Magnetic Moment Encoding (MME): An Information-Augmented Magnetic Navigation Method

In automated guided vehicle (AGV) magnetic navigation systems, magnetic markers typically serve solely as spatial references, offering limited informational content. This paper presents a novel Magnetic Moment Encoding (MME) system, which enables magnetic markers to simultaneously function as both physical landmarks and information carriers. The system leverages three-dimensional magnetic moment parameters of permanent magnets to achieve high-capacity information encoding. Compared to conventional binary magnetic pole encoding methods, MME increases information density by approximately threefold. To address the challenge of decoding MME signals, we propose a neural network-based Magnetic Moment Decoder (MMD), which enables robust decoding of encoded information even in complex magnetic field environments. Integrated into AGV navigation, the MME system achieves a positioning accuracy of 11.88±9.12 mm and a path yaw error of 4.21 ± 2.23°, while also supporting real-time adaptive path planning in dynamic environments. This study introduces a new paradigm for high-density, low-power magnetic information interaction, with broad application potential in industrial automation and the Internet of Things.