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

被引：5

作者：

Metzner, Maximilian ^{[1
]}

Albrecht, Felix ^{[2
]}

Fiegert, Michael ^{[3
]}

Bauer, Bastian ^{[1
]}

Martin, Susanne ^{[1
]}

Karlidag, Engin ^{[2
]}

Blank, Andreas ^{[1
]}

Franke, Joerg ^{[1
]}

机构：

[1] FAU Erlangen Nuremberg, Inst Factory Automat & Prod Syst, Egerlandstr 7-9, D-91058 Erlangen, Germany

[2] Siemens AG, Digital Ind Div, Geratewerk Erlangen, Germany

[3] Siemens AG, Corp Technol, Munich, Germany

来源：

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING | 2022年 / 35卷 / 4-5期

关键词：

Bin picking; synthetic training data; convolutional neural networks; robotics simulation; virtual commissioning;

D O I：

10.1080/0951192X.2021.2004618

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：483 / 492

页数：10