Global Uncertainty-Aware Planning for Magnetic Anomaly-Based Navigation

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.

Real-time Uncertainty-Aware Motion Planning for Magnetic-based Navigation

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.