• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Artikel
  4. Separating Entangled Workpieces in Random Bin Picking using Deep Reinforcement Learning
 
  • Details
  • Full
Options
2021
Journal Article
Title

Separating Entangled Workpieces in Random Bin Picking using Deep Reinforcement Learning

Abstract
Entangled workpiece situations often occur in random bin picking of chaotically stored objects and are a common source of problem in the bin picking process. Previous methods for averting this problem, such as randomly shaking the gripper over the bin, lead to decreasing production efficiency and an increase in cycle time. A promising new strategy uses supervised learning and deep neural networks to learn the separation. However, this approach requires a large amount of labeled data. To overcome this issue, this paper proposes a deep reinforcement learning approach to separate entangled workpieces and to minimize the setup effort.
Author(s)
Moosmann, Marius  
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA  
Kulig, Marco
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA  
Spenrath, Felix  
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA  
Mönnig, Manuel  
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA  
Roggendorf, Simon
RWTH Aachen
Petrovic, Oliver
RWTH Aachen
Bormann, Richard  
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA  
Huber, Marco  
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA  
Journal
Procedia CIRP  
Conference
Conference on Manufacturing Systems (CMS) 2021  
Open Access
DOI
10.1016/j.procir.2021.11.148
Language
English
Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA  
Keyword(s)
  • bin-picking

  • deep learning

  • Bestärkendes Lernen

  • deep grasping

  • maschinelles Lernen

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024