PickingDK: A Framework for Industrial Bin-Picking Applications

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 t...

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Hlavní autoři: Marco Ojer, Xiao Lin, Antonio Tammaro, Jairo R. Sanchez
Médium: Článek
Jazyk:English
Vydáno: MDPI AG 2022-09-01
Edice:Applied Sciences
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On-line přístup:https://www.mdpi.com/2076-3417/12/18/9200
Popis
Shrnutí: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.
ISSN:2076-3417