Product Identification And Robotic Arm Controller System Using Artificial Neural Networks And Microcontroller

Mohd Zulfadhli, Salehan (2015) Product Identification And Robotic Arm Controller System Using Artificial Neural Networks And Microcontroller. Project Report. UTeM, Melaka, Malaysia. (Submitted)

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Abstract

This project present a product identification system using artificial neural networks where this system capable of identifying and categorize the product based on classification range of the product. Through this project, it applied the image processing that required the product image and used Radial Basis Function Neural Network (RBFNN) to recognize the product category. Advantage of this RBF Neural Networks is it is a simple structure, rapid training process and good extend ability where RBF Neural Network is appropriate to used in various fields especially in the aspects of pattern classification and function approach. The output of the network can be optimized by setting suitable values of the center and spread of the RBF. In this paper, fixed spread value will be used for every cluster. This project also using a Robotic Arm Controller for pick and place the product according to their type. The controller is designed to control the process of lifting object using a robot arm using MATLAB. This project also uses a PIC microcontroller circuit as the basic circuit. 6 servo motors will be used as an application extension to make movements and lifting an object or as robot joint. In this project, the PIC microcontroller will be programmed to control the servo motor. At the end of the project, this system capable to display the product category through LCD display such as product A, product B, product C and product D. The project is also can be used in the electronics industry and the manufacturing industry.

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