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Designing and Implementing a Didactic Module of Artificial Vision for the Selection of Objects According to Colors and Morphological Characteristics


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DOI: https://doi.org/10.15866/ireaco.v13i5.19089

Abstract


This present research describes the design and the implementation of a didactic module for the selection of objects, according to their colors and morphological characteristics using a robotic arm, through artificial vision, as well as the visualization of the process in a virtual environment. The project is equipped with a robotic arm that serves to select, capture, and locate nylon elements of different colors and shapes, placing them in different classification trays; it will also be visualized on a graphic interface through a monitor. Elements such as the Arduino data processing card, a 1920×1080 pixel HD camera to improve the visualization of the shapes and color of the element and a conveyor belt that will allow the movement of the elements have been used. Using the images obtained by the camera and processed in binary form in the Arduino, an automated control of the robotic arm that allows controlled movement and proper positioning in the corresponding sorting tray is obtained. The processing of the images uses a specific programming to make the edge detection and to obtain the points of the image that belongs to the border of the desired figure. Due to the imperfections of the image a process of filters in the image is followed as the scaling, the dilation of the image that allows suppressing the background of the figure and finally the erosion that helps to join or to obtain an outline to the desired shape. With the design and the construction of this didactic module of artificial vision, students and teaching staff will be provided with a better visualization of an industrial environment in automated inspection and quality control tasks, with the aim of improving the repetitiveness and precision obtained in a manual inspection process.
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Keywords


Artificial Vision; Morphological Figures; Robotic Arm; Virtual Environment

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References


Xin Li, Yiliang Shi, Computer Vision Imaging Based on Artificial Intelligence, International Conference on Virtual Reality and Intelligent Systems (ICVRIS), ISSN: 978-1-5386-8031-5 PP. 22-25, 2018.
https://doi.org/10.1109/icvris.2018.00014

Y. A. Cruzado Ramírez, Design of a teaching module based on artificial vision for the inspection of products according to their shape, color and/or geometric dimensions, Trujillo: National University of Trujillo, p. 141, 2017.

S. Chen, J. Xiong, W. Guo, R. Bu, Z. Zheng, Y. Chen, Z. Yang, R. Lui, Colored rice quality inspection system using machine vision, Journal of Cereal Science, ISSN: 0733-5210, Vol. 88, Pg. 87-95, 2019.
https://doi.org/10.1016/j.jcs.2019.05.010

Chandra Sekhar, N., Laxminarayana, P., Development of Visual Identification System for Classification of Part Family for Medium Scale Industries, (2017) International Review of Mechanical Engineering (IREME), 11 (10), pp. 774-779.
https://doi.org/10.15866/ireme.v11i10.13400

El Kari, B., Ayad, H., El Kari, A., Mjahed, M., Pozna, C., Design and FPGA Implementation of a New Intelligent Behaviors Fusion for Mobile Robot Using Fuzzy Logic, (2019) International Review of Automatic Control (IREACO), 12 (1), pp. 1-10.
https://doi.org/10.15866/ireaco.v12i1.14802

Putra, O., Prianto, B., Prayitno, A., Setiawan, E., Yuniarno, E., Purnomo, M., A Novel Approach on Dehazing Volcanic Crater Lake Hazy Scene Videos Based on Color Attenuation Prior, (2017) International Review on Computers and Software (IRECOS), 12 (1), pp. 40-53.
https://doi.org/10.15866/irecos.v12i1.11735

FESTO, Industrial Automation, Learning systems and services for technical training, pp. 186–187, 2017.

L. F. Rico Riveros, J. E. Sanabria Sanabria, V. H. Bernal Tristancho, J. D. Quintero Urrea, R. M. Pinilla Santana, & J. L. Manosalva Fonseca, Didactic strategy based on industry 4.0 for the integration of programmable logic controller and industrial robot technologies. International Meeting on Engineering Education, pp. 10, Bogotá, 2020.

J. Tinajero, L. Acosta, E. Chando, J. Moyon, Artificial Vision System to classify paint cans by color considering the RGB color space, Revista Espacios, Vol. 41, No. 08, Pág. 18, ISSN: 07981015, España, 2020.

S. P. R. S. Valluru, Simulation and Analysis of Liquid Lens Behaviour for Machine Vision Applications -CA13 (AFT)*, 2019 30th Irish Signals and Systems Conference (ISSC), Maynooth, Ireland, 2019, pp. 1-6.
https://doi.org/10.1109/issc.2019.8904944

M. Serrano, N. Litardo, J. Ordoñez, Coffee Fruit Recognition Using Artificial Vision and neural NETWORKS, 5th International Conference on Control and Robotics Engineering, University of Brighton, ISSN: 978-1-7281-6791-6, pp. 224-228, 2020.
https://doi.org/10.1109/ICCRE49379.2020.9096441

D. Mantegazza, J. Guzzi, L. Gambardella, A. Giusti, Learning Vision-Based Quadrotor Control in User Proximity, 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Daegu, Korea (South), ISSN: 978-1-5386-8555-6, pp. 369, 2019.
https://doi.org/10.1109/hri.2019.8673022

N. Truong, N. Dac, H. Thanh, N. Tran, Mango Classification System Based on Machine Vision and Artificial Intelligence, IEEE 7th International Conference on Control, Mechatronics and Automation, Faculty of mechanical engineering Ho Chi Minh City University of Technology and Education Ho Chi Minh City, Viet Nam, ISSN: 978-1-7281-3787-2, pp. 475-482, 2019.
https://doi.org/10.1109/ICCMA46720.2019.8988603

V. Sergey, S. Illya, Artificial Object Images Synthesis in Underwater Robot Vision System, International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), Department of Computer Engineering Pacific National University Khabarovsk, ISSN: 978-1-7281-4590-7, pp. 6, Russia, 2020.
https://doi.org/10.1109/icieam48468.2020.9111940

Pan, Z., Jia, Z., Jing, K., Ding, Y., & Liang, Q., Manipulator Package Sorting and Placing System Based on Computer Vision. Chinese Control and Decision Conference (CCDC), Faculty of Robot Science and Engineering Northeastern UniversityˈLiaoning, ISSN: 978-1-7281-5855-6, pp. 409-414, 2020.
https://doi.org/10.1109/ccdc49329.2020.9164071
G. Viera , Image processing using OpenCV applied in raspberry PI for the cocoa clasification, University of Piura, Faculty of Engineering, Department of Mechanical and Electrical Engineering, pp. 150, Perú, 2017.

I. García, Practical introduction to Unity 3D, TRINIT Asoc. Computers, Zaragoza, pp. 41, 2018.

E. Lara, Accessing an FBX file in Unity, Escola Spai, pp 1-2 2018.

T. Gayathri, P. Neelamegam, S. Sudha, Image Processing System for Automatic Segmentation and Yield Prediction of fruits using OpenCV, International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), Sastra University, ISBN: 978-1-5386-3242-0, pp. 758-762, India, 2017.
https://doi.org/10.1109/ctceec.2017.8455137

Cross-Platform Mobile Development in Visual Studio | Microsoft Docs. Pp. 110. 2019.

L. Valle, How to install an Arduino library in the development Environment, pp. 3, 2017.

A. Fuster, Autonomous navigation via raspberry PI and the OpenCV platform, Polytechnic University of Valencia, School of Computer Engineering, pp. 74, Valencia, 2017.

J. Aditya, A. Pasha, V. Balaji, Design and development of solar powered automatic grain dryer for storage, IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Ramaiah University, Dep. Of Electrical Engineering, Bangalore, ISBN: 978-1-7281-3735-3, pp. 5, India, 2019.
https://doi.org/10.1109/discover47552.2019.9008041

D. Keyur, V. Chauhan, B. Surgenor A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach, Journal of Intelligent Manufacturing pp. 23, 2018.
https://doi.org/10.1007/s10845-018-1438-3

J. Taquía, Image processing and its potential application in companies with a digital strategy, University of Lima, ISSN: 1993-4912, No. 10, pp. 11-29, Lima, Perú, 2017.

C. Gamarra, M. Ríos, Application of deep learning techniques for the classification and recognition of objects in images, Faculty of Electronic Engineering, Furnace ST. Thomas, Bogotá D.C, pp. 66. 2018.

S. Pachpande, A. Chaudhari, Implementation of devanagri character recognition system through pattern recognition techniques, 2017 International Conference on Trends in Electronics and Informatics (ICEI), ISBN: 978-1-5090-4257-9, pp. 717-722, Tirunelveli, India, 2017.
https://doi.org/10.1109/icoei.2017.8300796

Unity Documentation, Unity Manual, Graphics FBX export Guide, 2017.

Unity Documentation, Unity Manual, Graphics GI Visualizations in the Scene View, 2017.

Allegro MicroSystems,LLC., Datasheet DMOS Microstepping Driver A4988, pp 1-3 2018.


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