Abstract
This paper proposes a new calibration method for enhancing robot positional accuracy of the industrial manipulators. By combining the joint deflection model with the conventional kinematic model of a manipulator, the geometric errors and joint deflection errors can be considered together to increase its positional accuracy. Then, a neural network is designed to additionally compensate the unmodeled errors, specially, non-geometric errors. The teaching-learning-based optimization method is employed to optimize weights and bias of the neural network. In order to demonstrate the effectiveness of the proposed method, real experimental studies are carried out on HH 800 manipulator. The enhanced position accuracy of the manipulator after the calibration confirms the feasibility and more positional accuracy over the other calibration methods.
Highlights
Robot manipulators are broadly employed in industry to attain many duties such as welding, painting, pick and place task, etc
There is a demand to create model-based robotic calibrations that depend on an error model that symbolizes the connection between the errors of geometric parameters and the arm’s tip positioning errors
The combination of simultaneous hybrid calibration with a teaching-learning-based optimization (TLBO) based neural network is shown to lead to the better capability of reducing the errors of the robot than other calibration methods
Summary
Robot manipulators are broadly employed in industry to attain many duties such as welding, painting, pick and place task, etc. Using the non-geometric error approach, some authors employ optimization methods to optimize the robot parameters [15]–[22]. The kinematic parameters and joint compliance parameters of the robot are simultaneously identified first by our model-based calibration [12]. The teaching-learning-based-optimization(TLBO) is employed to optimize the weight and bias of the neural network. The TLBO-NN based compensation is accomplished for the un-modelled non-geometric errors. The combination of simultaneous hybrid calibration with a TLBO based neural network is shown to lead to the better capability of reducing the errors of the robot than other calibration methods. The proposed calibration method utilizes the advantages of the TLBO neural network over the conventional back propagation neural network such as better error reducing capability and better global minimum reaching capability.
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