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.

According to various embodiments of the present invention, an electronic device comprises: a memory including instructions and a training database, which includes data, on at least one object, acquired on the basis of an artificial intelligence algorithm; at least one sensor; and a processor connected to the at least one sensor and the memory, wherein the processor can be configured to execute the instructions in order to acquire data on a designated area including the at least one object by using the at least one sensor, identify location information and positioning information on the at least one object on the basis of the training database, and transmit a control signal for picking the at least one object to a picking tool related to the electronic device on the basis of the identified location information and positioning information.

Provided are a measurement system, a measurement device, a measurement method, and a measurement program. 3D data is registered to 3D data based on the displacements of joints of a robot at a point in time when a 3D sensor measures 3D data of a measurement object at a specific measurement point while the robot is stopped, and the displacements of the joints of the robot at a point in time when the 3D sensor measures 3D data of the measurement object at a measurement point other than the specific measurement point while that robot is in motion. The 3D data is further registered to the 3D data such that a registration error between the 3D data and the 3D data is less than a threshold value. Similarly, each of 3D data is registered to the 3D data.

A gripper including: an orientation sensor configured to generate an orientation reading for a target object; a first grasping blade and a second grasping blade configured to secure the target object in conjunction with the first grasping blade and at an opposite end of the target object relative to the first grasping blade; a first position sensor, of the first grasping blade, configured to generate a first position reading of the first grasping blade relative to the target object; a second position sensor, of the second grasping blade, configured to generate a second position reading of the second grasping blade relative to the target object; and a blade actuator configured to secure the target object with the first grasping blade and the second grasping blade based on a valid orientation of the orientation reading and based on the first position reading and the second position reading indicating a stable condition.

A machine vision-based method and system for measuring 3D pose of a part or subassembly of parts having an unknown pose are disclosed. A number of different applications of the method and system are disclosed including applications which utilize a reprogrammable industrial automation machine such as a robot. The method includes providing a reference cloud of 3D voxels which represent a reference surface of a reference part or subassembly having a known reference pose. Using at least one 2D/3D hybrid sensor, a sample cloud of 3D voxels which represent a corresponding surface of a sample part or subassembly of the same type as the reference part or subassembly is acquired. The sample part or subassembly has an actual pose different from the reference pose. The voxels of the sample and reference clouds are processed utilizing a matching algorithm to determine the pose of the sample part or subassembly.

The electronic device manufacturing apparatus includes a cable holding tool, a work stage, a robot section, a first position detector, a second position detector, and a controller. The first position detector detects positions in a two-dimensional plane direction of the cable held by the cable holding tool and the connector of the electronic device held by the work stage. The second position detector detects positions in a height direction of the cable held by the cable holding tool and the connector of the electronic device held by the work stage. The controller controls the robot section based on the detection result of the first position detector and the second position detector.

A control server controls a dual-arm robotic manipulator (DARM) for handling deformable objects in a stack. The control server receives a set of images of the stack captured by a set of image sensors, and determines a contour of the stack based the set of images. Based on the contour and historical data associated with the deformable objects in the stack, the control server determines a sequence of actions to be performed by the DARM for handling a first deformable object in the stack, and controls the DARM to handle the first deformable object by communicating a set of commands corresponding to each action in sequence of actions. The first deformable object is handled such that original form factors of the first deformable object and the remaining stack are maintained.

An object detection device detects, when a camera that generates an image representing a target object and the target object do not satisfy a predetermined positional relationship, a position of the target object on the image by inputting the image to a classifier, and detects, when the camera and the target object satisfy the predetermined positional relationship, a position of the target object on the image by comparing, with the image, a template representing a feature of an appearance of the target object when the target object is viewed from a predetermined direction.

A robot system including: one or two robot arms which perform work on a target; a shape measurement device which is disposed on the robot arm and measures a shape of the target; and a controller which controls the robot arm based on the shape measurement device, in which the shape measurement device includes a projection device which projects striped pattern light onto the target, an image capturing unit which captures the image of the pattern light, and a processor which calculates the shape of the target based on the captured image by the image capturing device, and in which the projection device includes a light source device which emits linear laser, an optical scanner which generates the pattern light by reflecting the laser from the light source device and by scanning the target, and a scanner driver which outputs a driving signal to drive the optical scanner non-resonantly.

Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.