Konferenzbeitrag

Robot-human-learning for robotic picking processes

Purpose: This research paper aims to create an environment which enables robots to learn from humans by algorithms of Computer Vision and Machine Learning for object detection and gripping. The proposed concept transforms manual picking to highly automated picking performed by robots. Methodology: After defining requirements for a robotic picking system, a process model is proposed. This model defines how to extend traditional manual picking and which human-robot-interfaces are necessary to enable learning from humans to improve the performance of robots' object detection and gripping. Findings: The proposed concept needs a pool of images to train an initial setup of a convolutional neural network by the YOLO-Algorithm. Therefore, a station with two cameras and a flexible positioning system for image creation is presented by which the necessary number of images can be generated with little effort. Originality: A digital representation of an object is created based on the generated images of this station. The original idea is a feedback loop including human workers after a not successful object detection or gripping which enables robots in service to extend their ability to recognize and pick objects.

Sprache
Englisch

Erschienen in
10419/209196

Klassifikation
Informatik
Thema
Picking robots
Machine learning
Object detection
Computer vision
Human-robot-collaboration

Ereignis
Geistige Schöpfung
(wer)
Rieder, Mathias
Verbeet, Richard
Ereignis
Veröffentlichung
(wer)
epubli GmbH
(wo)
Berlin
(wann)
2019

DOI
doi:10.15480/882.2466
Handle
URN
urn:nbn:de:gbv:830-882.054127
Letzte Aktualisierung
20.09.2024, 08:21 MESZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Konferenzbeitrag

Beteiligte

  • Rieder, Mathias
  • Verbeet, Richard
  • epubli GmbH

Entstanden

  • 2019

Ähnliche Objekte (12)