A programmable logic controller controls at least one robot to manipulate a plurality of workpieces. The programmable logic controller includes a sensor interface configured to receive sensor data that represents information of the workpieces. The programmable logic controller includes a scheduler configured to create a schedule that includes information representing an order in which the workpieces are to be manipulated. The schedule created by the scheduler is based on the sensor data. The programmable logic controller includes a synchronizer that is configured to receive the schedule. The synchronizer is configured to cause a robot to manipulate the workpieces based on the schedule and based on a function block. The function block is configured via the programmable logic controller.

Example methods and systems for determining 3D scene geometry by projecting patterns of light onto a scene are provided. In an example method, a first projector may project a first random texture pattern having a first wavelength and a second projector may project a second random texture pattern having a second wavelength. A computing device may receive sensor data that is indicative of an environment as perceived from a first viewpoint of a first optical sensor and a second viewpoint of a second optical sensor. Based on the received sensor data, the computing device may determine corresponding features between sensor data associated with the first viewpoint and sensor data associated with the second viewpoint. And based on the determined corresponding features, the computing device may determine an output including a virtual representation of the environment that includes depth measurements indicative of distances to at least one object.

The present disclosure provides a control method for goods retrievement and storage, a control apparatus, and a transport robot. The control method for goods retrievement includes: receiving a retrievement instruction, and acquiring locating information of target goods according to the retrievement instruction; moving a transport robot to a target position according to the locating information; obtaining status information of the target goods and/or position relationship information between a carrying apparatus and the target goods; and adjusting a position and posture of the carrying apparatus according to the status information and/or the position relationship information, and causing the carrying apparatus to take out the target goods. According to the present disclosure, the position of the target goods can be accurately determined by obtaining status information of the target goods and/or position relationship information between the carrying apparatus and the target goods, so that the target goods can be accurately retrieved.

In one embodiment, a method includes receiving, from a first sensor on a robot, first sensor data indicative of an environment of the robot. The method also includes identifying, based on the first sensor data, an object of an object type in the environment of the robot, where the object type is associated with a classifier that takes sensor data from a predetermined pose relative to the object as input. The method further includes causing the robot to position a second sensor on the robot at the predetermined pose relative to the object. The method additionally includes receiving, from the second sensor, second sensor data indicative of the object while the second sensor is positioned at the predetermined pose relative to the object. The method further includes determining, by inputting the second sensor data into the classifier, a property of the object.

A closed-circuit television (CCTV) online pipeline detection system includes a detection robot and a control terminal of the detection robot, where the detection robot is provided with a CCTV pipeline detection system adapted to the detection robot; the CCTV pipeline detection system includes a GTR8600 monitoring module, a drive device, and a power drive device; the control terminal is separately electrically connected with the GTR8600 monitoring module, the drive device, and the power drive device through a control system; and the control system is provided inside the control terminal.

A computer-implemented method executed by data processing hardware of a robot causes the data processing hardware to perform operations. The operations include receiving a sensor pointing command that commands the robot to use a sensor to capture sensor data of a location in an environment of the robot. The sensor is disposed on the robot. The operations include determining, based on an orientation of the sensor relative to the location, a direction for pointing the sensor toward the location, and an alignment pose of the robot to cause the sensor to point in the direction toward the location. The operations include commanding the robot to move from a current pose to the alignment pose. After the robot moves to the alignment pose and the sensor is pointing in the direction toward the location, the operations include commanding the sensor to capture the sensor data of the location in the environment.

A device may receive, from a robotic device, time-of-flight sensor data that includes a point cloud derived from depth images of an environment with a floor and one or more obstacles, and may shift the depth images by a number of pixels to generate shifted depth images. The device may subtract the shifted depth images from the depth images to generate final images, and may calculate Euclidean distances associated with the final images. The device may generate masks for the final images associated with Euclidean distances that are less than a search radius, and may calculate a covariance matrix based on the masks. The device may calculate eigenvectors for the covariance matrix, and may generate an occupancy grid for the environment based on the eigenvectors for the covariance matrix.

A machine learning method for learning an action of a robot including a hand to pick out a workpiece from a container containing a plurality of the workpieces stacked in bulk and install the workpiece such that the workpiece is in a predetermined installation state includes learning a reverse-order action of removing, by the hand, the workpiece in the predetermined installation state after completion of installation, and learning an installation order of the workpiece based on a learning result of the reverse-order action of removing the workpiece.

A computer-implemented method executed by data processing hardware of a robot causes the data processing hardware to perform operations. The operations include receiving a sensor pointing command that commands the robot to use a sensor to capture sensor data of a location in an environment of the robot. The sensor is disposed on the robot. The operations include determining, based on an orientation of the sensor relative to the location, a direction for pointing the sensor toward the location, and an alignment pose of the robot to cause the sensor to point in the direction toward the location. The operations include commanding the robot to move from a current pose to the alignment pose. After the robot moves to the alignment pose and the sensor is pointing in the direction toward the location, the operations include commanding the sensor to capture the sensor data of the location in the environment.