公开(公告)号：US20240296662A1

公开(公告)日：2024-09-05

申请号：US18578471

申请日：2021-08-06

申请人： Siemens Corporation

发明人： Eugen Solowjow ,  Ines Ugalde Diaz ,  Yash Shahapurkar ,  Juan L. Aparicio Ojea ,  Heiko Claussen

IPC分类号： G06V10/774 ,  G06T15/20 ,  G06V10/764 ,  G06V10/776 ,  G06V20/70

CPC分类号： G06V10/774 ,  G06T15/20 ,  G06V10/764 ,  G06V10/776 ,  G06V20/70

摘要： A computer-implemented method for building an object detection module uses mesh representations of objects belonging to specified object classes of interest to render images by a physics-based simulator. Each rendered image captures a simulated environment containing objects belonging to multiple object classes of interest placed in a bin or on a table. The rendered images are generated by randomizing a set of parameters by the simulator to render a range of simulated environments. The randomized parameters include environmental and sensor-based parameters. A label is generated for each rendered image, which includes a two-dimensional representation indicative of location and object classes of objects in that rendered image frame. Each rendered image and the respective label constitute a data sample of a synthetic training dataset. A deep learning model is trained using the synthetic training dataset to output object classes from an input image of a real-world physical environment.

公开(公告)号：US20240198530A1

公开(公告)日：2024-06-20

申请号：US18557967

申请日：2021-06-25

申请人： Siemens Corporation

发明人： Ines Ugalde Diaz ,  Eugen Solowjow ,  Juan L. Aparicio Ojea ,  Martin Sehr ,  Heiko Claussen

IPC分类号： B25J9/16 ,  G06T7/73 ,  G06V10/764 ,  G06V10/80 ,  G06V10/82 ,  G06V20/50 ,  G06V20/70

CPC分类号： B25J9/1679 ,  B25J9/1697 ,  G06T7/73 ,  G06V10/764 ,  G06V10/811 ,  G06V10/82 ,  G06V20/50 ,  G06V20/70 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/20084

摘要： In described embodiments of method for executing autonomous bin picking, a physical environment comprising a bin containing a plurality of objects is perceived by one or more sensors. Multiple artificial intelligence (AI) modules feed from the sensors to compute grasping alternatives, and in some embodiments, detected objects of interest. Grasping alternatives and their attributes are computed based on the outputs of the AI modules in a high-level sensor fusion (HLSF) module. A multi-criteria decision making (MCDM) module is used to rank the grasping alternatives and select the one that maximizes the application utility while satisfying specified constraints.

公开(公告)号：US20230228688A1

公开(公告)日：2023-07-20

申请号：US17852417

申请日：2022-08-10

申请人： Siemens Corporation

发明人： Eduardo Moura Cirilo Rocha ,  Shashank Tamaskar ,  Wei Xi Xia ,  Eugen Solowjow ,  Nan Tian ,  Gokul Narayanan Sathya Narayanan

IPC分类号： G01N21/88 ,  G01B11/24 ,  G06T7/11 ,  G06T7/70

CPC分类号： G01N21/8851 ,  G01B11/24 ,  G06T7/11 ,  G06T7/70 ,  G06T2200/04 ,  G06T2207/10012 ,  H04N13/207

摘要： Robots might interact with planar objects (e.g., garments) for process automation, quality control, to perform sewing operations, or the like. It is recognized herein that robots interacting with such planar objects can pose particular problems, for instance problems related to detecting the planar object and estimating the pose of the detected planar object. A system can be configured to detect or segment planar objects, such as garments. The system can include a three-dimensional (3D) sensor positioned to detect a planar object along a transverse direction. The system can further include a first surface that supports the planar object. The first surface can be positioned such that the planar object is disposed between the first surface and the 3D sensor along the transverse direction. In various examples, the 3D sensor is configured to detect the planar object without detecting the first surface.

公开(公告)号：US20240012400A1

公开(公告)日：2024-01-11

申请号：US18041718

申请日：2020-08-28

申请人： Siemens Corporation

发明人： Shashank Tamaskar ,  Martin Sehr ,  Eugen Solowjow ,  Wei Xi Xia ,  Juan L. Aparicio Ojea ,  Ines Ugalde Diaz

IPC分类号： G05B19/418

CPC分类号： G05B19/41875 ,  G05B2219/32193

摘要： A computer-implemented method for failure classification of a surface treatment process includes receiving one or more process parameters that influence one or more failure modes of the surface treatment process and receiving sensor data pertaining to measurement of one or more process states pertaining to the surface treatment process. The method includes processing the received one or more process parameters and the sensor data by a machine learning model deployed on an edge computing device controlling the surface treatment process to generate an output indicating, in real-time, a probability of process failure via the one or more failure modes. The machine learning model is trained on a supervised learning regime based on process data and failure classification labels obtained from physics simulations of the surface treatment process in combination with historical data pertaining to the surface treatment process.

公开(公告)号：US20230359864A1

公开(公告)日：2023-11-09

申请号：US18043400

申请日：2020-08-31

申请人： Siemens Corporation ,  SIEMENS INDUSTRY, INC.

发明人： Martin Sehr ,  Eugen Solowjow ,  Wei Xi Xia ,  Shashank Tamaskar ,  Ines Ugalde Diaz ,  Heiko Claussen ,  Juan L. Aparicio Ojea

IPC分类号： G06N3/045

CPC分类号： G06N3/045 ,  G05B13/027

摘要： An edge device can be configured to perform industrial control operations within a production environment that defines a physical location. The edge device can include a plurality of neural network layers that define a deep neural network. The edge device be configured to obtain data from one or more sensors at the physical location defined by the production environment. The edge device can be further configured to perform one or more matrix operations on the data using the plurality of neural network layers so as to generate a large scale matrix computation at the physical location defined by the production environment. In some examples, the edge device can send the large scale matrix computation to a digital twin simulation model associated with the production environment, so as to update the digital twin simulation model in real time.

公开(公告)号：US20230330858A1

公开(公告)日：2023-10-19

申请号：US18044242

申请日：2021-09-09

申请人： Siemens Corporation

发明人： Eugen Solowjow ,  Juan L. Aparicio Ojea ,  Avinash Kumar ,  Matthias Loskyll ,  Gerrit Schoettler

IPC分类号： B25J9/16

CPC分类号： B25J9/1687 ,  B25J9/1697 ,  B25J5/007

摘要： In an example aspect, a first object (e.g., an electronic component) is inserted by a robot into a second object (e.g., a PCB). An autonomous system can capture a first image of the first object within a physical environment. The first object can define a mounting interface configured to insert into the second object. Based on the first image, a robot can grasp the first object within the physical environment. While the robot grasps the first object, the system can capture a second image of the first object. The second image can include the mounting interface of the first object. Based on the second image of the first object, the system can determine a grasp offset associated with the first object. The grasp offset can indicate movement associated with the robot grasping the first object within the physical environment. The system can also capture an image of the second object. Based on the grasp offset and the image of the second object, the robot can insert the first object into the second object.