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骨折复位机器人位姿误差分析的建模与补偿方法研究

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

作者信息

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

机构信息

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.

School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China.

出版信息

Micromachines (Basel). 2022 Jul 27;13(8):1186. doi: 10.3390/mi13081186.

DOI:10.3390/mi13081186
PMID:36014108
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9413538/
Abstract

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.

摘要

背景

在骨折复位过程中，由于机构误差，实际轨迹与理想轨迹之间存在一些偏差，这会影响骨折复位的顺利进行。为此，基于自主研发的并联机构骨折复位机器人（FRR），提出了一种减少FRR位姿误差的新方法。

方法

首先，分析了位姿误差，建立了机器人位姿误差模型。其次，将FRR的机构误差转换为驱动杆参数误差，并分析了各驱动杆参数对机器人位姿误差的影响。第三，结合基于柯西反对学习和差分进化算法（DE），提出了一种改进的鲸鱼优化算法（CRLWOA-DE）来补偿末端执行器的位姿误差，分别提高骨折复位的速度和精度。

结果

与鲸鱼优化算法（WOA）相比，CRLWOA-DE的迭代精度提高了50.74%，优化速度提高了22.62%。同时，与粒子群优化（PSO）和蚁群优化（ACO）相比，CRLWOA-DE被证明更准确。此外，利用MATLAB软件中的SimMechanics对骨折复位机器人进行了重构，验证了CRLWOA-DE优化后的运动学阶段实际运动轨迹在x轴和z轴方向上与期望运动轨迹相比误差显著减小。

结论

本研究表明，在FRR复位过程中实现了误差补偿，CRLWOA-DE方法可用于降低骨折复位机器人的位姿误差，对骨折和畸形矫正具有一定意义。

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