Design and Implementation of Neural-Network Based Robotic Gripper Control

• Main Authors: CHUNG,YUAN-HUNG, 鐘元宏
• Other Authors: Su,Kuo-Ho
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/m7bdwf

碩士 === 中國文化大學 === 機械工程學系數位機電碩士班 === 106 === Because the trends of industry 4.0, image processing has significant evolutions and improvement recently. The image resolution and quality have been promoted, such as satellite photography, medical microscope, etc., The technology can also be used in digital art, size measurement, image reconstruction, and so on. With the rapid technology development of machine and robot, the industrial 4.0 is developing rapidly. Among the technologies, image identification is an indispensable part of the robot. In this thesis, the camera module is integrated with the handling gripper and the gripper is mounted in the moving robot. The neural-network identification, which makes this study more humanity, can help the action of the claw grasping. The proposed method can provide another solution for image identification. Image identification is an important part of the robot, it can be used to identify the position and type of the gripping object. In this paper, we use the Raspberry Pi with the visual identification functions of OpenCV to execute the image preprocessing, coordinate values and eigenvalues calculation of the captured objects. In the identification architecture, the proposed method is composed of the image invariant moments and the heuristic neural-network. The neural-network model is trained and established via the input data, which is collected in the database including the invariant moment value and responding type. Then the neural-network model is embedded into the Raspberry Pi to control the handling gripper. In addition, the TSK fuzzy controller, the Arduino MCU with the ultrasonic sensor being adopted, is designed to control the base mobile platform. The Arduino MCU can connect with the Raspberry Pi to decide whether the appropriated location has been reached or not. To verify the effectiveness of the proposed method, the simulation results using the Matlab package are provided and a handing gripper prototype is implemented. Then the experimental results are also provided. From the results, the proposed neural-network model can correctly identify the grasping object and grasp it.