Rapid 2D Positioning of Multiple Complex Objects for Pick and Place Application Using Convolutional Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F20%3A39916799" target="_blank" >RIV/00216275:25530/20:39916799 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICSTCC50638.2020.9259696" target="_blank" >http://dx.doi.org/10.1109/ICSTCC50638.2020.9259696</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICSTCC50638.2020.9259696" target="_blank" >10.1109/ICSTCC50638.2020.9259696</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Rapid 2D Positioning of Multiple Complex Objects for Pick and Place Application Using Convolutional Neural Network

Popis výsledku v původním jazyce

Robot guidance in an industrial environment is an important task to be solved in modern production facilities. A pick and place task is definitely one of the most common robot guidance issues to solve. In the beginning of the pick and place task, we need to perform a precise positioning of the objects of interest. In this contribution, an innovative engineering approach to multiple object positioning is proposed. The approach consists of two consecutive steps. At first, the original scene with objects of interest is transformed using a neural network. The output of this transformation is a schematic image, which represents the positions of the objects with gradient circles of various colors. Then, the positions of the gradient circles are determined by finding local maxima in the transformed image. The proposed approach is tested on a legitimate positioning problem with more than 99.8 % accuracy.

Název v anglickém jazyce

Rapid 2D Positioning of Multiple Complex Objects for Pick and Place Application Using Convolutional Neural Network

Popis výsledku anglicky

Robot guidance in an industrial environment is an important task to be solved in modern production facilities. A pick and place task is definitely one of the most common robot guidance issues to solve. In the beginning of the pick and place task, we need to perform a precise positioning of the objects of interest. In this contribution, an innovative engineering approach to multiple object positioning is proposed. The approach consists of two consecutive steps. At first, the original scene with objects of interest is transformed using a neural network. The output of this transformation is a schematic image, which represents the positions of the objects with gradient circles of various colors. Then, the positions of the gradient circles are determined by finding local maxima in the transformed image. The proposed approach is tested on a legitimate positioning problem with more than 99.8 % accuracy.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Spolupráce Univerzity Pardubice a aplikační sféry v aplikačně orientovaném výzkumu lokačních, detekčních a simulačních systémů pro dopravní a přepravní procesy (PosiTrans)</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

24th international conference on system theory, control and computing, ICSTCC 2020 : proceedings

ISBN

978-1-72819-809-5

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

213-217

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

New York

Místo konání akce

ONLINE

Datum konání akce

8. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—