Method for estimating the pose of a robotic arm using a camera and calibration pattern

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68145535%3A_____%2F25%3A00638562" target="_blank" >RIV/68145535:_____/25:00638562 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27240/25:10258036

Výsledek na webu

<a href="https://doi.org/10.1016/j.asej.2025.103525" target="_blank" >https://doi.org/10.1016/j.asej.2025.103525</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asej.2025.103525" target="_blank" >10.1016/j.asej.2025.103525</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Method for estimating the pose of a robotic arm using a camera and calibration pattern

Popis výsledku v původním jazyce

Industrial robots are a key component of Industry 4.0, yet accurately estimating their pose remains challenging- especially when determining the spatial relationship between the tool center point (TCP) and the working space. This study presents a hybrid pose estimation method that leverages an industrial camera mounted on a robotic arm's effector and a calibration pattern positioned within the working frame. To solve the resulting optimization problem, the method integrates Newton's method with a neural network (NN) pre-trained on a full camera model. Comparative experiments with state-of-the-art optimization techniques show that the proposed approach achieves superior performance in terms of both accuracy and speed. Specifically, it yields a mean position error of 0.5 mm and a mean angle error of 0.31 degrees, with a computation time of 0.14 ms. These results suggest that the method offers an efficient and accurate alternative for camera-based pose estimation in industrial settings.

Název v anglickém jazyce

Method for estimating the pose of a robotic arm using a camera and calibration pattern

Popis výsledku anglicky

Industrial robots are a key component of Industry 4.0, yet accurately estimating their pose remains challenging- especially when determining the spatial relationship between the tool center point (TCP) and the working space. This study presents a hybrid pose estimation method that leverages an industrial camera mounted on a robotic arm's effector and a calibration pattern positioned within the working frame. To solve the resulting optimization problem, the method integrates Newton's method with a neural network (NN) pre-trained on a full camera model. Comparative experiments with state-of-the-art optimization techniques show that the proposed approach achieves superior performance in terms of both accuracy and speed. Specifically, it yields a mean position error of 0.5 mm and a mean angle error of 0.31 degrees, with a computation time of 0.14 ms. These results suggest that the method offers an efficient and accurate alternative for camera-based pose estimation in industrial settings.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Centrum výzkumu pokročilých mechatronických systémů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Ain Shams Engineering Journal

ISSN

2090-4479

e-ISSN

2090-4495

Svazek periodika

16

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

103525

Kód UT WoS článku

001513404000002

EID výsledku v databázi Scopus

2-s2.0-105007532866