İbrahim KESİCİ, Şeyma BİŞKİN, Alaa ELEYAN

Automatic Object Painting with SCARA Robot Using Computer Vision

Recognizing and accurately classifying colors in industrial applications is a critical challenge in developing robotics painting applications. To achieve this, many painting robots are attached with expensive color sensors. However, these sensors are coming with some drawbacks such as color ranges limitation and sensitivity to illumination in addition to their high costs. In the last decades, camera systems gained importance in robotics applications with the power presented by the computer vision techniques. The main objective of this paper is to design an automation line that includes a robot and camera system to perform painting in different colors, with various illumination conditions at cheaper costs. The proposed system can be used to paint multiple colors effectively and accurately. The power of the system comes from the color detection and classification algorithm that is designed using computer vision techniques. The algorithm is designed under C++ environment using OpenCV library. The system will able to detect all colors that are adjusted/predefined offline by the user and to work in different illumination conditions. The end-effector of the robot consists of two main parts, a camera to detect the desired color and an automatic spray gun to perform the painting operation. The proposed color detection system will be based on a small sticker pasted on the object that will be painted. When the desired color is detected, the system starts the painting operation. Moreover, the system has the capability to automatically cleaning the spray gun and the connected tubes in the case that the successive object is to be painted with different color.

PDF

___

10. Constante, P, Gordon, A, Chang, O, Artificial vision techniques for strawberry’s industrial classification, IEEE Latin America Transactions, 2016, 2576 – 2581.
9. Russell, M, Fischaber, S, OpenCV Based Road Sign Recognition on Zynq, 11th IEEE International Conference on Industrial Informatics (INDIN), 2013, pp. 596 – 601.
8. Silva, A,S, Marcolino, F, Severgnini, Q, Oliveira, M, L, Object tracking by color and active contour model s segmentation, IEEE Latin America Transactions, 2016, 1488 – 1493.
7. Szabo, R, Gontean, A, Sfirat, A, Robotic Arm Control in Space with Color Recognition Using a Raspberry Pi, 39th International Conference on Telecommunications and Signal Processing (TSP), pp. 689 – 692, Dec. 2016
6. Shin, K, J, Detecting the Position of The Fish Robot Using the Color Segment Algorithm, Future Technologies Conference (FTC), 2016, pp. 896 – 900.
5. Mu, J, and Li, Y, A New Efficient Real-Time Arbitrary Colored Ball Recognition Method for A Humanoid Soccer Robot, 12th World Congress on Intelligent Control and Automation (WCICA), 2016, pp. 494 – 499.
4. Bo, Z, Fang, F, Zhenhua, S, Zhengda, M, Xianzhong, D, Fast and Template Path Planning of Spray Painting Robots for Regular Surfaces, 34th Chinese Control Conference (CCC) , 2015, pp. 211- 216.
3. Gasparetto, A, Vidoni, R, Pillan, D, Saccavini, E, Automatic Path and Trajectory Planning for Robotic Spray Painting, 7th German Conference on Robotics, 2012, pp. 5925 – 5930.
2. Ata, A, Eleyan A, An Autonomous Robotic Cell for Painting Applications, Proceedings of the IASTED International Conference Modelling, Identification and Control (MIC 2017), Innsbruck, Austria, 2017, pp. 200 – 207.
1. Ata, A, Eleyan A, Innovative Painting Robotic Cell for Industrial Applications, 3rd International Symposium on Innovative Technologies in Engineering and Science (ISITES2015), Valencia, Spain, 2015, pp. 2462 – 2469.