A failure prediction method of predicting a failure of a component of a robot including a robot arm having the component and a detection section that detects information on vibration characteristics when the robot arm moves, includes generating a failure prediction model for prediction of the failure of the component by machine learning based on the information on vibration characteristics, and predicting the failure of the component based on an estimated value of failure prediction output by the generated failure prediction model when the information on vibration characteristics is input to the generated failure prediction model.

Provided is a control device capable of automatically determining whether or not an inertia estimation function needs to be activated. The control device 10 is for an electric motor and comprises: a first inertia estimation unit 11 that estimates whether or not there has been a change in the inertia of an object to be driven, on the basis of at least one among first information pertaining to an operation program or operation settings for a device comprising the electric motor, second information obtained from a detection device for detecting the shape of the object to be driven by the electric motor, and third information indicating the operation state of the electric motor; and a second inertia estimation unit 12 that estimates the inertia of the object to be driven if the first inertia estimation unit 11 has estimated that there has been a change in the inertia of the object to be driven.

A position measuring device includes a scale carrier with a measuring scale. A scanner is configured to generate position signals by scanning the measuring scale. A processor is configured to process the position signals into a digital position value. An interface is configured to communicate with downstream electronics. At least one collision sensor is assigned to the position measuring device, and is configured to generate analog or digital measured values from a time characteristic of which collision events are determinable. The measured values are fed to an evaluator configured to determine the collision events by evaluating the time characteristic of the measured values in a controller.

An automatic system level testing (ASLT) system for testing smart devices is disclosed. The system comprises a system controller coupled to and operable to stress a smart device in an enclosure, wherein the enclosure comprises a plurality of components, and wherein the system controller comprises: (a) a memory comprising test logic; and (b) a processor configured to automatically control the plurality of components and test the smart device in accordance with the test logic. Further, the plurality of components comprises: (a) a robotic arm comprising a stylus affixed thereto, wherein the stylus is operable to manipulate the smart device; and (b) a platform comprising a device holder affixed thereto, wherein the device holder is operable to receive the smart device, and wherein the platform and the robotic arm are robotically controlled to move by the processor.

An industrial robot system including a dual arm robot having two arms independently movable in relation to each other, and a hand-held control device for controlling the robot and provided with a visual display unit for displaying information about the arms. The control device is provided with a measuring device for measuring the orientation of the control device, and the control device is configured to display information about one of the arms in a first area on the display unit and to display information about the other arm in a second area on the display unit, and to change the positions of the first and second areas in dependence on the orientation of the control device in relation to the robot so that the positions of the first and second area on the display unit reflects the orientation of the control device in relation to the positions of the arms.

An Inertial Measurement Unit (IMU) is placed on a robot and outputs inertial information such as angular velocity and linear acceleration of the tip to which it is attached. A robot controller implements a recursive estimation algorithm to fuse robot encoder values with IMU data to determine a statistically optimized estimated position of the robot tip.

A control device controls a robot in which a sensor is disposed, the control device comprising: a sensor coordinate storage unit for storing coordinate system information of a sensor coordinate system; a sensor setting storage unit for storing setting information; a sensor data receiving unit for receiving sensor data; an axis angle detection unit for detecting an angle of each of a plurality of axes included in the robot; a sensor value estimation unit for estimating a sensor value to be detected; and an anomalous sensor value determination unit for comparing the value of the sensor data and the sensor value estimated by the sensor value estimation unit and determining that the sensor data receiving unit is receiving sensor data from a sensor of another robot if the difference between the value of the sensor data and the estimated sensor value exceeds a preset threshold value.

The disclosed technology relates to an intelligent motion control system that utilizes onboard sensors and processing to guide a surface manipulation machine along a path of travel on a surface, confirm a position of the machine with respect to the surface, and actuate a surface manipulation tool to achieve a desired surface profile or locate a point of interest. The system may include a first and second surface profiler that is configured to scan a surface on which the system travels and a positional sensor configured to generate positional data representing a position of the machine. The processor is configured to generate topography data based on output received from the first surface profiler, generate intermediate data based on output received from the second profiler, compare the intermediate data with the topography data to calculate an offset; and control motion of the system based on the offset.

An automatic system level testing (ASLT) system for testing smart devices is disclosed. The system comprises a system controller operable to be coupled with a smart device in an enclosure, wherein the system controller comprises a memory comprising test logic and a processor. The system also comprises the enclosure, wherein the enclosure comprises a plurality of components, the plurality of components comprising: (i) a robotic arm comprising a stylus, wherein the stylus is operable to manipulate the smart device to simulate human interaction therewith; and (ii) a platform comprising a device holder, wherein the device holder is operable to receive a smart device inserted there into. The processor is configured to automatically control the smart device and the plurality of components in accordance with the test logic.

An emergency stop system for suppressing the possibility that an emergency stop state of a machine is released due to the shock of the falling of an emergency stop switch that stops an operation of the machine in an emergency is provided. The emergency stop system includes: a fall detection unit that detects falling of an emergency stop switch on the basis of a detection value obtained by an acceleration sensor; a shock detection unit that detects shock after the falling of the emergency stop switch on the basis of the detection value of the acceleration sensor; a switch state detection unit that detects an ON-OFF state of the emergency stop switch; and a signal output unit that outputs an emergency stop signal to the machine regardless of the ON-OFF state of the emergency stop switch when the switch state detection unit has detected switching of the ON-OFF state of the emergency stop switch within a predetermined period after the fall detection unit detected the falling and the shock detection unit detected the shock.