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. Note to Practitioners—This work addresses a key limitation of current AGV magnetic navigation systems: the low information capacity of magnetic markers, which limits their dynamic adaptability in industrial environments. The proposed Magnetic Moment Encoding (MME) system converts traditional passive magnetic markers into active information carriers by utilizing three-dimensional magnetic moment parameters, achieving high-density data transmission through permanent magnets. With magnetic navigation intelligence moving along a fixed route, MME can achieve software defined path planning by reprogramming magnetic moments. In order to accurately identify the magnetic moment information of permanent magnets, we propose Magnetic Moment Decoder (MMD) that can parse the magnetic moment information of permanent magnets in complex background magnetic field environments and restore the encoded meaning of magnetic moments.