IEEE Trans Cybern. 2021 Aug;51(8):4286-4297. doi: 10.1109/TCYB.2019.2933397. Epub 2021 Aug 4.
Lots of evidence has indicated that many kinds of animals can achieve goal-oriented navigation by spatial cognition and dead reckoning. The geomagnetic field (GF) is a ubiquitous cue for navigation by these animals. Inspired by the goal-oriented navigation of animals, a novel long-distance underwater geomagnetic navigation (LDUGN) method is presented in this article, which only utilizes the declination component ( D ) and inclination component ( I ) of GF for underwater navigation without any prior knowledge of the geographical location or geomagnetic map. The D and I measured by high-precision geomagnetic sensors are compared periodically with that of the destination to determine the velocity and direction in the next step. A model predictive control (MPC) algorithm with control and state constraints is proposed to achieve the control and optimization of navigation trajectory. Because the optimal control is recalculated at each sampling instant, the MPC algorithm can overcome interferences of geomagnetic daily fluctuation, geomagnetic storms, ocean current, and geomagnetic local anomaly. The simulation results validate the feasibility and accuracy of the proposed algorithm.
大量证据表明，许多动物可以通过空间认知和推测来实现目标导向的导航。地磁场（GF）是这些动物导航的普遍线索。受动物目标导向导航的启发，本文提出了一种新的长距离水下地磁导航（LDUGN）方法，该方法仅利用地磁场的偏角分量（D）和倾角分量（I）进行水下导航，无需任何地理位置或地磁图的先验知识。通过高精度地磁传感器测量的 D 和 I 与目的地的 D 和 I 进行周期性比较，以确定下一步的速度和方向。提出了一种具有控制和状态约束的模型预测控制（MPC）算法，以实现导航轨迹的控制和优化。由于在每个采样瞬间重新计算最优控制，因此 MPC 算法可以克服地磁场日变化、地磁暴、海流和地磁局部异常的干扰。仿真结果验证了所提出算法的可行性和准确性。
