Abstract
Robot position accuracy plays a very important role in advanced industrial applications. This article proposes a new method for enhancing robot position accuracy. In order to increase robot accuracy, the proposed method models and identifies determinable error sources, for instance, geometric errors and joint deflection errors. Because non-geometric error sources such as link compliance, gear backlash, and others are difficult to model correctly and completely, an artificial neural network is used for compensating for the robot position errors, which are caused by these non-geometric error sources. The proposed method is used for experimental calibration of an industrial Hyundai HH800 robot designed for carrying heavy loads. The robot position accuracy after calibration demonstrates the effectiveness and correctness of the method.
Highlights
In the off-line programming (OLP), the joint angles are desired to achieve a given Cartesian position of the arm’s tip by using a kinematic model of the robot arm
Some methods of calibrating precisely kinematic and non-kinematic parameters are required to improve the accuracy of robot manipulators
Because the robot consists of a main open chain and a closed-loop mechanism (Figure 1), in order to identify the kinematic parameters of the entire robot kinematic model, robot identification equations are formulated by integrating the differential equations of the open chain and the closed loop via the passive joint angle u3p
Summary
In the off-line programming (OLP), the joint angles are desired to achieve a given Cartesian position of the arm’s tip by using a kinematic model of the robot arm. We model and identify robot kinematic error parameters, including geometric errors and joint compliance errors. The robot residual position errors, which are caused by other non-geometric errors such as link deflection and gear backlash, are compensated for by using an ANN. We built the robot kinematic model consisting of geometric and joint compliance parameters.
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