Browsing by Author "Hamdan, Sara"

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    Force reference extraction via human interaction for a robotic polishing task: Force-induced motion
    (IEEE, 2019-10) Hamdan, Sara; Öztop, Erhan; Uğurlu, Regaip Barkan; Computer Science; Mechanical Engineering; ÖZTOP, Erhan; UĞURLU, Regaip Barkan; Hamdan, Sara
    In this paper, a method to control a manipulator using force-induced trajectory is proposed. The trajectory is learned from an operator doing the polishing task using a tool attached to the robot's end-effector. The learning process is performed by a deep neural network which is designed and trained to generate a force profile according to the states (joints' positions and velocities). The admittance control technique is utilized to make the manipulator compliant to the operator movements in the teaching mode. Spring-Damper system along with Inertia-Damper system has been studied to impose the relationship between the operator's applied force and the reaction of the manipulator. The universal robot (UR5) aside with a force sensor (OptoForce) are used to run the experiment. Robot Operation System (ROS) is used to accomplish the task in real-time. The polishing task is learned and achieved by the robot itself, and the force trajectories are better followed using the Inertia-Damper system as the admittance controlling scheme.
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    Shoulder glenohumeral elevation estimation based on upper arm orientation
    (IEEE, 2018-10-26) Hamdan, Sara; Öztop, Erhan; Furukawa, J.-I.; Morimoto, J.; Uğurlu, Regaip Barkan; Computer Science; Mechanical Engineering; ÖZTOP, Erhan; UĞURLU, Regaip Barkan; Hamdan, Sara
    In this paper, the shoulder glenohumeral displacement during the movement of the upper arm is studied. Four modeling approaches were examined and compared to estimate the humeral head elevation (vertical displacement) and translation (horizontal displacement). A biomechanics-inspired method was used firstly to model the glenohumeral displacement in which a least squares method was implemented for parameter identification. Then, three Gaussian process regression models were used in which the following variable sets were employed: i) shoulder adduction/abduction angle, ii) combination of shoulder adduction/abduction and flexion/extension angles, iii) overall upper arm orientation in the form of quaternions. In order to test the respective performances of these four models, we collected motion capture data and compared the models' representative capabilities. As a result, Gaussian process regression that considered the overall upper arm orientation outperformed the other modeling approaches; however, it should be noted that the other methods also provided accuracy levels that may be sufficient depending on task requirements.