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

Minghe Liu¹, Jian Li², Hao Sun¹

¹School 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.

|August 26, 2022

Summary

This study introduces a novel method using an improved whale optimization algorithm (CRLWOA-DE) to reduce pose errors in fracture reduction robots (FRR). The new approach significantly enhances accuracy and speed for bone fracture and deformity correction.

Keywords:

differential evolution algorithm error compensation error model fracture reduction robot opposition-based learning whale optimization algorithm

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Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

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Published on: April 11, 2018

Area of Science:

Robotics
Biomedical Engineering
Optimization Algorithms

Background:

Mechanism errors in fracture reduction robots (FRR) lead to trajectory inaccuracies, impacting surgical precision.
Existing methods struggle to fully address the pose errors inherent in FRR systems.
Developing precise error compensation is crucial for effective fracture reduction.

Purpose of the Study:

To propose a novel method for reducing pose errors in a self-developed parallel mechanism fracture reduction robot (FRR).
To enhance the speed and accuracy of fracture reduction procedures.
To validate the effectiveness of the proposed error compensation method.

Main Methods:

Analysis of FRR pose errors and development of a corresponding mathematical model.
Conversion of mechanism errors into drive bar parameter errors to assess their influence.
Implementation of an improved whale optimization algorithm with Cauchy opposition-based learning and differential evolution (CRLWOA-DE) for pose error compensation.

Main Results:

The CRLWOA-DE algorithm demonstrated a 50.74% improvement in iterative accuracy and a 22.62% increase in optimization speed compared to the standard whale optimization algorithm (WOA).
CRLWOA-DE outperformed particle swarm optimization (PSO) and ant colony optimization (ACO) in terms of accuracy.
Simulations using MATLAB's SimMechanics confirmed significant error reduction in the robot's trajectory along the x-axis and z-axis.

Conclusions:

The developed CRLWOA-DE method effectively compensates for pose errors in fracture reduction robots.
This approach holds significant potential for improving outcomes in bone fracture and deformity correction surgeries.
The study validates the successful realization of error compensation in the FRR reset process.