A Quadrotor Aerial Docking System Utilizing Both Vision and Magnetic Field

This paper presents a complete quadrotor aerial docking system that utilizes both vision and magnetic field guidance to achieve high-precision docking. Visual guidance is implemented using a combination of a forward-facing camera and an upward-facing camera, which provide feedback on the local and relative positions, respectively. Magnetic guidance is realized through electromagnets on the quadrotor and permanent magnets at the docking site. The visual system provides guidance for the quadrotor to approach the target point, while the opposing magnetic poles attract and align with each other, cooperating with the quadrotor's four rotors to ensure accurate docking. In the visual guidance system, an intermediate coordinate frame is established to ensure consistent position feedback from the two sets of cameras. Meanwhile, the use of magnetic guidance enables a rapid and low-cost docking solution with greater scalability compared to mechanical docking devices. To coordinate the quadrotor's actuators with the magnetic force from the docking device, a magnetic field model is integrated into the flight controller. The model's parameters are identified offline using a simple, self-made measurement device, which collects data to train a precise model. This model is then used as a virtual sensor to provide real-time feedback. A quadratic programming (QP) problem is then formulated in the control allocation module, allowing the quadrotor's actuators to effectively coordinate with the magnetic field and guide the quadrotor to the docking point. Both simulations and experimental results validate the feasibility of the proposed system.

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Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2025-04-03
DOI
https://doi.org/10.1109/lra.2025.3557300
Citations
1
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
1.28
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article

A Quadrotor Aerial Docking System Utilizing Both Vision and Magnetic Field

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article

A Quadrotor Aerial Docking System Utilizing Both Vision and Magnetic Field

Xiaobin Yu, Zijie Qin, Mei Liu, Chunqiang Wang, Jingbo Wei, Jiawei Chen, Xinliang Li, Kaixin Li, Kun Liu
article en
1 citations

Abstract

This paper presents a complete quadrotor aerial docking system that utilizes both vision and magnetic field guidance to achieve high-precision docking. Visual guidance is implemented using a combination of a forward-facing camera and an upward-facing camera, which provide feedback on the local and relative positions, respectively. Magnetic guidance is realized through electromagnets on the quadrotor and permanent magnets at the docking site. The visual system provides guidance for the quadrotor to approach the target point, while the opposing magnetic poles attract and align with each other, cooperating with the quadrotor's four rotors to ensure accurate docking. In the visual guidance system, an intermediate coordinate frame is established to ensure consistent position feedback from the two sets of cameras. Meanwhile, the use of magnetic guidance enables a rapid and low-cost docking solution with greater scalability compared to mechanical docking devices. To coordinate the quadrotor's actuators with the magnetic force from the docking device, a magnetic field model is integrated into the flight controller. The model's parameters are identified offline using a simple, self-made measurement device, which collects data to train a precise model. This model is then used as a virtual sensor to provide real-time feedback. A quadratic programming (QP) problem is then formulated in the control allocation module, allowing the quadrotor's actuators to effectively coordinate with the magnetic field and guide the quadrotor to the docking point. Both simulations and experimental results validate the feasibility of the proposed system.

IEEE Robotics and Automation LettersVol. 10(6)
Sun Yat-sen University (CN), Nanjing University of Aeronautics and Astronautics (CN)
Affordable and clean energy
Openalex Percentile: Top 22%
Robotic Path Planning Algorithms
1.28
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