Publication detail

Robotic platform equipped with machine learning

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

Original Title

Robotic platform equipped with machine learning

Type

conference paper

Language

English

Original Abstract

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.

Keywords

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

Authors

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

Released

20. 5. 2022

Publisher

Elsevier

Location

Sarajevo

ISBN

2405-8963

Periodical

IFAC-PapersOnLine (ELSEVIER)

Year of study

55

Number

4

State

Kingdom of the Netherlands

Pages from

380

Pages to

386

Pages count

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"
}