Abstract
Industry 4.0 has become an inexorable trend. To increase the efficiency of testing screen on smartphone, we design a system which includes a 5 degrees-of-freedom robotic arm to smartphone automatic test. The coordinate conversion is important for the operation between robot and camera. We use an algorithm named improved back propagation (BP) neural network on the robot arm to solve the coordinate conversion problem. First, the neural network is used to fix the error of coordinate difference between robot and camera. Then the improved BP algorithm will train the network through the error. For the machine vision, we use a video camera to catch the patterns on the screen of tested smartphone. The control scheme calculates angle of motor through image processing and fuzzy control. When robot arm cannot press buttons correctly, fuzzy system will fix the error. Experimental results show that the proposed control scheme is capable of drive the robotic arm by DH model to press the desired positions on the tested smartphone.
This is a preview of subscription content, log in via an institution to check access.
References
Y.H. Weng, Automatic robot assembly with eye-in-hand binocular visual servoing and structured lighting, Master Thesis, Department of Electrical Engineering, National Taipei University of Technology, 2011
C.C. Liu, Commanded control of a robotic manipulator employing kinect and binocular vision sensors, Master Thesis, Department of Electrical Engineering, National Taipei University of Technology, 2011
C.L. Yu, Application of real-time image recognition and path planning to wheeled mobile robot for taking elevator, Master Thesis, Communications, Navigation and Control Engineering, Nation Taiwan Ocean University, 2012
T. Wei, Design and realization of autonomous mobile robot tracking system by multi-switching fuzzy sliding mode control, Master Thesis, Department of Electrical Engineering, National Cheng Kung University, 1998
S.F. Liang, A Parallel Back-Propagation Algorithm with the Levenberg Marquardt Method (Dept. of Computer Science & Information Management, Soochow University, 2006)
J.S. Huang, Servo motion control of robot arm, Master Thesis, Department of Power Vehicle and System Engineering, National Defense University, 2012
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hsu, WT., Lu, CC., Juang, JG. (2018). Application of the LM-HLP Neural Network to Automatic Smartphone Test System. In: Yao, L., Zhong, S., Kikuta, H., Juang, JG., Anpo, M. (eds) Advanced Mechanical Science and Technology for the Industrial Revolution 4.0. FZU 2016. Springer, Singapore. https://doi.org/10.1007/978-981-10-4109-9_23
Download citation
DOI: https://doi.org/10.1007/978-981-10-4109-9_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4108-2
Online ISBN: 978-981-10-4109-9
eBook Packages: EngineeringEngineering (R0)