GNSS拒止环境下基于深度强化学习的远距离地磁导航
Long-distance Geomagnetic Navigation in GNSS-denied Environments with Deep Reinforcement Learning
AI总结：
本文针对GNSS拒止环境下的远距离地磁导航问题，提出一种基于深度强化学习的方法，通过训练智能体获得磁感知能力，并引入地磁梯度并行机制抑制过度探索，在仿真中采用TD3实现，验证了其优于现有元启发式和仿生导航方法的性能。
摘要：
地磁导航因其在复杂环境中的导航能力以及不依赖全球导航卫星系统（GNSS）等外部导航服务而受到越来越多的关注。现有的地磁导航研究，即匹配导航和仿生导航，依赖于预先存储的地图或大规模搜索，导致在未探索区域适用性有限或导航效率降低。为了解决GNSS不可用区域的地磁导航问题，本文开发了一种基于深度强化学习（DRL）的机制，特别是针对远距离地磁导航。所设计的机制训练智能体学习并获得用于地磁导航的磁感知能力，而不是使用任何预先存储的地图或大规模且昂贵的搜索方法。特别地，我们将基于地磁梯度的并行方法集成到地磁导航中。这种集成通过调整地磁梯度来缓解学习智能体的过度探索，使得获得的梯度朝向目的地对齐。我们通过详细的数值仿真探索了所提方法的有效性，在实现所提方法时采用了双延迟深度确定性策略梯度（TD3）。结果表明，在多样化的导航条件下，我们的方法在远距离任务中优于现有的元启发式和仿生导航方法。
英文摘要：
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.
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