Abstract
In this paper, a new method for the calibration of robotic cell components is presented and demonstrated by identification of an industrial robotic manipulator’s base and end-effector frames in a workplace. It is based on a mathematical approach using a Jacobian matrix. In addition, using the presented method, identification of other kinematic parameters of a robot is possible. The Universal Robot UR3 was later chosen to prove the working principle in both simulations and experiment, with a simple repeatable low-cost solution for such a task—image analysis to detect tag markers. The results showing the accuracy of the system are included and discussed.
Highlights
For robotic arms there has always been a trade off between the repeatability and absolute accuracy of the measurement of a robot’s positioning in 3D space, as examined by Abderrahim [1] or byYoung [2]
A generally suggested method for robot calibration is the use of a laser tracker
We propose a solution based on the OpenCV libraries [18] for Aruco tag detection by a camera, which adds to the calibration process benefits of simplicity, repeatability and low price
Summary
For robotic arms there has always been a trade off between the repeatability and absolute accuracy of the measurement of a robot’s positioning in 3D space, as examined by Abderrahim [1] or byYoung [2]. For robotic arms there has always been a trade off between the repeatability and absolute accuracy of the measurement of a robot’s positioning in 3D space, as examined by Abderrahim [1] or by. Identification is the process in which a real robot’s kinematic (and possibly dynamic) characteristics are compared with its mathematical model. It includes determination of the error values that are afterwards applied into the control system, which improves the robot’s total pose accuracy using a software solution without the need for adjusting the hardware of the robot. The methodology identifies the error parameters of a robot’s kinematic structure, as is described by Nubiola [4]. In [6] Nguyen added neural network to compensate for non-geometric errors after the parameter identification was performed
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