Metzner M, Albrecht F, Fiegert M, Bauer B, Martin S, Karlidag E, Blank A, Franke J (2021)
Publication Type: Journal article
Publication year: 2021
DOI: 10.1080/0951192X.2021.2004618
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.
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://dx.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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