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PickingDK: A Framework for Industrial Bin-Picking Applications

Marco Ojer, Xiao Lin, Antonio Tammaro, Jairo R. Sánchez

Year
2022
Citations
6
Access
Open access

Abstract

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

Plug-inComputer scienceWorkflowSoftware engineeringRoboticsBinRobotFlexibility (engineering)ArchitectureArtificial intelligence

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