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A general approach for robot pose error compensation based on an equivalent joint motion error model
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Tian, Wenjie, Huo, Mingshuai, Zhang, Xiangpeng, Song, Yongbin and Wang, Lina (2022) A general approach for robot pose error compensation based on an equivalent joint motion error model. Measurement, 203 . 111952. doi:10.1016/j.measurement.2022.111952 ISSN 0263-2241.
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Official URL: http://dx.doi.org/10.1016/j.measurement.2022.11195...
Abstract
A general error modelling, measurement, and compensation approach for robot calibration was proposed based on the equivalent joint motion error model from the perspective of vector space. First, the end pose error was expressed as a linear combination of the twists and the equivalent motion errors of the actuated joints, and the latter were described as functions of the ideal configuration. On this basis, the pose error in operating space was decoupled in joint space, and the regression model corresponding to each actuated joint was established. Then, a statistical index with dimensional consistency was proposed to evaluate the predictive capability of the model, and the variation of the prediction accuracy in the presence of system noise was studied. Finally, experiments were conducted adopting the strategy of off-line identification and on-line compensation. After calibration, the average value of robot position and attitude errors can be reduced to 0.046 mm and 0.011 deg, respectively.
| Item Type: | Journal Article | ||||||||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||||||
| Journal or Publication Title: | Measurement | ||||||||||||
| Publisher: | Elsevier | ||||||||||||
| ISSN: | 0263-2241 | ||||||||||||
| Official Date: | 15 November 2022 | ||||||||||||
| Dates: |
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| Volume: | 203 | ||||||||||||
| Number of Pages: | 13 | ||||||||||||
| Article Number: | 111952 | ||||||||||||
| DOI: | 10.1016/j.measurement.2022.111952 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
| Copyright Holders: | Elsevier | ||||||||||||
| RIOXX Funder/Project Grant: |
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