Detail publikace

Robotic platform equipped with machine learning

KACZMARCZYK, V. BAŠTÁN, O. HUSÁK, M. BENEŠL, T. BRADÁČ, Z.

Originální název

Robotic platform equipped with machine learning

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Automatic lines equipped with stationary robots are a key element of the industry. The robots are integrated into production lines, to meet basic, repetitive operations, with a finite degree of variability in internal programs. Reprogramming in terms of, for example, changing a manufactured, manipulated part is time-consuming and cost-effective. However, the development of today's machine learning algorithms is only carefully integrated in this market segment. Manufacturers do not provide their closed systems with a sufficient degree of programming variability. The solution tries to outline this work, which complements the standard industrial robot with a cognitive interface. Such a robot is able to learn new programs and make production changes on the fly.

Klíčová slova

Industrial robotics, Industrial communication, Machine learning, Virtual commissioning, Fanuc, TensorFlow, Object detection

Autoři

KACZMARCZYK, V.; BAŠTÁN, O.; HUSÁK, M.; BENEŠL, T.; BRADÁČ, Z.

Vydáno

20. 5. 2022

Nakladatel

Elsevier

Místo

Sarajevo

ISSN

2405-8963

Periodikum

IFAC-PapersOnLine (ELSEVIER)

Ročník

55

Číslo

4

Stát

Nizozemsko

Strany od

380

Strany do

386

Strany počet

6

URL

https://www.sciencedirect.com/science/article/pii/S2405896322003780

BibTex

@inproceedings{BUT177979,
  author="Václav {Kaczmarczyk} and Ondřej {Baštán} and Michal {Husák} and Tomáš {Benešl} and Zdeněk {Bradáč}",
  title="Robotic platform equipped with machine learning",
  booktitle="17th IFAC INTERNATIONAL CONFERENCE on PROGRAMMABLE DEVICES and EMBEDDED SYSTEMS - PDeS 2022",
  year="2022",
  journal="IFAC-PapersOnLine (ELSEVIER)",
  volume="55",
  number="4",
  pages="380--386",
  publisher="Elsevier",
  address="Sarajevo",
  doi="10.1016/j.ifacol.2022.06.063",
  issn="2405-8963",
  url="https://www.sciencedirect.com/science/article/pii/S2405896322003780"
}