Abstract
The study proposed a robotic calibration algorithm for improving the robot manipulator position precision. At first, the kinematic parameters as well as the compliance parameters of the robot can be identified together to improve its accuracy using the joint deflection model and the conventional kinematic model calibration technique. Then, an artificial neural network is constructed for further compensating the unmodeled errors. The invasive weed optimization is used to determine the parameters of the neural network. To show the advantages of the suggested technique, an HH800 robot is employed for the experimental study of the proposed algorithm. The improved position precision of the robot after the experiment firmly proves the practicability and positional precision of the proposed method over the other algorithms in comparison.
Highlights
Robot manipulators are widely used in industry to attain many duties such as welding, painting, pick and place task, etc
A robot calibration system consists of a Hyundai HH800 robot
It is clear to see from the figure that the proposed method is the best over both the positions used in the calibration process and the general position in the overall workspace
Summary
Robot manipulators are widely used in industry to attain many duties such as welding, painting, pick and place task, etc. The kinematic and compliance parameters are simultaneously determined by a model-based calibration method [5]. It should be noted here that the proposed method is a combination of model-based and artificial neural network (ANN) methods that used the IWO technique to determine the weight and bias. Most of the ANN-based technique is applied after the kinematic calibration This calibration method simultaneously calibrates both the kinematic errors and joint compliances. A HH800 robot is employed for the experimental study of the proposed algorithm to compare with four other calibration methods, including the conventional kinematic calibration method (KM), as well as the simultaneous identification of joint compliance and kinematic parameters method (SKCM), and the combination of an NN compensator and SKCM method (NN-SKCM). This advantage makes the proposed method more feasible in real offline programming environments
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