Study on the Modeling and Compensation Method of Pose Error Analysis for the Fracture Reduction Robot

Minghe Liu; Jian Li; Hao Sun; Xin Guo; Bokai Xuan; Lifang Ma; Yuexuan Xu; Tianyi Ma; Qingsong Ding; Baichuan An

doi:10.3390/mi13081186

Micromachines (Jul 2022)

Study on the Modeling and Compensation Method of Pose Error Analysis for the Fracture Reduction Robot

Minghe Liu,
Jian Li,
Hao Sun,
Xin Guo,
Bokai Xuan,
Lifang Ma,
Yuexuan Xu,
Tianyi Ma,
Qingsong Ding,
Baichuan An

Affiliations

Minghe Liu: School of Artificial Intelligence and Data Science and Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Hebei University of Technology, Tianjin 300130, China
Jian Li: School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
Hao Sun: School of Artificial Intelligence and Data Science and Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Hebei University of Technology, Tianjin 300130, China
Xin Guo: School of Artificial Intelligence and Data Science and Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Hebei University of Technology, Tianjin 300130, China
Bokai Xuan: School of Artificial Intelligence and Data Science and Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Hebei University of Technology, Tianjin 300130, China
Lifang Ma: Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability and Key Laboratory of Neuro-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, National Research Center for Rehabilitation Technical Aids, Beijing 100176, China
Yuexuan Xu: School of Artificial Intelligence and Data Science and Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Hebei University of Technology, Tianjin 300130, China
Tianyi Ma: School of Artificial Intelligence and Data Science and Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Hebei University of Technology, Tianjin 300130, China
Qingsong Ding: School of Artificial Intelligence and Data Science and Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Hebei University of Technology, Tianjin 300130, China
Baichuan An: School of Artificial Intelligence and Data Science and Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Hebei University of Technology, Tianjin 300130, China

DOI: https://doi.org/10.3390/mi13081186
Journal volume & issue: Vol. 13, no. 8
p. 1186

Abstract

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Background: In the process of fracture reduction, there are some errors between the actual trajectory and the ideal trajectory due to mechanism errors, which would affect the smooth operation of fracture reduction. To this end, based on self-developed parallel mechanism fracture reduction robot (FRR), a novel method to reduce the pose errors of FRR is proposed. Methods: Firstly, this paper analyzed the pose errors, and built the model of the robot pose errors. Secondly, mechanism errors of FRR were converted into drive bar parameter’s errors, and the influence of each drive bar parameter on the robot pose error were analyzed. Thirdly, combining with Cauchy opposition-based learning and differential evolution algorithm (DE), an improved whale optimization algorithm (CRLWOA-DE) is proposed to compensate the end-effector’s pose errors, which could improve the speed and accuracy of fracture reduction, respectively. Results: The iterative accuracy of CRLWOA-DE is improved by 50.74%, and the optimization speed is improved by 22.62% compared with the whale optimization algorithm (WOA). Meanwhile, compared with particle swarm optimization (PSO) and ant colony optimization (ACO), CRLWOA-DE is proved to be more accurate. Furthermore, SimMechanics in the software of MATLAB was used to reconstruct the fracture reduction robot, and it was verified that the actual motion trajectory of the CRLWOA-DE optimized kinematic stage showed a significant reduction in error in both the x-axis and z-axis directions compared to the desired motion trajectory. Conclusions: This study revealed that the error compensation in FRR reset process had been realized, and the CRLWOA-DE method could be used for reducing the pose error of the fracture reduction robot, which has some significance for the bone fracture and deformity correction.

Published in Micromachines

ISSN: 2072-666X (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Technology: Mechanical engineering and machinery
Website: https://www.mdpi.com/journal/micromachines

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