo control for CNC load/unload manipulator

Optik ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4489-4492 ◽  
Author(s):  
Jinan Gu ◽  
Hongmei Wang ◽  
Yuelong Pan ◽  
Qian Wu
Keyword(s):  
Neural Network ◽  
Servo Control ◽  
Visual Servo ◽  
Visual Servo Control

A Visual Servo Control Method for Tomato Cluster-Picking Manipulators Based on a T-S Fuzzy Neural Network

2021 ◽  
Vol 64 (2) ◽  
pp. 529-543
Author(s):  
Xifeng Liang ◽  
Ming Peng ◽  
Jie Lu ◽  
Chao Qin
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fuzzy Neural Network ◽  
Jacobian Matrix ◽  
Servo System ◽  
Servo Control ◽  
Visual Servo ◽  
Visual Servo Control ◽  
Neural Network Controller ◽  
Fuzzy Neural

HighlightsA T-S fuzzy neural network was applied to the visual servo control system of a tomato picking manipulator.The T-S fuzzy neural network structure was designed, and collected data were used to train the neural network model.A visual servo control system for the picking manipulator based on the neural network was designed and tested.The T-S fuzzy neural network was superior to a BP neural network in visual servo control of the picking manipulator.Abstract. To reduce the computational load of image Jacobian matrix estimation and to avoid the appearance of singularity of a Jacobian matrix in the visual servo control of a picking manipulator, a T-S fuzzy neural network algorithm is proposed to replace the image Jacobian matrix. This better fits the hand-eye relationship by combining the knowledge structure of fuzzy reasoning with the self-learning ability of a neural network. The T-S fuzzy neural network was trained and tested by collecting the variation data of image features and joint angles; after training, the T-S fuzzy neural network was used to predict the joint angles of the picking manipulator. Simulation results show that the square sum of training errors and testing errors were 0.017 and 0.032, respectively, after training the T-S fuzzy neural network. A T-S fuzzy neural network controller was applied to the visual servo system of the picking robot, and the test results show that the average difference between the end-effector and the ultimate target location of the visual servo system based on the T-S fuzzy neural network controller was 0.0037 m, which was 79.44% less than that of the visual servo system based on a BP neural network. The final average error of image features was between 0.52 and 3.25 pixels, which was 74.932% less than that of the visual servo system based on the BP neural network. Keywords: Picking manipulator, Tomato clusters, T-S fuzzy neural network, Visual servoing.

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