Assessment of Robotic Picking Operations Using a 6 Axis Force/Torque Sensor Moreira,E en Luís Freitas Rocha en Andry Maykol Pinto en António Paulo Moreira en Germano Veiga en 2017-12-27T16:26:02Z 2017-12-27T16:26:02Z 2016 en
dc.description.abstract This letter presents a novel architecture for evaluating the success of picking operations that are executed by industrial robots. It is formed by a cascade of machine learning algorithms (kNN and SVM) and uses information obtained by a 6 axis force/torque sensor and, if available, information from the built-in sensors of the robotic gripper. Beyond measuring the success or failure of the entire operation, this architecture makes it possible to detect in real-time when an object is slipping during the picking. Therefore, force and torque signatures are collected during the picking movement of the robot, which is decomposed into five different stages that allows to characterize distinct levels of success over time. Several trials were performed using an industrial robot with two different grippers for picking a long and flexible object. The experiments demonstrate the reliability of the proposed approach under different picking scenarios since, it obtained a testing performance (in terms of accuracy) up to 99.5% of successful identification of the result of the picking operations, considering an universe of 400 attempts. © 2016 IEEE. en
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dc.language eng en
dc.relation 5364 en
dc.relation 5446 en
dc.relation 5157 en
dc.relation 5674 en
dc.rights info:eu-repo/semantics/embargoedAccess en
dc.title Assessment of Robotic Picking Operations Using a 6 Axis Force/Torque Sensor en
dc.type article en
dc.type Publication en
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