Siemens Aktiengesellschaft

Siemens Product Lifestyle Management Inc.

Siemens Corporation

Hasan Sinan Bank

Ziyan Wu

Justinian Rosca

Arquimedes Martinez Canedo

Naveen Kumar Singa

Lingyun Wang

Suraj Ravi Musuvathy

Hartmut Ludwig

Erhan Arisoy

Xiaofan Wu

David Madeley

Mareike Kritzler

Ulrich Muenz

Tao Cui

Edward Slavin, Iii

Michael Jäntsch

Phani Ram Kumar Kuruganty

Rizwan Majeed

Clifford Chou

Mark R Burhop

Giuseppe Francesco Rigano

Michael Little

Simon Mayer

Emmanouil Tzorakoleftherakis

Ulrich Münz

A system for safe interaction between a human and an industrial machine includes a cyber-mechanical system. The cyber-mechanical system includes at least one industrial machine and a cyber-mechanical control system for processing inputs and producing control outputs for the at least one industrial machine; The system further includes a task planner configured to translate high level goals into scheduled tasks of the industrial machine. The system includes an interaction reasoner that identifies at least one interaction between the industrial machine and a human working in cooperation with the industrial machine. Output of the interaction reasoner is provided to an image generator that produces an interaction image. The interaction image represents information relating to one or more of the scheduled tasks of the industrial machine. An image projector associated with the industrial machine conveys information about the scheduled tasks of the associated industrial machine to the human.

Method for dynamic multi-dimensional spatio-temporal human machine interaction and feedback

A system for performing autonomous agriculture within an agriculture production environment includes one or more agriculture pods, a stationary robot system, and one or more mobile robots. The agriculture pods include one or more plants and one or more sensor modules for monitoring the plants. The stationary robot system collects sensor data from the sensor modules, performs farming operations on the plants according to an operation schedule based on the collected sensor data, and generates a set of instruction for transporting the agriculture pods within the agriculture production environment. The stationary robot system communicates the set of instructions to the agriculture pods. The mobile robots transport the agriculture pods between the stationary robot system and one or more other locations within the agriculture production environment according to the set of instructions.

Autonomous mobile robots for movable production systems

A computer-based system and method is disclosed for spatial programming of a robotic device. A mixed reality tool may select an object related to one or more interactive tasks for the robotic device. A spatial location of the object may be located including Cartesian coordinates and orientation coordinates of the object. An application program may be executed to operate the robotic device using the spatial location. Based on initial parameters, execution of the one or more tasks by the robotic device on the object related to a skill set may be simulated in a mixed reality environment.

Mixed reality assisted spatial programming of robotic systems

Systems, methods, and computer-readable media are disclosed for planning and scheduling of manufacturing using a receding horizon planning approach that enables the manufacture of multiple products in a flexible and efficient manner. A receding horizon planning engine is provided that receives input data indicative of various manufacturing parameters associated with the manufacture of various products and utilizes the input data to solve an optimization problem with respect to a cost function to generate a receding horizon execution plan indicative of a set of planned manufacturing steps to be executed. Some subset of the planned manufacturing steps (which may include all planned steps) may then be executed. Upon completion of execution of the subset of planned manufacturing steps or upon the occurrence of a trigger event, the optimization problem may be re-solved to obtain a new receding horizon execution plan for a subsequent set of planned manufacturing steps.

Flexible product manufacturing planning

A system and method is disclosed for development of a control application for a controller of an automation system. The controller receives sensor signals associated with perception of a first real component during an execution of the control application program. Activity of a virtual component, including interaction with the real first component, is simulated, the virtual component being a digital twin of a second real component designed for the work environment and absent in the work environment. Virtual data is produced in response to the simulated activity of the virtual component. A control application module determines parameters for development of the control application program using the sensor signals for the first real component and the virtual data. An AR display signal for the work environment is rendered and displayed based on a digital representation of the virtual data during an execution of the control application program.

Development of control applications in augmented reality environment

Systems, techniques, and computer-program products are provided for tracking and traceability of parts of a finished product. In some embodiments, the tracking and traceability generates streams of semantic data obtained from an imaging sensor system that records the execution of a manufacturing process in industrial equipment. The execution of the manufacturing process yields a finished product from initial materials and/or parts. The tracking and traceability also implements artificial reasoning about the execution of the manufacturing process to generate assertions that characterize the execution of the manufacturing process. Semantic data and assertions can be aggregated into a digital trace record that tracks a defined component of the finished product throughout the execution of the manufacturing process and permit tracing the component to a defined event within the manufacturing process.

Tracking and traceability of parts of a product

Systems and methods for support structures for additive manufacturing of solid models. A method includes receiving a solid model, for a physical object to be manufactured, that includes a plurality of boundary representation surfaces. The method includes analyzing the b-rep surfaces to generate point samples for potential support locations. The method includes clustering points on the solid model, corresponding to at least some of the point samples, to create support locations. The method includes generating column supports in the solid model that connect to the original solid model at the support locations. The method includes storing the solid model, including the column supports.

Support structures for additive manufacturing of solid models

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Princeton, NJ, United States of America

Hasan Sinan Bank