Abstract
The accuracy of industrial robots, typically on the order of several millimeters, has inhibited their adoption in advanced aerospace manufacturing applications, such as robotic machining. For this reason, extensive investigations have been conducted to develop solutions to improve their accuracy. These solutions can be categorized into offline and online compensation strategies, both of which have advantages and limitations. Offline compensation has been shown to improve the accuracy of industrial robots by two to three orders of magnitude; however, the sensitivity of these solutions to environmental changes and external disturbances can degrade their performance. In contrast, online compensation, which utilizes real-time measurement and compensation, is robust to these changes; however, the performance of these solutions is limited by the causality of time, preventing them from compensating fast changing errors such as backlash. In this work, a novel kinematic modeling strategy, which models the robot's angular positioning deviations to predict when backlash will occur, is integrated into a novel online kinematic error compensation framework to leverage the advantages of both solutions. By integrating the proposed kinematic model into the online compensation framework, backlash errors can be predicted and preemptively compensated to improve performance. The performance of the combined system was evaluated using ball bar measurements, where it was shown to improve the circularity of the ball bar motion by 25 %.
Recommended Citation
M. R. Woodside et al., "A Feedforward Kinematic Error Controller with an Angular Positioning Deviations Model for Backlash Compensation of Industrial Robots," Manufacturing Letters, vol. 41, pp. 1579 - 1584, Elsevier, Oct 2024.
The definitive version is available at https://doi.org/10.1016/j.mfglet.2024.09.184
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
Angular Positioning Deviations; Backlash; Feedforward; Kinematic Error
International Standard Serial Number (ISSN)
2213-8463
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Oct 2024
Comments
American Petroleum Institute, Grant None