Browsing by Author "Kaya, Ozan"

Filter results by typing the first few letters
Now showing 1 - 4 of 4
  • Thumbnail Image
    Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Humanoid Robot Arm Design, Simulation, Kinesthetic Learning, Impedance Control and Suggestions
    (Gazi Univ, Fac Engineering Architecture, 2022) Ertugrul, Seniz; Kaya, Ozan; Turkmen, Dila; Eraslan, Hulya; Taglioglu, Gokce Burak; Gulec, Musa Ozgun
    Robot technology is constantly developing and the studies in this field are also increasing in our country. Universities, machine-manufacturing and defense industry have been either doing or planning robot projects. This study presents designing of a humanoid robot arm desired to be cooperative so that it can work as dual arm or with human operator. Mechanical design, kinematic and dynamic analysis, kinesthetic learning, impedance control, software and hardware studies were carried out within the scope of the study. The stages from the initial design of the humanoid robot arm to the control, the problems encountered, the experiences gained and the suggestions for advanced designs are shared in a very comprehensive way in this article. It has been explained in an easy-to-understand manner in order to be useful for national robot projects which are being developed especially for commercial purposes. Mechanical design, dynamic analyses, simulation and other files will be shared as open source with interested researchers.
  • Thumbnail Image
    Conference Object
    Non-Linear Friction Force Estimation for Ball and Beam Mechanism Using R-Pinn
    (IEEE, 2025) Kaya, Ozan; Ertugrul, Seniz; Abedinifar, Masoud; Egeland, Olav
    Different friction forces or torques are affecting the system's performance and control. Friction forces occur due to bearings, gearboxes, or any other contacts in the system. Researchers have reported different forms of friction, such as stiction, viscous and Stribeck effects, pre-sliding displacement, stick-slip effects, hysteresis (or frictional lag), etc. Developing a mathematical model to describe the underlying dynamics of a complex system may become necessary to design either a modelbased controller or at least compensate for the non-linear effects of friction forces. For this reason, either test set-ups or datadriven techniques might be used. In this study, the RecurrentPhysics Informed Neural Network is studied to determine the friction forces and model the Ball and beam system. While PINN provides faster results to model non-linear systems with noisy and small data sizes, Recurrent Neural Network architecture is fruitful for modeling time-dependent systems. Thus, R-PINN is trained with noisy signals for system response and friction model of the ball and beam system. Despite noisy signals and nonlinearity in the system, R-PINN is promising in modeling the system response and estimating the friction model.
  • Thumbnail Image
    Article
    Citation - WoS: 7
    Citation - Scopus: 7
    The Series Elastic Gripper Design, Object Detection, and Recognition by Touch
    (Asme, 2022) Kaya, Ozan; Taglioglu, Gokce Burak; Ertugrul, Seniz
    In recent years, robotic applications have been improved for better object manipulation and collaboration with human. With this motivation, the detection of objects has been studied with a series elastic parallel gripper by simple touching in case of no visual data available. A series elastic gripper, capable of detecting geometric properties of objects, is designed using only elastic elements and absolute encoders instead of tactile or force/torque sensors. The external force calculation is achieved by employing an estimation algorithm. Different objects are selected for trials for recognition. A deep neural network (DNN) model is trained by synthetic data extracted from standard tessellation language (STL) file of selected objects. For experimental setup, the series elastic parallel gripper is mounted on a Staubli RX160 robot arm and objects are placed in pre-determined locations in the workspace. All objects are successfully recognized using the gripper, force estimation, and the DNN model. The best DNN model is capable of recognizing different objects with the average prediction value ranging from 71% to 98%. Hence, the proposed design of the gripper and the algorithm achieved the recognition of selected objects without the need for additional force/torque or tactile sensors.
  • Thumbnail Image
    Patent