Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and a method for predicting motion-outcome data of a robot moving between a given pair of robotic locations. Data on a given pair of robotic locations are received as input data. A function trained by a machine learning algorithm is applied to the input data, wherein a related robotic motion-outcome data is generated as output data. The robotic motion-outcome data is provided as output data.

Abstract: 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 (sn); 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 Atnfs and on the basis of the virtual time stamp after completion of the cycle of the second simulation system a virtual duration time Atnss; and e) calculating an update sn+1 for the scaling factor according the most recent scaling factor sn multiplied by Atnss/Atnfs.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and a method for programming for a plurality of cells of an industrial environment. A physical cobot is provided within a lab cell comprising lab physical objects. A virtual simulation system with a user interface is provided. The virtual simulation system receives information inputs on the virtual cobot, on the virtual lab cell comprising lab virtual objects, and on a plurality of virtual industrial cells comprising virtual industrial objects. The virtual cobot and the physical cobot are connected together. A superimposed meta-cell is generated by superimposing the plurality of virtual cells and the virtual lab cell so as to obtain a single superimposed meta cell including a set of superimposed virtual objects. The virtual cobot is positioned in the superimposed meta cell.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and a process for determining a set of tuned robotic motion instructions of a robot for performing robotic tasks, wherein a sudden stop of the robot implies a trajectory deviation. Inputs are received, including virtual representation of the robot, information on a sequence of robotic motion instructions, information on a set of forbidden volumes. A set of segments of robotic motion instructions is defined. For each segment, a corresponding stop envelope is generated by taking into account a set of sudden stops of the robot. A subset of critical segments for which there is a certain overlapping zone between the corresponding stop envelope and the set of forbidden volumes is determined. For each critical segment, the corresponding robotic motion instructions are iteratively tuned until the newly generated stop envelope for the segment has a minimal overlapping zone with the set of forbidden volumes.

Abstract: 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.

Abstract: 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.