Estimating Robot Manipulator End-effector Forces using Deep Learning (CROSBI ID 691709)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Kružić, Stanko ; Musić, Josip ; Kamnik, Roman ; Papić, Vladan
engleski
Estimating Robot Manipulator End-effector Forces using Deep Learning
The measurement of the robotic manipulator end- effector interaction forces can in certain cases be challenging, especially when using robots that have a small payload (and consequently not capable of using wrist-mounted force sensor), which is often case with educational robots. In the paper, a method for estimation of end- effector forces using measurements from the base- mounted force sensor and deep neural networks is presented. Several deep architectures were trained using data collected on real 6-DOF robot manipulator (Commonplace Robotics Mover6 robot) using custom-made interaction object operated by a human. The obtained results show that when using appropriate deep architecture promising estimates can be achieved (with an RMSE metric on test set which was 16%, 12% and 6% of maximum force in respective directions of x, y and z axes). This makes this approach suitable for use in a variety of applications, including but not limited to usage with haptic feedback interfaces for robot control.
robotics, force estimation, deep learning
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Podaci o prilogu
1411-1416.
2020.
objavljeno
Podaci o matičnoj publikaciji
Skala, Karolj
Opatija: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO
1847-3938
1847-3946
Podaci o skupu
MIPRO 2020
predavanje
28.09.2020-02.10.2020
Opatija, Hrvatska