A malfunction determination method for a production machine including a motor as a driving source of a rotating mechanism acquires sensor data of a sensor for detecting a condition of the production machine, determines whether the production machine has an operation stop period during which the production machine has stopped its operation for a predetermined period of time or longer in accordance with an operation history of the production machine, sets a malfunction determination suspension period for suspending a malfunction determination of the production machine when determined to have the operation stop period, in accordance with a length of the operation stop period, and determines whether the production machine has a malfunction in a period other than the malfunction determination suspension period.

A system and method for adaptive diagnostics and data collection in a connected robot-cloud environment allows for the management and use of date from a robot or fleet of robots to ensure the efficient utilization thereof. The data is collected from the robots via a software agent and is transmitted to an interface that allows action from an end-user.

A system and method palletize containers having electrical terminals for charging electronic devices packaged therein. First, a stacking pattern is determined on the basis of the sizes, shapes, and locations of electrical terminals on both the pallets and the containers to be stacked. These data may be read, for example, with a computer vision system that uses an articulating robotic arm, and may be encoded in a two-dimensional barcode on each pallet and/or container. Next, the robotic arm stacks the container so that its terminals make electrical contact with terminals on the pallet, or on a previously-stacked layer of containers. Then, the placement is tested to ensure that a good electrical connection exists vertically through the entire stack. Once the pallet is finalized, all electronic devices carried thereon may be simultaneously charged during transit or storage.

The invention relates to a robot controller controlling a robot arm, the robot controller is configured to maintain the robot arm in a static posture when only gravity is acting on the robot arm and allow change in posture of the robot arm when an external force different from gravity is applied to the robot arm. The free-drive mode of operation is activatable by a user establishing a free-drive activation signal to the robot controller, which then is configured to initiate a free-drive mode activation sequence including the steps of: in a predetermined activation sequence period of time monitor a value of at least one joint sensor parameter, and compare this value to a free-drive activation joint sensor parameter threshold value. The robot controller is configured to switch to the free-drive mode of operation if the at least one value does not exceed the free-drive activation joint sensor parameter threshold value within the predetermined activation sequence period of time.

The surgical robotic system includes a robotic arm having one or more joints, each having a motor and at least one torque sensor and a velocity sensor. The system also includes a main controller, which outputs a drive command to the motor thereby actuating the motor. The system further includes a safety observer, which receives a measured velocity of the motor from the sensor, calculates an observed velocity, and detects a failure in operation of the at least one joint based on the observed velocity and the measured velocity.

The inventive concept provides a substrate treating apparatus. The substrate treating apparatus includes a plurality of treating chambers performing a respective treatment on a substrate therein; a transfer chamber having a robot transferring the substrate between the plurality of treating chambers; a detection unit mounted on the robot and configured to detect a substrate state; and a controller for controlling the detection unit, wherein the detection unit comprises: an imaging member for imaging the substrate; and a driving member for moving the imaging member, and wherein the controller controls the detection unit to image and store an image of the substrate at an optimal position and determines whether an image of the substrate is a normal state based on the image obtained in the optimal position, the optimal position determined based on a process variable of the treating chamber.

An example robotic welding system, includes: a robotic manipulator configured to manipulate a welding torch; and a robotic controller, comprising: a processor; and a machine readable storage medium comprising machine readable instructions which, when executed by the processor, cause the processor to, in response to initiation of a robotic welding procedure involving the robotic manipulator: prior to starting the robotic welding procedure, output at least one of a visual notification or an audible notification proximate to the robotic manipulator; and after satisfying at least one weld-ready condition, control the robotic manipulator to perform the robotic welding procedure using the welding torch.

A method of evaluating safety of a robot includes a step of obtaining a three-dimensional image or three-dimensional model of a test robot comprising shape information of a real robot, a step of setting a movement time and movement path of the test robot by inputting profile information comprising movement time information and movement path information of the test robot, a step of calculating a collision pressure and collision force applied to a collision object in consideration of a shape, effective mass, movement speed, and direction of an injury-causing dangerous portion of the test robot, and a step of evaluating safety of the robot by determining whether magnitudes of the calculated collision pressure and collision force fall within magnitudes of a predetermined maximum collision pressure and predetermined maximum collision force.

A method for monitoring acceleration of a number A of axes of a multi-axis kinematic system utilizes a sampling process with a first sampling interval, wherein a first acceleration limit value assigned to the first sampling interval and a second different acceleration limit value is determined for the acceleration, where a second time interval is assigned to the second acceleration limit value, a plurality of position values of the axis is determined by sampling with the first sampling interval, a current acceleration is calculated via the ascertained position values, and the calculated current acceleration is monitored via a first instance of monitoring utilizing the first acceleration limit value and the assigned first sampling interval and, simultaneously, via a second instance of monitoring utilizing the second acceleration limit value and the assigned second time interval, such that acceleration of an axis is monitored using at least two acceleration limit values simultaneously.

The present invention provides a method for monitoring a balanced state of a humanoid robot, comprising: acquiring state data of the robot falling in different directions and being stable, forming a support vector machine (SVM) training data set and obtaining, by training, an initial SVM classifier; inputting the state data of the robot to the trained SVM classifier, so that the SVM classifier outputs a classification result; taking statistics on a proportion of cycles judged to have an impending fall in the total number of control cycles within a judgment buffer time after the SVM classifier outputs the classification result, and finally determining a monitoring result of the balanced state of the robot according to the proportion and finally extracting state data of misjudged cycles within the buffer time, adding the state data to the current training data set and updating the SVM classifier, eventually enabling the classifier to achieve the effects of matching motion capabilities of the robot and monitoring the balanced state.