Long-distance Geomagnetic Navigation in GNSS-denied Environments with Deep Reinforcement Learning

TL;DR

The designed mechanism trains an agent to learn and gain the magnetoreception capacity for geomagnetic navigation, rather than using any pre-stored map or extensive and expensive searching approaches, and outperforms existing metaheuristic and bionic navigation methods in long-distance missions under diverse navigation conditions.

Abstract

Geomagnetic navigation has drawn increasing attention with its capacity in navigating through complex environments and its independence from external navigation services like global navigation satellite systems (GNSS). Existing studies on geomagnetic navigation, i.e., matching navigation and bionic navigation, rely on pre-stored map or extensive searches, leading to limited applicability or reduced navigation efficiency in unexplored areas. To address the issues with geomagnetic navigation in areas where GNSS is unavailable, this paper develops a deep reinforcement learning (DRL)-based mechanism, especially for long-distance geomagnetic navigation. The designed mechanism trains an agent to learn and gain the magnetoreception capacity for geomagnetic navigation, rather than using any pre-stored map or extensive and expensive searching approaches. Particularly, we integrate the geomagnetic gradient-based parallel approach into geomagnetic navigation. This integration mitigates the over-exploration of the learning agent by adjusting the geomagnetic gradient, such that the obtained gradient is aligned towards the destination. We explore the effectiveness of the proposed approach via detailed numerical simulations, where we implement twin delayed deep deterministic policy gradient (TD3) in realizing the proposed approach. The results demonstrate that our approach outperforms existing metaheuristic and bionic navigation methods in long-distance missions under diverse navigation conditions.

Figures (10)

• Figure 1: The geomagnetic field of the earth is represented in a spherical coordinate system, the axes of the geomagnetic dipole and the earth's rotation form an angle of approximately 11°.
• Figure 2: Interactions between the DRL agent and the environment in geomgnetic navigation.
• Figure 3: The framework of TD3 algorithms for geomagnetic navigation.
• Figure 4: Deduction of the bio-inspired geomagnetic navigation.
• Figure 5: Contour maps demonstrating magnetic deviation ${D}$, magnetic inclination ${I}$, and the horizontal component ${B_H}$ as derived from the IGRF model, within the region spanning from $(10^\circ S,160^\circ E)$ to $(0^\circ N,170^\circ E)$.