Siemens Industry Software Gmbh

Siemens Product Lifecycle Management Software Inc.

Moshe Hazan

Rahav Madvil

Lisandro Embon

Gal Snir

Gilles Velay

Guy Barak

Dror Davidi

Gilad Milman

Zohar Bengad

Rafael Blumenfeld

Ilanit Shavit

Werner Herla

Shaul Badusa

Guillaume Ginioux

Robert Winter

Matthias Frische

Yael Seror

Meir Koren

Ada Raveh

Johannes Ottermann

Yona Eilat

Yael Barak

Roni Blacher

Etienne Ferre

Ambroise Confetti

Systems and a method for predicting a motion trajectory of a robot moving between a given pair of robotic locations. Training data of motion trajectories of the robot are received for a plurality of robotic location pairs. The training data are processed so as to obtain x tuples and y tuples for machine learning purposes; wherein the x tuples describe the robotic location pair and the y tuples describe one or more intermediate robotic locations at specific time stamps during the motion of the robot between the locations of the location pair. From the processed data, a function is learned for mapping the x tuples into the y tuples so as to generate a motion prediction module for the robot. For a given robotic location pair, the robotic motion between the given pair is predicted by obtaining the corresponding intermediate locations resulting from the motion prediction module.

Method and system for predicting a motion trajectory of a robot moving between a given pair of robotic locations

Systems and methods determining motion data samples of robots and surrounding object sets displaying robotic tasks within a virtual environment portion include accessing a virtual simulation system simulating robotic tasks interacting with object sets within the virtual environment. The system receives virtual data inputs on the environment including robots and object sets. Instruction sets representing robotic tasks and resulting data of a simulation of robotic tasks and object sets is received and sampled at sampling times until performing iterations. Sampled position data of robots and object sets are collected at sampling times. A robotic relation group includes the robot, objects in the environment portion and time stamps. A virtual camera point of view displays the robot and object set. The robot and object set are on collected sampled position data at time stamps and motion data samples are determined by recording their motion data sample at the point of view.

Method and system for automatically determining a motion data sample of a given robot and its surrounding object set

Systems and a method for facilitating a concurrent simulation of multiple tasks of a plurality of robots in a virtual environment, wherein at least one virtual robot is foreseen to concurrently simulate one robotic motion task and a set of robotic logic tasks by concurrently executing one corresponding robotic motion program and a set of corresponding robotic logic programs on a set of operands. During a concurrent execution of the plurality of robotic motion programs and the plurality of sets of robotic logic programs of the plurality of robots, the execution of at least one given logic program is suspended and resumed by repetitively: executing a run of the given logic program; collecting a subset of operands used in the executed run; if none of the collected operands is modified in the execution run, suspending the execution of the given logic program and resuming its execution when one of the collected operands is modified.

Method and system for facilitating a concurrent simulation of multiple robotic tasks

A robotic program of an industrial robot is simulated. Inputs on a robotic program of a robot are received. The robotic program of the robot is represented with a neutral representation modeled with a neutral language. Specific code portions of the robotic program in the neutral representation are mapped with corresponding specific code portions of a native representation modeled with a native language of the at least one robot. The robot program in simulated in one of the neutral representation and the native representation. Corresponding code portions of the neutral representation and of the native representation of the robotic program are synchronized via the mapping.

Method and system for simulating a robotic program of an industrial robot

A method synchronizes first and second simulation systems, each operating in a free running operation thereby exchanging data to run the simulation systems. The method includes: a) providing the first simulation system (PLCSIM) being enabled to run in cycles at a linear speed determined by repeatably setting a scaling factor (s); b) providing the second simulation system (Process Simulate) to run in cycles at different speeds; c) the second simulation system requests at the end of a cycle a virtual time stamp from the first simulation system; d) calculating on the basis of the virtual time stamp a virtual duration time Atand on the basis of the virtual time stamp after completion of the cycle of the second simulation system a virtual duration time At; and e) calculating an update sn+1 for the scaling factor according the most recent scaling factor smultiplied by At/At.

Method and a system for synchronizing a first and a second simulation system

Systems and methods may support generation and use of simulation signature keys for robotic simulations. A computer-aided manufacturing (CAM) system may access robotic path data for a robot that includes target locations and corresponding motion parameters for the robot. The CAM system may partition the robotic path data into simulation events according to event partitioning criteria such that each simulation event includes one or multiple target locations and corresponding motion parameters and further generate a respective signature simulation key for each partitioned simulation event. The simulation signature key may provide a lookup key into a motion planning data cache. When an entry exists in the motion planning data cache for a simulation signature key generated for a simulation event, the CAM system may retrieve cached motion planning data for the simulation event. When an entry does not exist, the CAM system may obtain motion planning data through robotic simulation.

Simulation signature keys for robotic simulations

Systems and a method predict a generation of a robotic program for industrial coating. Inputs are received including a virtual representation of a robot, a coating gun, elements of the object surface to be coated and a set of desired coating thickness ranges. Inputs on a coating dispersion object are also received. Training data of a plurality of robotic programs for industrial coating and of their corresponding coating thickness coverage on a plurality of surfaces are received. The training data are processed in x, y tuples so as to learn a mapping function to generate a coating prediction module. Starting with a given selected valid thickness coverage as input parameters, it is proceeded in an iterative manner to predict a robotic program via the coating prediction module. A coating robotic program is generated for each surface element based on the resulting predicted coating programs.

Method and system for generating a robotic program for industrial coating

Systems and a method for teaching a robot in reaching a given target location. The system and method include receiving inputs on a representation of a given target location to be reached by the robot. A check is made whether the given target location is singular. If the given target location is non-singular, the teaching of the robot is effected by associating with the given target location a selected configuration. If the given target is singular, the teaching of the robot is effected by associating with the given target location an assigned joint-values solution.

Method and system for teaching a robot in reaching a given target in robot manufacturing

Systems and a method determine a sequence of joint values of an external axis along a sequence of targets. Inputs are received, including robot representation, tool representation, sequence of targets, kinematics of the axis joints, and/or type of robot-axis motion. For each target, it is generated at least one weight factor table representing, for each available configuration of the axis joint motion, a combined effort of the robot motion and the axis motion depending on the type of combined robot-axis motion. Valid weight factor values of the table are determined by simulating collision free trajectories for reaching the target. The sequence of joint values of the at least one external axis is determined by finding from the weight factor table a sequence of joint values for which the sum of their corresponding weight factors for reaching the target location sequence is minimized.

Method and system for determining joint values of an external axis in robot manufacturing

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.

Method and system for programming a cobot for a plurality of industrial cells

Systems and a method for determining a sequence of kinematic chains of a multiple robot along a sequence of locations. Inputs on the locations to be reached by a robot tool are received. Each chain is considered separately by setting one chain in use and determining, for each chain in use, available configurations for each location. The available configurations are represented as nodes of a graph representing available robotic paths for reaching with a tool the locations, while allowing the switching among different chains within the same robotic path. Valid connectors are determined by simulating collision free robot trajectories while taking into account working modality constraints of the locations. Weight factors are assigned to connectors to represent robot efforts in moving between subsequent configurations. The shortest robotic path among valid paths is determined by taking into account the weight factors. The sequence of chains is determined from the shortest path.

Method and system for determining a sequence of kinematic chains of a multiple robot

Systems and a method for calculating thickness values of a coating material applied by a coating gun on object surfaces in industrial processes include measuring, on real test surfaces, the thickness values of coating material samples applied by the coating gun on the real test surfaces. The measured thickness values are used to generate 3D virtual objects modeling the coating dispersions of the coating gun at given angles formed between the coating gun and the test surface. The thickness values of the coating material applied on the surface of a simulated object by a simulated gun at a certain angle are calculated by detecting the collision between the 3D virtual object mounted on the simulated coating gun and surface elements of the simulated object surface.

Calculating thicknesses of applied coating material

Methods for CAD, simulation, and corresponding systems and computer-readable mediums. A method includes receiving inputs including one or more of robot information, operation information, position information, and constraint information. The method includes generating a list of candidate positions of a robot. The method includes, for each candidate position, determining a time value of the candidate position and when the time value of the candidate position does not meet a threshold cycle time value, removing the candidate position. The method includes, for each candidate position, determining an energy consumption value of the candidate position. The method includes, for each candidate position, determining one or more of a rating and a ranking for the candidate position based on the time value and the energy consumption value. The method includes determining the optimal position of the robot based on the ranking of each candidate position.

Energy and cycle time efficiency based method for robot positioning

Methods for optimizing energy savings and reducing cycle time for mutating an industrial robotic path when a collision is detected. A method includes initializing a plurality of clone paths where a collision was detected, wherein a clone path is a clone of the initial path and the initial path comprises a source location, a plurality of intermediate locations, and a target location; for each clone path, determining a candidate path to store in a population, determining an optimal breed comprising the candidate path with an optimal rating, wherein the optimal rating is determined by the lowest breed rating in the population, and returning the optimal breed.

Method and apparatus for industrial robotic energy saving optimization using fly-by

Systems and a method for anti-collision management of two or more robots with at least partially overlapping robotic movements are provided. The systems and method include receiving inputs from two or more single robots performing robotic operations along a trajectory defining for each robot a single swept volume. Discretized subswept volumes are created between robotic path locations for each single robot according to one or more discretization criteria such that the operation of each single robot moving along the known trajectory is controlled to allow synchronized execution of robotic operations. Only overlapping discretized subswept volumes are considered for synchronization. This enables focusing only on the relevant collision-prone areas. Deadlocks may be prevented by a look-ahead behavior. Smart synchronization methodology allows optimizing cycle time.

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Elad, Israel

Moshe Hazan