Siemens Aktiengesellschaft

Gm Cruise Holdings LLC

Martin Sehr

Juan L Aparicio Ojea

Chengtao Wen

Heiko Claussen

Eugen Solowjow

Dogan Gidon

Michael Unkelbach

Mathias Maurmaier

Tao Cui

Shad Laws

Mohamed El Amine Houyou

Rushil Goradia

Ines Ugalde Diaz

Pradeep Bhatta

Benjamin Tearle

Changtao Wen

Systems and techniques are provided for adjusting the planned path of an autonomous vehicle to converge with the actual path. An example method can include determining a first pose error associated with a first pose component from one or more pose components, wherein the first pose error is based on a first difference between an actual pose of an autonomous vehicle and a planned pose of the autonomous vehicle; determining that the first pose error associated with the first pose component exceeds a pose error threshold corresponding to the first pose component; and generating a new planned pose of the autonomous vehicle that yields a reduced first pose error associated with the first pose component, wherein the reduced first pose error is based on a second difference between the actual pose of the autonomous vehicle and the new planned pose of the autonomous vehicle.

Modification of the planned path of an autonomous vehicle to converge to the actual position

A vehicle capability broadcaster may receive information indicating a condition in an environment where an AV may operate. The vehicle capability broadcaster determines a first vehicle capability parameter and a second vehicle capability parameter based on the condition. The vehicle capability parameters may indicate capabilities of the AV for making different types of motions, e.g., limits of the AV making different motions. The vehicle capability broadcaster may transmit the first vehicle capability parameter to a first planning module and transmit the second vehicle capability parameter to a second planning module. The first planning module and the second planning module may plan one or more motions of the AV in the operation based on the vehicle capability parameters. The vehicle capability broadcaster may update a vehicle capability parameter and transmit the updated vehicle capability parameter to one of the planning modules after the one or more motions are planned.

Broadcasting vehicle capabilities to planning modules

Examples of techniques for threat detection in an industrial process system are described herein. An aspect includes determining a plurality of subsystems of an industrial process system. Another aspect includes, for each of the plurality of subsystems, constructing and training a respective deep autoencoder (DAE) model of the subsystem based on data corresponding to the industrial process system. Another aspect includes monitoring the industrial process system using the plurality of DAE models corresponding to the plurality of subsystems. Another aspect includes, based on the plurality of DAE models, determining a cyberattack in a subsystem of the plurality of subsystems.

Industrial process system threat detection

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.

Orchestration of containerized applications

A system controller for visual servoing includes a technology module with dedicated hardware acceleration for deep neural network that retrieves a desired configuration of a workpiece object being manipulated by a robotic device and receives visual feedback information from one or more sensors on or near the robotic device that includes a current configuration of the workpiece object. The hardware accelerator executes a machine learning model trained to process the visual feedback information and determine a configuration error based on a difference between the current configuration of the workpiece object and the desired configuration of the workpiece object. A servo control module adapts a servo control signal to the robotic device for manipulation of the workpiece object in response to the configuration error.

Machine learning enabled visual servoing with dedicated hardware acceleration

Computerized system and method are provided. A robotic manipulator () is arranged to grasp objects (). A gripper () is attached to robotic manipulator (), which includes an imaging sensor (). During motion of robotic manipulator (), imaging sensor () is arranged to capture images providing different views of objects in the environment of the robotic manipulator. A processor () is configured to find, based on the different views, candidate grasp locations and trajectories to perform a grasp of a respective object in the environment of the robotic manipulator. Processor () is configured to calculate respective values indicative of grasp quality for the candidate grasp locations, and, based on the calculated respective values indicative of grasp quality for the candidate grasp locations, processor () is configured to select a grasp location likely to result in a successful grasp of the respective object.

Computerized system and method using different image views to find grasp locations and trajectories for robotic pick up

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Kensington, CA, United States of America

Martin Sehr