A first image obtained by capturing an image of an object on which pattern light is projected and a second image obtained by capturing an image of the object on which light not containing the pattern light is projected are acquired. A first distance image having a distance value for each pixel is generated based on the first image and distortion of the first image is corrected based on the distance value of the first distance image. A second distance image having a distance value for each pixel is generated based on the first image obtained after correcting distortion of the first image. Distortion of an edge image having edge information of the object in the second image is corrected by using the distance value of a corresponding pixel in the first distance image or the second distance image for each pixel of the edge image.

A robot-defective-part diagnostic device includes a position measuring unit with a target and a sensor for capturing an image of the target. One of the target and the sensor is attached to the robot and the other is disposed outside the robot. The position measuring unit measures the positions of the target with the sensor for postures of the robot. The device also includes an error calculation unit that calculates the positioning error based on the measured positions of the target. A parameter calculation unit that calculates the mechanical parameters of the respective operation shafts based on the measured positions of the target for the respective postures when the calculated positioning error is larger than a predetermined threshold, and a defective-part identifying unit that identifies the operation shaft where the difference between the calculated mechanical parameters and preset mechanical parameters for achieving the postures is the largest.

A work robot system including a conveying apparatus that conveys an object, a robot that performs a predetermined task on a target portion of the object being conveyed by the conveying apparatus, a controller that controls the robot, a sensor that is attached to the robot and successively detects a position, relative to the robot, of the target portion of the object being conveyed by the conveying apparatus, and a force detector that detects a force generated by a contact between the object and a part supported by the robot. When the robot is performing the predetermined task, the controller performs force control based on a detection value of the force detector while controlling the robot by using a detection result of the sensor.

The present disclosure provides a method for simultaneously performing a robot's kinematic calibration and hand-eye calibration. One exemplary method comprises acquiring a plurality of point clouds of a calibration fixture and formulating this simultaneous calibration problem as an optimization problem based on the collection of point clouds of the same fixture.

A camera abnormality cause estimation system for estimating the causes of abnormalities in a camera in a production system in which the camera controls a robot. The production system includes a robot, a camera that detects visual information of the robot or the surrounding thereof, and a controller that controls the robot based on an image signal obtained by the camera. The camera abnormality cause estimation system estimates the causes of abnormalities in the camera and includes an environment information acquisition unit that acquires environment information of the camera, and an abnormality cause estimation unit that estimates a probability that each of a plurality of predetermined abnormality cause items is the cause of an abnormality in the camera for the respective abnormality cause items using the environment information acquired by the environment information acquisition means and displays the estimated probability on a display unit for the respective abnormality cause items.

An apparatus for controlling a robot arm includes: the robot arm; a calibration board on which calibration marks for self-diagnosis are shown; a distance sensor mounted on the robot arm and configured to measure a distance; an image sensor mounted on the robot arm and configured to obtain an image; and a processor configured to move the robot arm to a position for the self-diagnosis, measure a distance from a predetermined part of the robot arm to the calibration board by using the distance sensor, obtain an image of the calibration board by using the image sensor, and output a signal indicating a malfunction of the robot arm in response to the measured distance being outside a distance error range, and an image measurement value of the obtained image being outside an image error range.

An automatic robotic arm system and a coordinating method for robotic arm and computer vision thereof are disclosed. A beam-splitting mirror splits an incident light into a visible light and a ranging light and respectively guides to an image capturing device and an optical ranging device arranged in the different reference axes. In a calibration mode, a transformation relation is computed based on a plurality of the calibration postures and corresponding calibration images. In an operation mode, a mechanical space coordinate is determined based on an operation image and the transformation relation, and the robotic arm is controlled to move based on the mechanical space coordinate.

Disclosed are an automatic welding system and method for large structural parts based on hybrid robots and 3D vision. The system comprises a hybrid robot system composed of a mobile robot and an MDOF robot, a 3D vision system, and a welding system used for welding. The rough positioning technique based on a mobile platform and the accurate recognition and positioning technique based on high-accuracy 3D vision are combined, so the working range of the MDOF robot in the XYZ directions is expanded, and flexible welding of large structural parts is realized. The invention adopts 3D vision, thus having better error tolerance and lower requirements for the machining accuracy of workpieces, positioning accuracy of mobile robots and placement accuracy of the workpieces; and the cost is reduced, the flexibility is improved, the working range is expanded, labor is saved, production efficiency is improved, and welding quality is improved.

Described are machine vision systems and methods for simultaneous kinematic and hand-eye calibration. A machine vision system includes a robot and a 3D sensor in communication with a control system. The control system is configured to move the robot to poses, and for each pose: capture a 3D image of calibration target features and robot joint angles. The control system is configured to obtain initial values for robot calibration parameters, and determine initial values for hand-eye calibration parameters based on the initial values for the robot calibration parameters, the 3D image, and joint angles. The control system is configured to determine final values for the hand-eye calibration parameters and robot calibration parameters by refining the hand-eye calibration parameters and robot calibration parameters to minimize a cost function.

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.