Design and Development of Product Sorting Robot
Abstract
The EV3 Product Sorting Machine is designed and implemented to provide a better machine for the industry that will act on behalf of the workers in an industry by helping to sort the products according to the type without the usage of manpower. This machine will detect the stock to arrive from the factory, sort it and arrange it for their categories. At the same time, it will also record the total number of stock available in the store and also can withdraw stock from storage by their categories. The main idea why we propose this project is because there are certain problems in the industry like the industry of manufacturing that needs a lot of manpower to complete their work, workers always have mistaken or errors in sorting products to the right place and there is difficulty to calculate stock available. Our objective is to create a sustainable robot to sort products according to the code, to classify products according to the category and to identify the number of products available from each category of product. Here, the image processing method is implemented to determine and read the code on the label of the product, once the image of the code is processed it will instruct the next execution. This project is more significant in developing this robot as this invention could help the industry to get more systematic work with fewer man errors. Besides that, this project will also enable the industry to save a lot of money as less manpower needed to work.
Keywords—EV3 Robot; Product Sorting; Image Processing;
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REFERENCES
M. K. Nurul Nadirah, S. A. (UTeM) Sharifah Sakinah, and S. Abdul Samad, “Improved fuzzy_PID controller in following complicated path for LEGO Mindstorms NXT,” in Proceedings of Mechanical Engineering Day 2017, 2017, pp. 474–475.
A. Mohammed, L. Wang, and R. X. Gao, “Integrated image processing and path planning for robotic sketching,” Procedia CIRP, vol. 12, pp. 199–204, 2013.
P. Kopacek, “Development Trends in Robotics,” IFAC-PapersOnLine, vol. 49, no. 29, pp. 36–41, 2016.
E. Guizzo, “Robots with their heads in the clouds,” IEEE Spectr., vol. 48, no. 3, pp. 17–18, 2011.
I. A. T. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. Ullah Khan, “The rise of ‘big data’ on cloud computing: Review and open research issues,” Inf. Syst., vol. 47, pp. 98–115, 2015.
A. G. Del Molino, B. Mandal, J. Lin, J. H. Lim, V. Subbaraju, and V. Chandrasekhar, “VC-I2R@ImageCLEF2017: Ensemble of deep learned features for lifelog video summarization,” CEUR Workshop Proc., vol. 1866, 2017.
S. Z. Masood, G. Shu, A. Dehghan, and E. G. Ortiz, “License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks,” 2017.
A. Dehghan, E. G. Ortiz, G. Shu, and S. Z. Masood, “DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network,” 2017.
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Z. Othman, N. A. Abdullah, K. Y. Chin, F. F. W. Shahrin, S. S. S. Ahmad, and F. Kasmin, “Comparison on Cloud Image Classification for Thrash Collecting LEGO Mindstorms EV3 Robot,” Int. J. Hum. Technol. Interact., vol. 2, no. 1, pp. 29–34, 2018.
Z. Othman, N. A. Abdullah, C. K. Yee, F. Farina, W. Shahrin, and S. S. Syed, “Image Processing Technique using Google Cloud API and Sighthound for Lego Mindstorms EV3 Robot,” Robot. Autom. Eng. J., vol. 2, no. 3, pp. 2–4, 2018.
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