Localization in GPS-denied environments is critical for autonomous systems, and traditional methods like SLAM have limitations in generalizability across diverse environments. Magnetic-based navigation (MagNav) offers a robust solution by leveraging the ubiquity and unique anomalies of external magnetic fields. This paper proposes a real-time uncertainty-aware motion planning algorithm for MagNav, using onboard magnetometers and information-driven methodologies to adjust trajectories based on real-time localization confidence. This approach balances the trade-off between finding the shortest or most energy-efficient routes and reducing localization uncertainty, enhancing navigational accuracy and reliability. The novel algorithm integrates an uncertainty-driven framework with magnetic-based localization, creating a real-time adaptive system capable of minimizing localization errors in complex environments. Extensive simulations and real-world experiments validate the method, demonstrating significant reductions in localization uncertainty and the feasibility of real-time implementation. The paper also details the mathematical modeling of uncertainty, the algorithmic foundation of the planning approach, and the practical implications of using magnetic fields for localization. Future work includes incorporating a global path planner to address the local nature of the current guidance law, further enhancing the method's suitability for long-duration operations.

翻译：在GPS拒止环境中的定位对于自主系统至关重要，而SLAM等传统方法在不同环境间的泛化能力存在局限。基于磁场的导航（MagNav）通过利用外部磁场的普遍性和独特异常，提供了一种鲁棒的解决方案。本文提出了一种用于MagNav的实时不确定性感知运动规划算法，利用机载磁力计和信息驱动方法，根据实时定位置信度调整轨迹。该方法在寻找最短或最节能路径与降低定位不确定性之间取得平衡，从而提高了导航精度和可靠性。该新颖算法将不确定性驱动框架与基于磁场的定位相结合，创建了一个能够最小化复杂环境中定位误差的实时自适应系统。大量的仿真和真实世界实验验证了该方法，证明了其能显著降低定位不确定性，并具备实时实施的可行性。本文还详细阐述了不确定性的数学模型、规划方法的算法基础，以及利用磁场进行定位的实际意义。未来的工作包括引入全局路径规划器以解决当前制导律的局部性问题，从而进一步提升该方法对长时间运行任务的适用性。