Abstract: A device capable of acquiring a deviation of a working position of a tool with respect to a target position of a workpiece with higher accuracy in accordance with actual work. A device for acquiring a deviation amount of a working position of a tool with respect to a target position when a work is performed on a workpiece with respect to the target position by the tool, the tool being moved by a movement machine, the device including: a camera arranged in a predetermined positional relationship with respect to the tool and configured to image the target position at a first time point when the tool performs an operation for the work; and a deviation amount acquisition section configured to acquire a deviation amount between the working position and the target position at the first time point, based on a position of the target position in image data imaged by the camera and information indicating a position of the working position in the image data.

Abstract: This device for simulating the operation of a robot comprises: an acquisition unit 341 that acquires the position, relative to a robot 100 having a shaft that can move in a rotational or linear manner as a joint, of a mounting article of the robot 100, the mounting article being mounted on a link that is connected to the joint, and also acquires a constraint for at least one of the speed, acceleration, and acting force generated in the mounting article when the robot 100 operates; a calculation unit 342 that calculates at least one of the speed, acceleration, and acting force anticipated to be generated in the mounting article in accordance with the position of the mounting article relative to the robot 100 when the robot 100 executes an operation; and a comparison unit 342 that compares the constraint and at least one of the speed, acceleration, and acting force calculated by the calculation unit 342.

Abstract: A vibration suppression device acquires a teaching position, computes a speed plan based on the acquired teaching position and a first acceleration/deceleration parameter, computes data related to deflection occurring during an acceleration/deceleration operation of a robot based on the teaching position and the speed plan, and acquires data indicating a posture at the teaching position. Further, a machine learning unit of the vibration suppression device estimates an acceleration/deceleration parameter with respect to the data related to the deflection and the data related to the posture using the data related to the deflection and the data related to the posture as input data.

Abstract: A device capable of acquiring a deviation of a working position of a tool with respect to a target position of a workpiece with higher accuracy in accordance with actual work. A device for acquiring a deviation amount of a working position of a tool with respect to a target position when a work is performed on a workpiece with respect to the target position by the tool, the tool being moved by a movement machine, the device including: a camera arranged in a predetermined positional relationship with respect to the tool and configured to image the target position at a first time point when the tool performs an operation for the work; and a deviation amount acquisition section configured to acquire a deviation amount between the working position and the target position at the first time point, based on a position of the target position in image data imaged by the camera and information indicating a position of the working position in the image data.

Abstract: A gripper control device includes a controller, the controller is configured to conduct: a grasp quality evaluation process to obtain a grasp quality contribution degree for each of a plurality of contact positions at which fingers that support the object are in contact with the object; a finger-to-be-moved determination process configured to determine, as a finger-to-be-moved, the finger corresponding to one of the contact positions at which the grasp quality contribution degree is low; and a finger movement control process configured to send a control command to a finger driving apparatus, wherein the grasp quality contribution degree for each of the multiple contact positions is defined using at least one of the following elements: relative positions of the plurality of contact positions related to the object; and friction coefficients between the object and the fingers at the plurality of contact positions.

Abstract: A machine learning device that learns a control command for a machine by machine learning, including a machine learning unit that performs the machine learning to output the control command; a simulator that performs a simulation of a work operation of the machine based on the control command; and a first determination unit that determines the control command based on an execution result of the simulation by the simulator.

Abstract: A vibration suppression device acquires a teaching position, computes a speed plan based on the acquired teaching position and a first acceleration/deceleration parameter, computes data related to deflection occurring during an acceleration/deceleration operation of a robot based on the teaching position and the speed plan, and acquires data indicating a posture at the teaching position. Further, a machine learning unit of the vibration suppression device estimates an acceleration/deceleration parameter with respect to the data related to the deflection and the data related to the posture using the data related to the deflection and the data related to the posture as input data.

Abstract: A gripper control device includes a controller, the controller is configured to conduct: a grasp quality evaluation process to obtain a grasp quality contribution degree for each of a plurality of contact positions at which fingers that support the object are in contact with the object; a finger-to-be-moved determination process configured to determine, as a finger-to-be-moved, the finger corresponding to one of the contact positions at which the grasp quality contribution degree is low; and a finger movement control process configured to send a control command to a finger driving apparatus, wherein the grasp quality contribution degree for each of the multiple contact positions is defined using at least one of the following elements: relative positions of the plurality of contact positions related to the object; and friction coefficients between the object and the fingers at the plurality of contact positions.

Abstract: A cell controller capable of optimizing the operation of a production system having a plurality of industrial machines operated by an operation program. The cell controller includes: a system operational information analyzer configured to analyze time-series operational information received from the industrial machines via a network, so as to find a part in the system which generates an adverse effect on a cycle time of the entire production system; a state quantity analyzer configured to analyze a state quantity of the industrial machines so as to calculate a degree of margin of motion of each industrial machine; a program modifier configured to automatically modify a velocity or acceleration in the operation program based on the degree of margin; and a simulator configured to execute an operational simulation of the production system in order to confirm a modification result of the operation program.

Abstract: A machine learning device that learns a control command for a machine by machine learning, including a machine learning unit that performs the machine learning to output the control command; a simulator that performs a simulation of a work operation of the machine based on the control command; and a first determination unit that determines the control command based on an execution result of the simulation by the simulator.