Applied Sciences (Sep 2022)

PickingDK: A Framework for Industrial Bin-Picking Applications

  • Marco Ojer,
  • Xiao Lin,
  • Antonio Tammaro,
  • Jairo R. Sanchez

DOI
https://doi.org/10.3390/app12189200
Journal volume & issue
Vol. 12, no. 18
p. 9200

Abstract

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This work presents an industrial bin-picking framework for robotics called PickingDK. The proposed framework employs a plugin based architecture, which allows it to integrate different types of sensors, robots, tools, and available open-source software and state-of-the-art methods. It standardizes the bin-picking process with a unified workflow based on generally defined plugin interfaces, which promises the hybridization of functional/virtual plugins for fast prototyping and proof-of-concept. It also offers different levels of controls according to the user’s expertise. The presented use cases demonstrate flexibility when building bin-picking applications under PickingDK framework and the convenience of exploiting hybrid style prototypes for evaluating specific steps in a bin-picking system, such as parameter fine-tuning and picking cell design.

Keywords