Force-Torque Sensor Disturbance Observer using Deep Learning

Abstract : Robots executing force controlled tasks require accurate perception of the applied force in order to guarantee precision. However, dynamic motions generate non-contact forces due to the inertia. These non-contact forces can be regarded as disturbances to be removed such that only the forces generated by contacts with the environment remain. This paper presents an observer based on a recurrent neural network that estimates the non-contact forces measured by a force-torque sensor attached at the end-effector of a robotic arm. The approach is proven to also work with an external load attached to the robotic arm. The recurrent neural network observer uses signals from the joint encoders of the robotic arm and a low-cost inertial measurement unit to estimate the wrenches (i.e. forces and torques) generated due to gravity, inertia, centrifugal and Coriolis forces. The accuracy of the proposed observer is experimentally evaluated by comparing the measurements of the attached force-torque sensor to the observer's non-contact forces estimation. Additionally, the pure contact force estimation is evaluated against an external force-torque sensor.
Type de document :
Communication dans un congrès
2018 International Symposium on Experimental Robotics (ISER 2018), Nov 2018, Buenos Aires, Argentina. 〈http://iser2018.org/〉
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https://hal.archives-ouvertes.fr/hal-01921495
Contributeur : Jose Manuel Sanchez Loza <>
Soumis le : mardi 13 novembre 2018 - 18:53:44
Dernière modification le : mardi 27 novembre 2018 - 01:20:56

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iser2018.pdf
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  • HAL Id : hal-01921495, version 1

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Kamal Mohy El Dine, Jose Sanchez, Juan Corrales, Youcef Mezouar, Jean-Christophe Fauroux. Force-Torque Sensor Disturbance Observer using Deep Learning. 2018 International Symposium on Experimental Robotics (ISER 2018), Nov 2018, Buenos Aires, Argentina. 〈http://iser2018.org/〉. 〈hal-01921495〉

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