Dung Beetle Optimizer-based High-precision Localization for Magnetic-Controlled Capsule Robot

As a medical microrobot, magnetic-controlled capsule robots (MCRs) are pivotal in internal diagnostics and therapeutic interventions. Achieving high-precision localization of MCRs is essential for the successful execution of medical procedures. This paper introduces a novel Dung Beetle Optimizer (DBO)-based localization method for MCR, demonstrating high localization accuracy and flexibility in static magnetic field environments and under the control of existing magnetic control systems. With the aid of an FPGA-based parallel measurement system, it can effectively eliminate measurement distortion. The average position and orientation errors could achieve 0.53 mm and 0.60° when performing 600 iterations per computation, and further increasing the number of iterations reduces the errors, which is superior to existing methods. Experimental validations underscore the method’s robust performance and compatibility with existing magnetic control systems.

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Publication Details

Published
2024-10-14
DOI
https://doi.org/10.1109/iros58592.2024.10802352
Citations
3
Primary Topic
Soft Robotics and Applications
Type
article
Field-Weighted Citation Impact
0.55
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article

Dung Beetle Optimizer-based High-precision Localization for Magnetic-Controlled Capsule Robot

Zijin Zeng, Menglu Tan, Shengyuan Wang, Lin Feng et al.
3 citations
Soft Robotics and Applications
0.55
article

Dung Beetle Optimizer-based High-precision Localization for Magnetic-Controlled Capsule Robot

Zijin Zeng, Menglu Tan, Shengyuan Wang, Lin Feng, Chan Li, Fengwu Wang
article en
3 citations

Abstract

As a medical microrobot, magnetic-controlled capsule robots (MCRs) are pivotal in internal diagnostics and therapeutic interventions. Achieving high-precision localization of MCRs is essential for the successful execution of medical procedures. This paper introduces a novel Dung Beetle Optimizer (DBO)-based localization method for MCR, demonstrating high localization accuracy and flexibility in static magnetic field environments and under the control of existing magnetic control systems. With the aid of an FPGA-based parallel measurement system, it can effectively eliminate measurement distortion. The average position and orientation errors could achieve 0.53 mm and 0.60° when performing 600 iterations per computation, and further increasing the number of iterations reduces the errors, which is superior to existing methods. Experimental validations underscore the method’s robust performance and compatibility with existing magnetic control systems.

Beihang University (CN)
Openalex Percentile: Top 35%
Soft Robotics and Applications
0.55
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