Скачать или смотреть A novel vision-based calibration framework for industrial robotic manipulators

With the increasing involvement of industrial robots in manufacturing processes, the demand for high quality
robots has increased considerably. A high-quality robot is a robot having good repeatability and accuracy.
Industrial robots are known to have very good repeatability, however it is not the same with accuracy. Due
to harsh working conditions, accuracy of robots deteriorate over time. Calibration is a practical approach to
sustain accuracy. In calibration, position and orientation of the tool center point (TCP) of a robot arm should
be corrected using a tracking device with higher accuracy. Different devices such as laser-trackers, optical
CMMs, and stereo cameras have been used in the literature. In this paper, a novel calibration framework
is proposed based on a single camera and computer vision techniques using ArUco markers. The product of
exponentials method is used for kinematic modeling of the robot to avoid the singularity. The performance
of the framework is tested using computer-based simulations and using a six degree of freedom (6-DOF) UR5
robotic manipulator. Position and orientation errors are used as metrics in the experiments. The position and
orientation errors in real world experiments reached to 2.5 mm and 0.2◦
, respectively. The result shows that
the method is usable in real world scenarios.