Pose Estimation of Magnetically Driven Helical Robots With Eye-in-Hand Magnetic Sensing

IF 4.6 2区计算机科学 Q2 ROBOTICS

IEEE Robotics and Automation Letters Pub Date : 2025-03-18 DOI:10.1109/LRA.2025.3553048

Yong Zeng;Guangyu Chen;Haoxiang Lian;Kun Bai

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Abstract

This letter presents a magnetic-based pose sensing method for magnetically driven helical robots. Unlike conventional methods that directly compute pose from magnetic field measurements, the proposed approach decouples magnetic field components caused by the helical robot's pose from the rotating magnetic field by deriving the analytic relationship between the spatial characteristics of the rotating magnetic field and the rotating permanent magnet (PM). A magnetic field model for a dual-rotating PM system is established under quasi-static driving conditions, enabling real-time pose estimation by taking account into the effects of the driving PM. To address workspace and signal quality limitations, a mobile sensor array in eye-in-hand configuration is presented, achieving follow-up measurements with improved signal-to-noise ratio and high precision. The proposed method has been validated experimentally on a magnetically driving platform and the results demonstrate that this method enables large-range tracking with limited number of sensors and provides a robust solution for continuous real-time pose sensing for in magnetically driven helical robots.

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基于眼手磁传感的磁驱动螺旋机器人位姿估计

本文提出了一种磁驱动螺旋机器人的基于磁的姿态传感方法。与传统方法直接从磁场测量中计算位姿不同，该方法通过推导旋转磁场的空间特征与旋转永磁（PM）之间的解析关系，将螺旋机器人位姿引起的磁场分量与旋转磁场解耦。建立了准静态驱动条件下双旋转永磁系统的磁场模型，实现了考虑驱动永磁影响的实时位姿估计。为了解决工作空间和信号质量的限制，提出了一种手眼配置的移动传感器阵列，以提高信噪比和高精度实现后续测量。该方法在一个磁驱动平台上进行了实验验证，结果表明，该方法可以在有限传感器数量下实现大范围跟踪，为磁驱动螺旋机器人的连续实时位姿传感提供了鲁棒解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Robotics and Automation Letters Computer Science-Computer Science Applications

CiteScore

9.60

自引率

15.40%

发文量

1428

期刊介绍： The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.