The invention relates to a production planning method using a plurality of manufacturing devices (INTMA) according to which tasks (TD) of a work plan (BOP) are compared (MA) with manufacturing capabilities (SD) of the manufacturing devices (INTMA) and, depending on the one or more results (MAQ) of said comparison (MA), at least one or more manufacturing devices (INTMA) are commissioned to match their manufacturing capabilities (SD) with the task(s) (TD).

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. 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 the user interface, a visualization of the virtual cobot's movement within the superimposed meta 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 robot control device includes an obtaining unit that obtains, from an image sensor that captures a workpiece group to be handled by a robot, a captured image, a simulation unit that simulates operation of the robot, and a control unit that performs control such that the captured image is obtained if, in the simulation, the robot is retracted from an image capture forbidden space, in which an image is potentially captured with the workpiece group and the robot overlapping each other, and which is set based on either or both a first space being the visual field range of the image sensor, and a columnar second space obtained by taking a workpiece region including the workpiece group or each of divided regions into which the workpiece region is divided, as a bottom area, and extending the bottom area to the position of the image sensor.

The present invention relates to a collision-free path generating method for a robot and an end effector quipped thereon to move. The method includes steps of configuring a virtual working environment, containing a plurality of virtual objects at least including the robot, the end effector and a target object consisting of a plurality of basic members and mapped from a working environment in a reality, in a robot simulator; selecting a level of detail and a pre-determined shape for a collider covering the plurality of virtual objects to determine boundaries for the plurality of objects; randomly sampling a combination of robot configurations; and based on the determine boundaries and the randomly sampled combination of robot configurations, performing a heuristic based pathfinding algorithm to compute a collision-free path for the robot and the end effector quipped thereon to move to the target object accordingly.

An information processing device converts first information for manipulating a robot model inputted into a manipulation terminal connected with a simulation device configured to execute a simulation for causing the robot model to perform a simulated operation and operated by a remote user of the simulation device, into second information for manipulating the robot model of the simulation device, operates the simulation device according to the second information, and causes the manipulation terminal to present information on the operation of the robot model of the simulation device configured to operate according to the second information.

A gap detection device includes: a measurement unit measuring the driving torque or current value of a motor when the robot is actually being operated along an arbitrary motion trajectory; a simulation unit setting an arbitrary second gap amount between pairing elements of a plurality of pairing elements, performing a simulation in which the robot operates along the same arbitrary motion trajectory, and estimating the driving torque or the current value of the motor; a feature amount calculation unit calculating a first feature amount indicating fluctuations of a value related to the measured driving torque or current value, and a second feature amount indicating fluctuations of a value related to the estimated driving torque or current value; and a gap calculation unit calculating an index related to a first gap amount on the basis of the first feature amount, the second feature amount, and the second gap amount.

A robot simulation device includes: an arrangement unit that arranges a robot model, a visual sensor model, and a workpiece model in a virtual space; a calculation unit that, by superimposing three-dimensional position information for a workpiece, acquired by a visual sensor in a workspace and based on a robot or the visual sensor, and shape characteristics of a workpiece model, calculates a position and orientation of the workpiece model based on a robot model or a visual sensor model in the virtual space; and a simulation unit that measures the workpiece model using the visual sensor model and executes a simulation operation in which work is performed on the workpiece model by the robot model. The arrangement unit arranges, in the virtual space, the workpiece model in the position and the orientation calculated by the calculation unit and based on the robot model or the visual sensor model.

Implementations disclosed herein relate to mitigating the reality gap through training a simulation-to-real machine learning model (Sim2Real model) using a vision-based robot task machine learning model. The vision-based robot task machine learning model can be, for example, a reinforcement learning (RL) neural network model (RL-network), such as an RL-network that represents a Q-function.

The purpose of the present invention is to provide a device for outputting holding detection results by a highly accurate simulation in consideration of parameters related to a holding member. A user enters workpiece information through an input UI unit. A selection control unit executes an automatic selection process of a suction pad based on the workpiece information input through the input UI unit, an automatic selection process of a workpiece physical model, an automatic selection process of a robot, and a confirmation process of a vibration tolerance, and then displays the selection results. The selection control unit determines whether there is a problem with the selection results based on an input instruction from the user.

A system for generating a path to be followed by a robot used to perform a process on a workpiece has a computing device that has program code for operating the robot and obtaining information related to the workpiece and a vision system that scans the workpiece to obtain images thereof that are provided to the computing device. The computing device processes the images to obtain geometric information about the workpiece that the computing device uses in combination with process related reference parameters stored in the computing device to generate program code for a path to be followed by the robot to perform the process on the workpiece. The computing device also includes code configured to verify for quality the generated program code for the path to be followed by the robot to perform the process on the workpiece.