Various animals exhibit accurate navigation using environment cues. The
Earth's magnetic field has been proved a reliable information source in
long-distance fauna migration. Inspired by animal navigation, this work
proposes a bionic and data-driven approach for long-distance underwater
navigation. The proposed approach uses measured geomagnetic data for the
navigation, and requires no GPS systems or geographical maps. Particularly, we
construct and train a Temporal Attention-based Long Short-Term Memory (TA-LSTM)
network to predict the heading angle during the navigation. To mitigate the
impact of geomagnetic anomalies, we develop the mechanism to detect and
quantify the anomalies based on Maximum Likelihood Estimation. We integrate the
developed mechanism with the TA-LSTM, and calibrate the predicted heading
angles to gain resistance against geomagnetic anomalies. Using the retrieved
data from the WMM model, we conduct numerical simulations with diversified
navigation conditions to test our approach. The simulation results demonstrate
a resilience navigation against geomagnetic anomalies by our approach, along
with precision and stability of the underwater navigation in single and
multiple destination missions.

通过测量地磁数据，该研究构建和训练了一种基于时间注意力的长短期记忆网络，用于长距离水下导航，并通过最大似然估计检测并量化地磁异常，以提高导航的准确性和稳定性。

一种仿生数据驱动的远距离水下导航方法和抗异常技术

A Bionic Data-driven Approach for Long-distance Underwater Navigation  with Anomaly Resistance

Animal navigation research posits that organisms build and maintain internal
spatial representations, or maps, of their environment. We ask if machines --
specifically, artificial intelligence (AI) navigation agents -- also build
implicit (or 'mental') maps. A positive answer to this question would (a)
explain the surprising phenomenon in recent literature of ostensibly map-free
neural-networks achieving strong performance, and (b) strengthen the evidence
of mapping as a fundamental mechanism for navigation by intelligent embodied
agents, whether they be biological or artificial. Unlike animal navigation, we
can judiciously design the agent's perceptual system and control the learning
paradigm to nullify alternative navigation mechanisms. Specifically, we train
'blind' agents -- with sensing limited to only egomotion and no other sensing
of any kind -- to perform PointGoal navigation ('go to $\Delta$ x, $\Delta$ y')
via reinforcement learning. Our agents are composed of navigation-agnostic
components (fully-connected and recurrent neural networks), and our
experimental setup provides no inductive bias towards mapping. Despite these
harsh conditions, we find that blind agents are (1) surprisingly effective
navigators in new environments (~95% success); (2) they utilize memory over
long horizons (remembering ~1,000 steps of past experience in an episode); (3)
this memory enables them to exhibit intelligent behavior (following walls,
detecting collisions, taking shortcuts); (4) there is emergence of maps and
collision detection neurons in the representations of the environment built by
a blind agent as it navigates; and (5) the emergent maps are selective and task
dependent (e.g. the agent 'forgets' exploratory detours). Overall, this paper
presents no new techniques for the AI audience, but a surprising finding, an
insight, and an explanation.

本研究研究了机器（人工智能导航代理）是否也能建立内在空间表征或地图，通过对盲智能体进行训练，发现即使在没有预先设定机制条件的情况下，盲智能体也能通过记忆体和选择性建立地图，并表现出智能行为，这证明了地图是导航的基本机制之一。