Abstract
Robot positioning accuracy plays an important role in industrial automation applications. In this paper, a method is proposed for the improvement of robot accuracy with an optical tracking system that integrates a least-square numerical algorithm for the identification of kinematic parameters. In the process of establishing the system kinematics model, the positioning errors of the tool and the robot base, and the errors of the Denavit-Hartenberg parameters are all considered. In addition, the linear dependence among the parameters is analyzed. Numerical simulation based on a 6-axis UR robot is performed to validate the effectiveness of the proposed method. Then, the method is implemented on the actual robot, and the experimental results show that the robots can reach desired poses with an accuracy of ±0.35 mm for position and ±0.07° for orientation. Benefitting from the optical tracking system, the proposed procedure can be easily automated to improve the robot accuracy for applications requiring high positioning accuracy such as riveting, drill, and precise assembly.
Highlights
Industrial robots have been widely applied for manufacturing automation in high-volume production due to their good task repeatability features
Offline programming can significantly reduce the workload for robot teaching, the generated robot paths are based on the robot’s nominal kinematic model and, whether the robot can successfully complete the task via offline programming depends on its absolute accuracy
The robot is taught to insert the pin into these holes using a traditional teaching method, and we can obtain the positional relationship of the teaching point relative to the base frame
Summary
Industrial robots have been widely applied for manufacturing automation in high-volume production due to their good task repeatability features. In a common scenario of the use of these machines, a human operator teaches the robot to move to a desired position; the robot records this position and repeats the taught path to complete the task. Offline programming can significantly reduce the workload for robot teaching, the generated robot paths are based on the robot’s nominal kinematic model and, whether the robot can successfully complete the task via offline programming depends on its absolute accuracy. Industrial robots still face challenges in many low-volume applications where high absolute accuracy (with the positioning error less than 0.50 mm for an industrial robot of a medium to large size) is required, such as milling, drilling, and precise assembly
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