TY - JOUR T1 - Design and Vision-Based Control of a Low-Cost SCARA Robot AU - Adar, Nurettin Gökhan AU - Özden, Mustafa PY - 2025 DA - September Y2 - 2025 DO - 10.17350/HJSE19030000357 JF - Hittite Journal of Science and Engineering JO - Hittite J Sci Eng PB - Hitit University WT - DergiPark SN - 2148-4171 SP - 111 EP - 120 VL - 12 IS - 3 LA - en AB - SCARA robots are widely used in industrial automation due to their high precision and speed, particularly in pick-and-place operations. In addition to conventional programming approaches, alternative vision-based control methods have gained interest to enhance flexibility and efficiency in robotic applications. This study presents the design and implementation of a Position-Based Visual Servoing (PBVS) for the SCARA robot system capable of detecting and manipulating objects in real-time. The proposed system consists of a fixed overhead camera, a SCARA robot, and Python-based control software. The software integrates image processing algorithms, kinematic calculations, and motor control, enabling the robot to autonomously identify objects, compute their positions, and execute pick and place tasks. To enhance object detection accuracy, Kuwahara filtering, Canny edge detection, morphological transformations, and connected component analysis were applied. Experimental results demonstrated that the combination of Kuwahara filtering and Canny edge detection achieved the lowest MSE error (8.45%), ensuring precise object localization. Furthermore, inverse kinematics was employed to generate accurate joint movements, allowing smooth and reliable grasping operations. The system was tested through 100 pick-and-place trials, achieving a 100% grasping success rate when Kuwahara filtering was applied. The experimental findings confirm that vision-based control significantly improves SCARA robot performance, making it suitable for automated assembly, material handling, and quality control applications. KW - : SCARA Robot KW - Vision-Based Control KW - Image Processing KW - Kuwahara Filtering KW - Real-Time Robotic Application CR - 1. Shariatee M, Akbarzadeh A, Mousavi A, Alimardani S. Design of an economical SCARA robot for industrial applications. In: Proceedings of the 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM); 2014 Oct 15-17; Tehran, Iran. IEEE; 2014. p. 534-539. doi:10.1109/ICRoM.2014.6990957. CR - 2. Priya PV, Suresh J. Integration of SCARA Robot for Pick and Place Application using PLC. In: Proceedings of the ICCAP; 2021 Dec 7. EAI; 2021. doi:10.4108/eai.7-12-2021.2314569. CR - 3. Vagaš M. The Assembly of Electric Socket at Automated Workplace with SCARA Robot. Applied Mechanics and Materials. 2016;844:25–30. doi:10.4028/www.scientific.net/amm.844.25. CR - 4. Kolanur CB, Tanvashi S, Asuti P, et al. Inspection of Silk Cocoons using 3-DOF SCARA Robot for Quality Control. Research Square. 2024 Mar 26. doi:10.21203/rs.3.rs-4149336/v1. CR - 5. Kapusi TP, Erdei TI, Husi G, Hajdu A. Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection. Robotics. 2022;11(4):69. doi:10.3390/robotics11040069. CR - 6. Pires JN, Azar AS, Nogueira F, Zhu CY, Branco R, Tankova T. The role of robotics in additive manufacturing: review of the AM processes and introduction of an intelligent system. Ind Robot. 2022;49(2):311–31. doi:10.1108/IR-06-2021-0110. CR - 7. Febrianto R. Design and development of a 5-DOF SCARA robot arm for robotics education in a STEM laboratory. Indones J Comput Sci. 2024;13(5):7198–217. doi:10.33022/ijcs.v13i5.4373. CR - 8. He Y, Li X, Xu Z, Zhou X, Li S. Collaboration of multiple SCARA robots with guaranteed safety using recurrent neural networks. Neurocomputing. 2021;438:245-259. doi:10.1016/j. neucom.2021.05.049. CR - 9. Răileanu N. Optimizing Energy Consumption of Industrial Robots with Model-Based Layout Design. Sustainability. 2024;16(3):1053. doi:10.3390/su16031053. CR - 10. D. M, Vignesh T, Amritbalaji K, Annamalaisamy K. Design and fabrication of SCARA for image processing in industry. International Journal of Scientific Research in Engineering and Management. 2024;8(10):1-15. doi:10.55041/ijsrem37833. CR - 11. Bi Y, Cheng J, Wang L, Peng Y. Intelligent logistics handling robot: design, control, and recognition. In: Proceedings of the International Conference on Artificial Life and Robotics; 2024 Jan 12–14; Beppu, Japan. 2024;29:337–45. doi:10.5954/icarob.2024.os13-1. CR - 12. Alkhedher M, Alshamasin M. SCARA robot control using neural networks. In: Proceedings of the International Conference on Information and Automation for Sustainability (ICIAfS); 2012. p. 126-130. doi:10.1109/icias.2012.6306173. CR - 13. Li L, Zhang Y. Robustness of civil aviation air cargo network based on SCARA robot dynamics model. Research Square [Preprint]. 2023. doi:10.21203/rs.3.rs-3609427/v1. CR - 14. Zhen S, Zhao Z, Liu X, Feng C, Zhao H, Chen Y. A novel practical robust control inheriting PID for SCARA robot. IEEE Access. 2020;8:227409-227419. doi:10.1109/access.2020.3045789. CR - 15. Surapong N, Mitsantisuk C. Position and force control of the SCARA robot based on disturbance observer. Procedia Computer Science. 2016;86:116-119. doi:10.1016/j.procs.2016.05.029. CR - 16. Kara S. Model predictive trajectory tracking control of 2 DOF SCARA robot under external force acting to the tip along the trajectory. DÜMF Mühendislik Dergisi. 2023. doi:10.24012/dumf.1289356. CR - 17. Tay S, Choong W, Yoong H. A review of SCARA robot control system. Proceedings of the IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET). 2022. doi:10.1109/iicaiet55139.2022.9936755. CR - 18. Farrage A, Sharkawy A, Ali A, Soliman M, Mohamed H. Experimental investigation of an adaptive neuro-fuzzy control scheme for industrial robots. JES Journal of Engineering Sciences. 2014;42(3):703-721. doi:10.21608/jesaun.2014.115023. CR - 19. Popov V, Ahmed S, Shakev N, Topalov A. Gesture-based interface for real-time control of a Mitsubishi SCARA robot manipulator. IFAC-PapersOnline. 2019;52(25):180-185. doi:10.1016/j.ifacol.2019.12.469. CR - 20. Adelhedi F, Jribi A, Bouteraa Y, Derbel N. Adaptive sliding mode control design of a SCARA robot manipulator system under parametric variations. Journal of Engineering Science and Technology Review. 2015;8(5):117-123. doi:10.25103/jestr.085.16. CR - 21. Kuwahara M, Hachimura K, Eiho S, Kinoshita M. Processing of RI-Angiocardiographic Images. In: Preston K Jr, Onoe M, editors. Digital Processing of Biomedical Images. New York: Plenum Press; 1976. p. 187–203. doi:10.1007/978-1-4684-0769-3_13. UR - https://doi.org/10.17350/HJSE19030000357 L1 - https://dergipark.org.tr/en/download/article-file/4623817 ER -