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.
K.M. el Dine and J. Sanchez—These authors contributed equally to the work.
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Note that for simplicity, when the subscripts are not indicated the quantities are assumed to be of the end-effector expressed on the robot frame.
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Acknowledgements
Kamal Mohy El Dine works for the European project Robots to Re-Construction (Bots2ReC). His research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programmes under grant agreement No. 687593.
Jose Sanchez is sponsored by the French government research program Investissements d’Avenir through the RobotEx Equipment of Excellence (ANR-10-EQPX-44) and the IMobS3 Laboratory of Excellence (ANR-10-LABX-16-01), by the European Union through the program Regional competitiveness and employment 2007–2013 (ERDF - Auvergne region) and by the Auvergne region.
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el Dine, K.M., Sanchez, J., Corrales, J.A., Mezouar, Y., Fauroux, JC. (2020). Force-Torque Sensor Disturbance Observer Using Deep Learning. In: Xiao, J., Kröger, T., Khatib, O. (eds) Proceedings of the 2018 International Symposium on Experimental Robotics. ISER 2018. Springer Proceedings in Advanced Robotics, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-33950-0_32
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