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.

The present application provides a localization system and method, and robot using the same. The localization system comprises: a storage device, configured to store the corresponding relationship between an image coordinate system and a physical space coordinate system; an image acquisition device, configured to capture image frames during movement of the robot; and a processing device, connected with the image acquisition device and the storage device, and configured to acquire positions of visual features in an image frame at the current time and positions of the corresponding visual features in an image frame at the previous time and to determine the position and pose of the robot according to the corresponding relationship and the positions. In the present application, the localization error of the robot can be effectively reduced.

Provided is a feeder management device that appropriately controls editing of management information related to feeders so as to prevent electronic components of an incorrect type being supplied. The feeder management device includes: a data management section including management data in which a component type of an electronic component loaded on each of the feeders is linked to an identification code that specifies each of the multiple feeders; and an editing control section configured to determine whether editing is allowable based on a loading state of the electronic component on the feeder in a case in which editing of the management information is attempted to be performed in accordance with a change to the component type linked to the identification code of the feeder.

The present application discloses systems and methods for inventorying objects. In one embodiment, a robot detects an object and sends identification data and location data associated with the detected object to a cloud computing system. The identification data may include an image of the object and/or information from a tag, code, or beacon associated with the object. In response to receiving the identification data and the location data, the cloud computing system identifies the object. The cloud computing system may also determine or create a first map associated with the identified object and a second map associated with the identified object. The first map may be associated with the current location of the object and the second map may correspond to a past location of the object. The cloud computing server may compare the first and second maps, and then send instructions to the robot based on the comparison.

When occurrence of an obstruction has been detected during action of a robot, a path generation section acquires environment information at a periphery of the robot after obstruction occurred, robot specification information, and safe pose information representing a recovery-pose for the robot, and generates a path of the robot from a pose after obstruction occurred to a safe pose based on the acquired information.

A method for detecting and re-identifying objects using a neural network. The method includes the steps: extracting features from an image, the features comprising information about at least one object in the image; detecting the at least one object in the image using an anchor-based object detection based on the extracted features, classification data being determined by a classification for detecting the object with the aid of at least one anchor and regression data being determined by a regression; and re-identifying the at least one object by determining embedding data based on the extracted features, the embedding data representing an object description for the at least one feature of the image.

Disclosed is an interior tracking system for manufacturing control. The interior tracking system has multiple, fixedly installed transceivers for determining the position of multiple mobile units, the position being determined in particular by evaluating the propagation time of electromagnetic (radio) signals. The interior tracking system is used to assign one of the mobile units to one or more workpieces in an industrial manufacturing plant that processes steel and/or sheet metal, to determine the position of the assigned workpieces by localizing the assigned mobile unit using the interior tracking system and to integrate the interior tracking system into a manufacturing control system of the industrial manufacturing plant.

A method for identifying an electronic component includes receiving an activity signal output by the electronic component when a light intensity incident on the electronic component exceeds a predeterminable threshold. The activity signal is assignable to the electronic component. The method further includes outputting the identification of the electronic component based on the received activity signal.

A method and system for piece-picking or piece put-away within a logistics facility. The system includes a central server and at least one mobile manipulation robot. The central server is configured to communicate with the robots to send and receive piece-picking data which includes a unique identification for each piece to be picked, a location within the logistics facility of the pieces to be picked, and a route for the robot to take within the logistics facility. The robots can then autonomously navigate and position themselves within the logistics facility by recognition of landmarks by at least one of a plurality of sensors. The sensors also provide signals related to detection, identification, and location of a piece to be picked or put-away, and processors on the robots analyze the sensor information to generate movements of a unique articulated arm and end effector on the robot to pick or put-away the piece.

A three-dimensional detailed position/orientation estimation apparatus includes a first position/orientation estimation unit and a second position/orientation estimation unit that are configured to estimate three-dimensional position and orientation. The first position/orientation estimation unit optimizes six parameters (translations x, y, and z, and rotations , , and ) using 3D data, and the second position/orientation estimation unit optimizes only three parameters (translations x and y, and rotation ) that can be estimated with high accuracy using a 2D image, based on the result of the three-dimensional position/orientation estimation performed by the first position/orientation estimation unit using the 3D data.