用深度强化学习实现无GPS环境下的长距离地磁导航
Long-distance Geomagnetic Navigation in GNSS-denied Environments with Deep Reinforcement Learning
- 用深度强化学习让智能体自主学会地磁感知导航，无需预存地图
- 结合地磁梯度引导，使导航路径更优，长距离任务成功率超传统方法
- 适合无人系统在无GPS区域执行长时间自主导航任务
地磁导航因其能在复杂环境中独立运行且不依赖全球导航卫星系统（GNSS）而受到越来越多关注。现有地磁导航方法如匹配导航和仿生导航依赖预存地图或大量搜索，导致在未知区域适用性差或效率低。为解决无GNSS环境下地磁导航的挑战，本文提出一种基于深度强化学习（DRL）的机制，专用于长距离地磁导航。该机制训练智能体自主学习地磁感知能力，无需预存地图或昂贵的遍历搜索。特别地，将基于地磁梯度的并行策略融入导航过程，通过调整地磁梯度方向使其指向目标，有效减少智能体的过度探索。通过详细数值仿真验证，采用双延迟深度确定性策略梯度（TD3）实现该方法。结果表明，在多种导航条件下，该方法在长距离任务中均优于现有元启发式与仿生导航方法。
原文摘要 · Abstract (English)
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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