Various embodiments relate to a method for motion simulation for a manipulator, such as an NC-controlled manipulator, in a machining environment, wherein the manipulator is moved in an operating mode by a control apparatus and the machining environment is at least partly mapped in an environment model and wherein the method comprises computation of a trajectory plan by the control apparatus from a setpoint movement of the manipulator starting from an initial situation and based on a kinematic model of the manipulator, performance of a kinematic collision check based on the trajectory plan, the kinematic model and the environment model, and production of a prediction result based on the kinematic collision check. The method is characterized in that the initial situation corresponds to the current manipulator state. Further, some embodiments relate to a corresponding computer program with program code and to a corresponding system for motion simulation for a manipulator.

A Multi-Purpose Dynamic Simulation and run-time Control platform includes a virtual process environment coupled to a physical process environment, where components/nodes of the virtual and physical process environments cooperate to dynamically perform run-time process control of an industrial process plant and/or simulations thereof. Virtual components may include virtual run-time nodes and/or simulated nodes. The MPDSC includes an I/O Switch which delivers I/O data between virtual and/or physical nodes, e.g., by using publish/subscribe mechanisms, thereby virtualizing physical I/O process data delivery. Nodes serviced by the I/O Switch may include respective component behavior modules that are unaware as to whether or not they are being utilized on a virtual or physical node. Simulations may be performed in real-time and even in conjunction with run-time operations of the plant, and/or simulations may be manipulated as desired (speed, values, administration, etc.). The platform simultaneously supports simulation and run-time operations and interactions/intersections therebetween.

A Multi-Purpose Dynamic Simulation and run-time Control platform includes a virtual process environment coupled to a physical process environment, where components/nodes of the virtual and physical process environments cooperate to dynamically perform run-time process control of an industrial process plant and/or simulations thereof. Virtual components may include virtual run-time nodes and/or simulated nodes. The MPDSC includes an I/O Switch which delivers I/O data between virtual and/or physical nodes, e.g., by using publish/subscribe mechanisms, thereby virtualizing physical I/O process data delivery. Nodes serviced by the I/O Switch may include respective component behavior modules that are unaware as to whether or not they are being utilized on a virtual or physical node. Simulations may be performed in real-time and even in conjunction with run-time operations of the plant, and/or simulations may be manipulated as desired (speed, values, administration, etc.). The platform simultaneously supports simulation and run-time operations and interactions/intersections therebetween.

Implementations are provided for operably coupling multiple robot controllers to a single virtual environment, e.g., to generate training examples for training machine learning model(s). In various implementations, a virtual environment may be simulated that includes an interactive object and a plurality of robot avatars that are controlled independently and contemporaneously by a corresponding plurality of robot controllers that are external from the virtual environment. Sensor data generated from a perspective of each robot avatar of the plurality of robot avatars may be provided to a corresponding robot controller. Joint commands that cause actuation of one or more joints of each robot avatar may be received from the corresponding robot controller. Joint(s) of each robot avatar may be actuated pursuant to corresponding joint commands. The actuating may cause two or more of the robot avatars to act upon the interactive object in the virtual environment.

A system (1) for emulating remote control of a physical robot (5) via a wireless network (11) is disclosed. The system comprises a control module (2) for determining a trajectory for the physical robot and generating trajectory control data that comprises velocity data and positional data based on the determined trajectory. The system further comprises a first control loop (3), comprising a first feed forward controller (4). The first feed forward controller is configured to receive the trajectory control data, send, via the wireless network (11), and a first velocity command to a first control interface of the physical robot (5). The first velocity command is based on the trajectory data. The first feed forward controller (4) is further configured to receive, via the wireless network, a first set of sensor data from physical robot. The system (1) further comprises a simulated robot implementing a digital twin (9) of the physical robot, and a second control loop (6) comprising a second feed forward controller (7). The second feed forward controller is configured to receive, via the wireless network, a second set of sensor data from the physical robot, determine a second velocity command based on the received second set of sensor data, and send the second velocity command to a second control interface of the digital twin. A corresponding method (100) is also disclosed.

A Multi-Purpose Dynamic Simulation and run-time Control platform includes a virtual process environment coupled to a physical process environment, where components/nodes of the virtual and physical process environments cooperate to dynamically perform run-time process control of an industrial process plant and/or simulations thereof. Virtual components may include virtual run-time nodes and/or simulated nodes. The MPDSC includes an I/O Switch which delivers I/O data between virtual and/or physical nodes, e.g., by using publish/subscribe mechanisms, thereby virtualizing physical I/O process data delivery. Nodes serviced by the I/O Switch may include respective component behavior modules that are unaware as to whether or not they are being utilized on a virtual or physical node. Simulations may be performed in real-time and even in conjunction with run-time operations of the plant, and/or simulations may be manipulated as desired (speed, values, administration, etc.). The platform simultaneously supports simulation and run-time operations and interactions/intersections therebetween.

Systems and a method are provided for programming a cobot for a plurality of cells of an industrial environment. A physical cobot is provided within a lab cell comprising physical lab objects. A virtual simulation system receives information inputs on a virtual cobot representing the physical cobot, regarding a virtual lab cell comprising virtual lab objects, and on a plurality of virtual industrial cells comprising virtual industrial objects. Inputs are received from the physical cobot's movement during teaching whereby the physical cobot is moved in the lab cell to the desired position(s) while providing, via a user interface, a visualization of the virtual cobot's movement within a meta cell generated by superimposing the plurality of virtual industrial cells with the virtual lab cell, so that collisions with any object are minimized. A robotic program is generated based on the received inputs of the physical cobot's movement.

A method for applying machine learning to an application includes: a) generating a set of candidate parameters by a learner; b) executing a program in at least one simulated application based on the set of candidate parameters and providing interim results of tested sets of candidate parameters based on a measured performance information of the execution of the program; c) collecting a predetermined number of interim results and providing an end result based on a combination of the candidate parameters and the measured performance information by a trainer; and d) generating a new set of candidate parameters by the learner based on the end result for execution by the unchanged program.

A method for applying machine learning to an application includes: a) generating a candidate policy by a learner; b) executing a program in at least one simulated application based on a set of candidate parameters provided based on the candidate policy and a state of the at least one simulated application, execution of the program providing interim results of tested sets of candidate parameters based on a measured performance information of the execution of the program; c) collecting a predetermined number of interim results and providing an end result based on a combination of the candidate parameters and/or the state with the measured performances information by a trainer; and d) generating a new candidate policy by the learner based on the end result.

A system to determine a real process parameter of a real field device in a real flow route of a process plant can include an interface and a virtual twin flow route that may virtually represent the real flow route. A twin process fluid flow is virtually influenced according to a twin influence factor. The virtual twin flow route may: receive, via the interface, a real initial parameter of the real process fluid flow and/or of the real field device, and virtually determine, fluid flow upstream or downstream, a twin process parameter of the twin field device corresponding to the real field device using the at least one twin influence factor, the virtually determined twin process parameter being supplied to the at least one real field device as the to be determined real process parameter for further influencing the real process fluid flow.