用磁场异常图指导路径规划，提升导航稳定性与精度
Global Uncertainty-Aware Planning for Magnetic Anomaly-Based Navigation
- 通过熵图识别高信息量磁场区域，引导智能体主动探索
- 实测显示定位误差显著降低，稳定性优于现有方法
- 适用于地形、水下等多种梯度导航场景，适应性强
在部分可观测、随机性环境中的磁异常导航面临状态估计精度与定位稳定性难以兼顾的挑战，传统方法因定位更新受限和动态条件而性能下降。本文提出一种面向磁异常导航（MagNav）的多目标全局路径规划方法，利用熵图评估磁场的空间频率变化，识别高信息区域，并通过势场规划器生成前往这些区域的路径，增强主动定位能力。硬件实验表明，相比现有主动定位技术，该方法显著提升了定位稳定性和准确性。结果证明该方法能有效降低定位不确定性，并可适配多种基于梯度的导航地图，包括地形和水下深度地图。
原文摘要 · Abstract (English)
Navigating and localizing in partially observable, stochastic environments with magnetic anomalies presents significant challenges, especially when balancing the accuracy of state estimation and the stability of localization. Traditional approaches often struggle to maintain performance due to limited localization updates and dynamic conditions. This paper introduces a multi-objective global path planner for magnetic anomaly navigation (MagNav), which leverages entropy maps to assess spatial frequency variations in magnetic fields and identify high-information areas. The system generates paths toward these regions by employing a potential field planner, enhancing active localization. Hardware experiments demonstrate that the proposed method significantly improves localization stability and accuracy compared to existing active localization techniques. The results underscore the effectiveness of this method in reducing localization uncertainty and highlight its adaptability to various gradient-based navigation maps, including topographical and underwater depth-based environments.
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