Calibration of the Robotic Arm with Corrections using Local Linear Neuro-fuzzy Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F22%3A00362039" target="_blank" >RIV/68407700:21220/22:00362039 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.17973/MMSJ.2022_12_2022160" target="_blank" >https://doi.org/10.17973/MMSJ.2022_12_2022160</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17973/MMSJ.2022_12_2022160" target="_blank" >10.17973/MMSJ.2022_12_2022160</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Calibration of the Robotic Arm with Corrections using Local Linear Neuro-fuzzy Models

Popis výsledku v původním jazyce

The paper deals with the enhancement of the robotic arm calibration using corrections based on local linear neuro-fuzzy models. After the standard calibration of the geometric parameters in the robot's kinematic model, there are still residual errors between the measured positions and the positions predicted by the model. The source of these errors are various non-geometric parameters and nonlinear phenomena that traditional kinematic calibration models do not include. The neuro-fuzzy model based on a locally linear model tree can approximate the residual error as a function of the robot's joint angles. Adding this approximation to the output of the calibrated robot model significantly increases the accuracy of the end-effector position. The results of the described method were verified and compared with other approaches on a simulation model of a flexible planar two-link mechanism. Experimental verification was performed on an industrial robot Stäubli TX200 with data measured by Leica laser tracking device.

Název v anglickém jazyce

Calibration of the Robotic Arm with Corrections using Local Linear Neuro-fuzzy Models

Popis výsledku anglicky

The paper deals with the enhancement of the robotic arm calibration using corrections based on local linear neuro-fuzzy models. After the standard calibration of the geometric parameters in the robot's kinematic model, there are still residual errors between the measured positions and the positions predicted by the model. The source of these errors are various non-geometric parameters and nonlinear phenomena that traditional kinematic calibration models do not include. The neuro-fuzzy model based on a locally linear model tree can approximate the residual error as a function of the robot's joint angles. Adding this approximation to the output of the calibrated robot model significantly increases the accuracy of the end-effector position. The results of the described method were verified and compared with other approaches on a simulation model of a flexible planar two-link mechanism. Experimental verification was performed on an industrial robot Stäubli TX200 with data measured by Leica laser tracking device.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20302 - Applied mechanics

Projekt

<a href="/cs/project/EF16_026%2F0008404" target="_blank" >EF16_026/0008404: Strojírenská výrobní technika a přesné strojírenství</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

MM Science Journal

ISSN

1803-1269

e-ISSN

1805-0476

Svazek periodika

2022

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

8

Strana od-do

6225-6232

Kód UT WoS článku

000898328400001

EID výsledku v databázi Scopus

2-s2.0-85144060748