公开(公告)号：WO2022046104A1

公开(公告)日：2022-03-03

申请号：PCT/US2020/048735

申请日：2020-08-31

Applicant: SIEMENS CORPORATION ,  SIEMENS INDUSTRY, INC.

Inventor： SEHR, Martin ,  SOLOWJOW, Eugen ,  XIA, Wei Xi ,  TAMASKAR, Shashank ,  UGALDE DIAZ, Ines ,  CLAUSSEN, Heiko ,  APARICIO OJEA, Juan L.

IPC: G06N3/063 ,  G06N3/08

Abstract: 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：WO2021211134A1

公开(公告)日：2021-10-21

申请号：PCT/US2020/028694

申请日：2020-04-17

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS INDUSTRY, INC.

Inventor： XIA, Wei Xi ,  YU, Xiaowen ,  TAMASKAR, Shashank ,  APARICIO OJEA, Juan L. ,  CLAUSSEN, Heiko ,  UGALDE DIAZ, Ines ,  SEHR, Martin ,  SOLOWJOW, Eugen ,  WEN, Chengtao

IPC: G06N20/20 ,  G06N3/08 ,  G06N3/04

Abstract: Distributed neural network boosting is performed by a neural network system through operating at least one processor. A method comprises providing a boosting algorithm that distributes a model among a plurality of processing units each being a weak learner of multiple weak learners that can perform computations independent from one another yet process data concurrently. The method further comprises enabling a distributed ensemble learning which enables a programmable logic controller (PLC) to use more than one processing units of the plurality of processing units to scale an application and training the multiple weak learners using the boosting algorithm. The multiple weak learners are machine learning models that do not capture an entire data distribution and are purposefully designed to predict with a lower accuracy. The method further comprises using the multiple weak learners to vote for a final hypothesis based on a feed forward computation of neural networks.

公开(公告)号：WO2023018752A1

公开(公告)日：2023-02-16

申请号：PCT/US2022/039889

申请日：2022-08-10

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： XIA, Wei Xi ,  SOLOWJOW, Eugen ,  TAMASKAR, Shashank ,  APARICIO OJEA, Juan L. ,  CLAUSSEN, Heiko ,  UGALDE DIAZ, Ines ,  SHAHAPURKAR, Yash ,  SATHYA NARAYANAN, Gokul Narayanan ,  WEN, Chengtao

IPC: G06F30/10 ,  G06T7/50 ,  G06N3/04

Abstract: System and method are disclosed for training a generative adversarial network pipeline that can produce realistic artificial depth images useful as training data for deep learning networks used for robotic tasks. A generator network receives a random noise vector and a computer aided design (CAD) generated depth image and generates an artificial depth image. A discriminator network receives either the artificial depth image or a real depth image in alternation, and outputs a predicted label indicating a discriminator decision as to whether the input is the real depth image or the artificial depth image. Training of the generator network is performed in tandem with the discriminator network as a generative adversarial network. A generator network cost function minimizes correctly predicted labels, and a discriminator cost function maximizes correctly predicted labels.

公开(公告)号：WO2022046062A1

公开(公告)日：2022-03-03

申请号：PCT/US2020/048315

申请日：2020-08-28

Applicant: SIEMENS CORPORATION

Inventor： TAMASKAR, Shashank ,  SEHR, Martin ,  SOLOWJOW, Eugen ,  XIA, Wei Xi ,  APARICIO OJEA, Juan L. ,  UGALDE DIAZ, Ines

IPC: G05B19/418

Abstract: 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：WO2021162681A1

公开(公告)日：2021-08-19

申请号：PCT/US2020/017702

申请日：2020-02-11

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： APARICIO OJEA, Juan L. ,  CLAUSSEN, Heiko ,  UGALDE DIAZ, Ines ,  SEHR, Martin ,  SOLOWJOW, Eugen ,  WEN, Chengtao ,  XIA, Wei Xi ,  YU, Xiaowen ,  TAMASKAR, Shashank

IPC: G06F8/30 ,  G06F8/34 ,  G05B19/02 ,  B25J13/00 ,  B25J9/00

Abstract: According to an aspect of the present disclosure, a computer-implemented includes creating a plurality of basic skill functions for a controllable physical device of an autonomous system. Each basic skill function includes a functional description for using the controllable physical device to interact with a physical environment to perform a defined objective. The method further includes selecting one or more basic skill functions to configure the controllable physical device to perform a defined task. The method also includes determining a decorator skill function specifying at least one constraint. The decorator skill function is configured to impose, at run-time, the at least one constraint, on the one or more basic skill functions. The method further includes generating executable code by applying the decorator skill function to the one or more basic skill functions, and actuating the controllable physical device using the executable code.

公开(公告)号：WO2020072155A1

公开(公告)日：2020-04-09

申请号：PCT/US2019/049033

申请日：2019-08-30

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： UGALDE DIAZ, Ines ,  SEHR, Martin ,  APARICIO OJEA, Juan L. ,  UNKELBACH, Michael

IPC: G06F9/50

Abstract: A system and method are disclosed for orchestrating the execution of computing tasks. An orchestration engine can receive task requests over a network from a plurality of process engines. The process engines may correspond to respective edge or field devices that are remotely located as compared to the orchestration engine. Each task request may indicate at least one task requirement for executing a respective computing task. A plurality of computing instances that have available computing resources can be selected from a set of computing instances. A predicted runtime can be generated for each of the computing tasks. In an example, based on the predicted runtimes, task requirements, available computing resources, and associated network conditions, a schedule and allocation scheme are determined by the orchestration engine. The schedule and allocation scheme define when each of the plurality of computing tasks is performed, and which of the plurality of selected computing instances performs each of the plurality of computing tasks. The selected computing instances execute the plurality of computing tasks according to the schedule and allocation scheme.

公开(公告)号：WO2023064260A1

公开(公告)日：2023-04-20

申请号：PCT/US2022/046257

申请日：2022-10-11

Applicant: SIEMENS CORPORATION

Inventor： SUSA RINCON, Jose Luis ,  UGALDE DIAZ, Ines ,  JAENTSCH, Michael ,  FELD, Joachim

IPC: G01C21/00 ,  G01C21/28 ,  G01C25/00 ,  G05D1/02 ,  G05D1/08 ,  G01C21/20

Abstract: Current approaches to controlling robots from multiple vendors typically requires multiple software systems that define vendor-exclusive fleet manager or dispatch systems. Autonomous devices (e.g., robots, drones, vehicles) can be controlled from multiple vendors that use multiple locally sourced map. For example, maps from individual robots can be translated to a base map that can be used to command and control hybrid fleets of robots.

公开(公告)号：WO2022250659A1

公开(公告)日：2022-12-01

申请号：PCT/US2021/034035

申请日：2021-05-25

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： APARICIO OJEA, Juan L. ,  CLAUSSEN, Heiko ,  UGALDE DIAZ, Ines ,  SATHYA NARAYANAN, Gokul Narayanan ,  SOLOWJOW, Eugen ,  WEN, Chengtao ,  XIA, Wei Xi ,  SHAHAPURKAR, Yash ,  TAMASKAR, Shashank

IPC: B25J9/16

Abstract: In some cases, grasp point algorithms can be implemented so as to compute grasp points on an object that enable a stable grasp. It is recognized herein, however, that in practice a robot in motion can drop the object or otherwise have grasp issues when the object is grasped at the computed stable grasp points. Path constraints that can differ based on a given object are generated while generating the trajectory for a robot, so as to ensure that a grasp remains stable throughout the motion of the robot.

公开(公告)号：WO2021201823A1

公开(公告)日：2021-10-07

申请号：PCT/US2020/025719

申请日：2020-03-30

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： UGALDE DIAZ, Ines ,  CLAUSSEN, Heiko ,  APARICIO OJEA, Juan L. ,  SEHR, Martin ,  SOLOWJOW, Eugen ,  WEN, Chengtao ,  XIA, Wei Xi ,  YU, Xiaowen ,  TAMASKAR, Shashank

IPC: G06N3/04 ,  G06N3/08 ,  G06N3/0454

Abstract: A system for supporting artificial intelligence inference in an edge computing device associated with a physical process or plant includes a neural network training module, a neural network testing module and a digital twin of the physical process or plant. The neural network training module is configured to train a neural network model for deployment to the edge computing device based on data including baseline training data and field data received from the edge computing device. The neural network testing module is configured to validate the trained neural network model prior to deployment to the edge computing device by leveraging the digital twin of the physical process or plant.

公开(公告)号：WO2023033814A1

公开(公告)日：2023-03-09

申请号：PCT/US2021/048530

申请日：2021-08-31

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： APARICIO OJEA, Juan L. ,  CLAUSSEN, Heiko ,  UGALDE DIAZ, Ines ,  SEHR, Martin ,  SOLOWJOW, Eugen ,  WEN, Chengtao ,  XIA, Wei Xi ,  YU, Xiaowen ,  TAMASKAR, Shashank

IPC: B25J9/16

Abstract: It is recognized herein that current approaches to autonomous operations are often limited to grasping and manipulation operations that can be performed in a single step. It is further recognized herein that there are various operations in robotics (e.g., assembly tasks) that require multiple steps or a sequence of motions to be performed. To determine or plan a sequence of motions for fulfilling a task, an autonomous system that includes a robot can perform object recognition, pose estimation, affordance analysis, decision-making, probabilistic task or motion planning, and object manipulation.