Abstract
A robot can identify the position of a target and complete a grasping based on the hand–eye calibration algorithm, through which the relationship between the robot coordinate system and the camera coordinate system can be established. The accuracy of the hand–eye calibration algorithm affects the real-time performance of the visual servo system and the robot manipulation. The traditional calibration technique is based on a perfect mathematical model AX = XB, in which the X represents the relationship of (A) the camera coordinate system and (B) the robot coordinate system. The traditional solution to the transformation matrix has a certain extent of limitation and instability. To solve this problem, an optimized neural-network-based hand–eye calibration method was developed to establish a non-linear relationship between robotic coordinates and pixel coordinates that can compensate for the nonlinear distortion of the camera lens. The learning process of the hand–eye calibration model can be interpreted as B=fA, which is the coordinate transformation relationship trained by the neural network. An accurate hand–eye calibration model can finally be obtained by continuously optimizing the network structure and parameters via training. Finally, the accuracy and stability of the method were verified by experiments on a robot grasping system.
Highlights
With the widespread use of robots in many areas of modern industry, there is an increasing demand for stability and accuracy in robot grasping
The calibration method based on a neural network simplifies the process and improves the precision of the calibration
Robot hand–eye calibration based on neural network optimization is viable and can learn the parameter model of coordinate transformation from known data
Summary
With the widespread use of robots in many areas of modern industry, there is an increasing demand for stability and accuracy in robot grasping. Flexible manipulation in a complex environment relies heavily on visual feedback, with which the robot can recognize the object and control the manipulator to complete the grasp. The basis of the visual system is the hand–eye calibration that the relationship between the robot’s world coordinate system and the camera coordinate system can obtain [1]. The base coordinates of the object in space can be calculated from the pixel information in the camera with the aid of an obtained coordinate transformation relationship [2]. The accuracy of robot manipulation and the stability of the visual system are determined by the hand–eye calibration algorithm
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