A method calculates a calibration parameter for a robot tool. The method is based on the reception of an image dataset from medical imaging of an image volume via a first interface. The image volume contains a part of the robot tool and the robot tool is attached to a robot. A robot dataset is received by a second interface. The robot dataset contains a position of a movable axis of the robot during the recording of the image dataset. The position and/or orientation of a marking in the image dataset are determined by a computing unit. An image-based position and/or orientation of the tool center point of the robot tool are calculated by transforming the position and/or orientation of the marking. The calibration parameter is calculated based on the robot dataset and on the image-based position and/or orientation of the tool center point via the computing unit.

A robotic cell calibration method comprising a robotic cell system having elements comprising: one or more cameras, one or more sensors, components, and a robotic arm. The method comprises localizing positions of the one or more cameras and components relative to a position of the robotic arm using a common coordinate frame, moving the robotic arm in a movement pattern, and using the cameras and sensors to determine robotic arm position at multiple times during the movement. The method includes identifying a discrepancy in robotic arm position between a predicted position and the determined position in real time, and computing, by an auto-calibrator, a compensation for the identified discrepancy, the auto-calibrator solving for the elements in the robotic cell system as a system. The method includes modifying actions of the robotic arm in real time during the movement based on the compensation.

A method, a drone device, and an adaptive robot control system (ARCS) for adaptively controlling a programmable robot are provided. The ARCS receives environmental parameters of a work environment where the drone device operates and geometrical information of a target object to be operated on by the programmable robot. The ARCS dynamically receives a calibrated spatial location of the target object in the work environment based on the environmental parameters and a discernment of the target object from the drone device. The ARCS determines control information including parts geometry of the target object, a task trajectory of a task to be performed on the target object, and a collision-free robotic motion trajectory for the programmable robot, and dynamically transmits the control information to the programmable robot via a communication network to adaptively control the programmable robot while accounting for misalignments of the target object in the work environment.

[Object] To calibrate an internal model more efficiently and more precisely. [Solution] Provided is a robot arm apparatus including: an arm unit made up of a plurality of links joined by one or a plurality of a joint unit, the arm unit being connectable to an imaging unit. An internal model including at least geometric information about the arm unit and focus position information about the imaging unit is updated using internal model information acquired in a state in which the imaging unit is pointed at a reference point in real space.

A robotic cell calibration method comprising a robotic cell system having elements comprising: one or more cameras, one or more sensors, components, and a robotic arm. The method comprises localizing positions of the one or more cameras and components relative to a position of the robotic arm using a common coordinate frame, moving the robotic arm in a movement pattern, and using the cameras and sensors to determine robotic arm position at multiple times during the movement. The method includes identifying a discrepancy in robotic arm position between a predicted position and the determined position in real time, and computing, by an auto-calibrator, a compensation for the identified discrepancy, the auto-calibrator solving for the elements in the robotic cell system as a system. The method includes modifying actions of the robotic arm in real time during the movement based on the compensation.

Provided is a method for improved hand-eye calibration method based on a structured light camera. The method includes: step 1: establishing a pinhole camera model, and using a depth camera to detect a three-dimensional (3D) coordinate to obtain a physical coordinate of each point in an image coordinate system with a known depth relative to a camera coordinate system; step 2: establishing a Denavit-Hartenberg (DH) model of a robotic arm, and moving the robotic arm to a determined coordinate using inverse kinematics; step 3: collecting n sets of point cloud data, applying depth scaling coefficients to the n sets of point cloud data to perform Singular Value Decomposition (SVD), and solving for an optimal depth scaling coefficient using a Nelder-Mead algorithm; and step 4: completing the hand-eye calibration of the robotic arm based on the solved optimal depth scaling coefficient. The method offers strong operability and robustness.

A robot control system and a method for automatic camera calibration is presented. The robot control system includes a control circuit configured to determine all corner locations of an imaginary cube that fits within a camera field of view, and determine a plurality of locations that are distributed on or throughout the imaginary cube. The control circuit is further configured to control a robot arm to move a calibration pattern to the plurality of locations, and to receive a plurality of calibration images corresponding to the plurality of locations, and to determine respective estimates of intrinsic camera parameters based on the plurality of calibration images, and to determine an estimate of a transformation function that describes a relationship between a camera coordinate system and a world coordinate system. The control circuit is further configured to control placement of the robot arm based on the estimate of the transformation function.