- Titel
- An AI-based collaborative Robot System for Technical Education
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:l189-qucosa2-896785
- Konferenz
- 20. AALE-Konferenz. Bielefeld, 06.03.-08.03.2024
- Quellenangabe
- Tagungsband AALE 2024
Herausgeber: Hochschule für Technik, Wirtschaft und Kultur Leipzig
Erscheinungsort: Leipzig
Erscheinungsjahr: 2024
ISBN: 978-3-910103-02-3 - Erstveröffentlichung
- 2024
- DOI
- https://doi.org/10.33968/2024.41
- Abstract (EN)
- In this paper a cobot system is presented, that extends a Universal Robot with Artificial Intelligence (i.e., machine learning techniques) to allow for a safe human-robot collaboration, which is one of the main technologies in Industry 4.0 and is currently significantly changing the shop floor of manufacturing companies. Typically, these cobots are equipped with a camera to dynamically adapt to new situations and actions carried out by the worker who is collaborating with the robot in the same workspace. But obviously, switching from traditional industrial robots (acting completely isolated from humans) to smart robots also requires a change concerning the skills and knowledge workers must have to be able to control, manage, and interact with such cobot systems. Therefore, the main goal of this demonstrator is to develop a hard- and software environment, enabling a variety of different training scenarios to get trainees, employees, and students familiar with the main technical aspects of such human-robot interaction. Besides hardware and software related aspects, the paper will also briefly address the learning content, which is on the one hand, the basics of robotics and machine learning based image processing, and on the other hand, the interaction of the various components to form a functional overall system.
- Freie Schlagwörter (EN)
- Cobots, Machine Learning, Human-Machine Collaboration, Technical Education
- Herausgeber (Institution)
- Hochschule für Technik, Wirtschaft und Kultur Leipzig
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:l189-qucosa2-896785
- Veröffentlichungsdatum Qucosa
- 12.02.2024
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0