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    Design and Vision-Based Control of a Low-Cost SCARA Robot
    (2025) Adar, Nurettin Gökhan; Özden, Mustafa
    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.
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    Development of Seam Tracking Sensor in ROS Environment
    (Institute of Electrical and Electronics Engineers Inc., 2025) Yildiz, Ali Riza; Adar, Nurettin Gökhan
    Seam tracking sensors are critical for improving the accuracy and adaptability of robotic welding systems. In this study, a vision-based seam tracking sensor was developed and simulated entirely within the Robot Operating System (ROS) and Gazebo environment. A structured light sensor, consisting of a line laser and a camera, was modeled to detect weld seam positions through laser triangulation. Real-time image processing algorithms were implemented to extract seam coordinates, while trajectory planning and motion control modules enabled the Cartesian robot to dynamically adjust its path during operation. The developed system architecture ensured synchronized communication between sensor data acquisition and robot control layers. Simulation results demonstrate that the proposed system achieves accurate and responsive seam tracking under varying seam deviations. The study highlights the potential of simulation-driven development for validating sensor designs and control strategies in a risk-free and cost-effective manner before physical deployment. © 2025 IEEE.
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    Image-Based Control of 2-DOF Ball Balancing System
    (Bursa Teknik Üniversitesi, 2022) Gürsoy, Hüseyin Can; Adar, Nurettin Gökhan
    In this study, the Ball-Plate stabilization system is designed to control with image processing algorithms. The position of the ball is aimed to control by tilting the plate on which the ball is located at a certain position and velocity. The system has two rotational degrees of freedom and is unstable. In the system, two DC motors are used as an actuator, and a camera is used as a feedback sensor. The camera captures the position of the ball and image processing algorithm calculates the that position to blance the plate..PID control is selected for servo motors. Thus, the position of the ball can be controlled so that it converges to the desired point on the plate. Real-time tests are conducted, and Maximum Overshoot and Steady State Error are calculated for both the x and y-axis, and results are given in figures. For the setpoint (15 cm, 15 cm) the Maximum Overshoot and Steady State Error were measured at 40.6% - 8% on the x-axis and 48.6% - 8.6% on the y-axis, while for the setpoint (10 cm, 10 cm) The Maximum Overshoot and Steady State Error were measured at 40.6% - 8% on the x-axis and 48.6% - 8.6% on the y axis.
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    Prediction of Displacement and Stress Values of Composite Materials Under Load with Machine Learning Models
    (Osman SAĞDIÇ, 2022) Ferati, Kajs; Adar, Nurettin Gökhan
    In this study, the determination of displacement and stress values under certain load of glass fiber and epoxy resin laminated reinforced composite materials by using machine learning models is targeted. In the scope of study, the modelling is done by changing the material properties of varied laminations of composite samples via Ansys software and a tensile force is implemented in order to receive the total deformation and Von Misses stresses under the implemented tensile force and creation of the dataset is completed. The robust linear regression and Gaussian process regression models from machine learning algorithms are used to predict and determine the total deformation and Von Misses stresses by training and testing the models with the dataset created. As result, the predicted values obtained from trained and tested regression models and the real values obtained by modelling in Ansys are compared. Additionally, in consideration of model parameters for both regression models, the evaluation of true responses and correct prediction/determination is done. According to the results, Gaussian process regression model is determined as a better model for related study.
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    Real Time Control Application of the Robotic Arm Using Neural Network Based Inverse Kinematics Solution
    (Sakarya University, 2021) Adar, Nurettin Gökhan
    Robotic arms are widely used in many industrial applications at present. The control of robotic arms involves position coordination Cartesian space by a forward/inverse kinematics solution method. The inverse kinematics is difficult for real-time control applications, computational requirements are intensive and the run-time is high. The traditional solution methods used geometric, algebraic, and numerical iterative techniques are inadequate and slow in the inverse kinematics solution. Recently, alternative solution methods based on artificial intelligence techniques have been developed to solve the inverse kinematics problem. In this study, a multi-layered feed-forward Artificial Neural Network model was developed to solve the inverse kinematics of the 5 degrees of freedom robotic arm. Using the Proportional-Integral control algorithm combined with this Artificial Neural Network model, the real-time position control of the robotic arm was accomplished. The obtained results were compared with the PI control supported by an analytical inverse kinematics solution in real-time. The results showed that the PI control combined with Artificial Neural Network has superior tracking ability, smaller control error, and better absolute fit to the reference.
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    Real-time vision-based grasping randomly placed object by low-cost robotic arm using surf algorithm
    (IOP Publishing Ltd, 2020) Beyhan, Ayberk; Adar, Nurettin Gökhan
    Vision-Based Manipulation is popular and still have open issues in robotics. The camera is a very important part of this method to obtain desired data with image processing techniques. In this study, the Dynamic position-based look and move method was selected to control the 4 DOF robotic arm. For this method, the Kinect camera was used for image processing. Kinect is a special camera which consists of both RGB and infrared camera. SURF algorithm was selected to detect a target object from the target scene using Kinect RGB camera. 3-D target object localization was calculated using Kinect infrared camera with the point cloud. The obtained target object's location is according to the camera and transformed according to the robotic arm base. Using inverse kinematics, desired joint angles were calculated according to the object position. Therefore, the robot is provided to make the desired motion and grasping the object by using gripper. Real-time implementation of the proposed method is carried out using Matlab-Simulink. © 2020 Institute of Physics Publishing. All rights reserved.