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. ; European Union (EU) ; H2020 project CoMRAde


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    Titel :

    Force reference extraction via human interaction for a robotic polishing task: Force-induced motion



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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