Naslov
Estimating Robot Manipulator End-effector Forces using Deep Learning

Autori
Kružić, Stanko ; Musić, Josip ; Kamnik, Roman ; Papić, Vladan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
MIPRO 2020 43rd International Convention - Proceedings / Skala, Karolj - Opatija : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2020, 1411-1416

Skup
43rd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2020)

Mjesto i datum
Opatija, Hrvatska, 28.09.2020. - 02.10.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
robotics, force estimation, deep learning

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo

POVEZANOST RADA