Evolutionary algorithms in robot calibration

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F23%3A00373270" target="_blank" >RIV/68407700:21220/23:00373270 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1080/10426914.2023.2238368" target="_blank" >https://doi.org/10.1080/10426914.2023.2238368</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/10426914.2023.2238368" target="_blank" >10.1080/10426914.2023.2238368</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evolutionary algorithms in robot calibration

Popis výsledku v původním jazyce

Robots are universal mechanical systems that are now ubiquitous in manufacturing. One of the most important properties of industrial robots is their kinematic accuracy. Robot's accuracy is influenced by many factors including manufacture accuracy of mechanical parts and other aspects. Calibration is a technique that allows to identify design and other parameters of the robot to achieve its highest accuracy. There are widely used traditional kinematic calibration methods based on kinematic models of the robot. Simulation is used to compare results of traditional calibration method and a newly developed method based on multi-objective deep learning evolutionary algorithm. EvoDN2 was used together with a reference vector-based evolutionary algorithm, cRVEA, used for optimization, in order to find optimal estimates of the robot parameters. It is well known that the evalutionary algorithms are capable of dealing with noisy data from measurement. Results and comparison of both techniques are discussed and evaluated.

Název v anglickém jazyce

Evolutionary algorithms in robot calibration

Popis výsledku anglicky

Robots are universal mechanical systems that are now ubiquitous in manufacturing. One of the most important properties of industrial robots is their kinematic accuracy. Robot's accuracy is influenced by many factors including manufacture accuracy of mechanical parts and other aspects. Calibration is a technique that allows to identify design and other parameters of the robot to achieve its highest accuracy. There are widely used traditional kinematic calibration methods based on kinematic models of the robot. Simulation is used to compare results of traditional calibration method and a newly developed method based on multi-objective deep learning evolutionary algorithm. EvoDN2 was used together with a reference vector-based evolutionary algorithm, cRVEA, used for optimization, in order to find optimal estimates of the robot parameters. It is well known that the evalutionary algorithms are capable of dealing with noisy data from measurement. Results and comparison of both techniques are discussed and evaluated.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20302 - Applied mechanics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Materials and Manufacturing Processes

ISSN

1042-6914

e-ISSN

1532-2475

Svazek periodika

38

Číslo periodika v rámci svazku

16

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

20

Strana od-do

2051-2070

Kód UT WoS článku

001048238000001

EID výsledku v databázi Scopus

2-s2.0-85166940363