Virtual training and commissioning of industrial bin picking systems using synthetic sensor data and simulation (IMS 2019)

Metzner M, Albrecht F, Fiegert M, Bauer B, Martin S, Karlidag E, Blank A, Franke J (2021)


Publication Type: Journal article

Publication year: 2021

Journal

DOI: 10.1080/0951192X.2021.2004618

Abstract

Defined handling of unsorted parts, known as bin picking, is a challenge in robotic automation. Available solution concepts for this problem are usually either costly or require considerable setup and tuning efforts. In this contribution, a setup for virtual commissioning of such automation systems is introduced. Using a physics-based simulation environment, a virtual stereo-camera simulation and robot controller integration, a full simulation of the bin picking cycle is possible. The setup is also used to generate realistic synthetic training data for learning-based computer vision routines. The functionality of the system is demonstrated for generating training data capable of enabling a real-life deployment of the pipeline. A simulation of both model-based and learning-based bin picking systems is also conducted. This simulation also involves the path planning and execution as well as the grasp itself, allowing for a full simulation of the bin picking cycle.

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How to cite

APA:

Metzner, M., Albrecht, F., Fiegert, M., Bauer, B., Martin, S., Karlidag, E.,... Franke, J. (2021). Virtual training and commissioning of industrial bin picking systems using synthetic sensor data and simulation (IMS 2019). International Journal of Computer Integrated Manufacturing. https://doi.org/10.1080/0951192X.2021.2004618

MLA:

Metzner, Maximilian, et al. "Virtual training and commissioning of industrial bin picking systems using synthetic sensor data and simulation (IMS 2019)." International Journal of Computer Integrated Manufacturing (2021).

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