mlsanityData to 14 Oct 2026

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

arXiv 2410.15837 ↗First posted 21 Oct 2024cs.RO cs.AIcited by 2

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 53 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

8 of 53 references.

References · 8

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  3. 2020ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objectscited by 244
  4. 2021IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learningcited by 10
  5. 2022Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shapingcited by 10
  6. 2022Neuro-Planner: A 3D Visual Navigation Method for MAV with Depth Camera based on Neuromorphic Reinforcement Learningcited by 7
  7. 2024Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learningcited by 6
  8. 2024A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistancecited by 4

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillationcited by 2
  2. 2025Fine-tuning for Data-enabled Predictive Control of Noisy Systems by Reinforcement Learningcited by 0

Similar, not cited

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  3. 2026Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observations
  4. 2026Path-conditioned Reinforcement Learning-based Local Planning for Long-Range Navigation
  5. 2025Transferable Deep Reinforcement Learning for Cross-Domain Navigation: from Farmland to the Moon
  6. 2025GRL-SNAM: Geometric Reinforcement Learning with Path Differential Hamiltonians for Simultaneous Navigation and Mapping in Unknown Environments
  7. 2024Benchmarking Deep Reinforcement Learning for Navigation in Denied Sensor Environments
  8. 2024Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing