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Geomagnetic Navigation of Autonomous Underwater Vehicle Based on Multi-objective Evolutionary Algorithm

机译:基于多目标进化算法的自主水下航行器地磁导航

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摘要

This paper presents a multi-objective evolutionary algorithm of bio-inspired geomagnetic navigation for Autonomous Underwater Vehicle (AUV). Inspired by the biological navigation behavior, the solution was proposed without using a priori information, simply by magnetotaxis searching. However, the existence of the geomagnetic anomalies has significant influence on the geomagnetic navigation system, which often disrupts the distribution of the geomagnetic field. An extreme value region may easily appear in abnormal regions, which makes AUV lost in the navigation phase. This paper proposes an improved bio-inspired algorithm with behavior constraints, for sake of making AUV escape from the abnormal region. First, the navigation problem is considered as the optimization problem. Second, the environmental monitoring operator is introduced, to determine whether the algorithm falls into the geomagnetic anomaly region. Then, the behavior constraint operator is employed to get out of the abnormal region. Finally, the termination condition is triggered. Compared to the state-of- the-art, the proposed approach effectively overcomes the disturbance of the geomagnetic abnormal. The simulation result demonstrates the reliability and feasibility of the proposed approach in complex environments.
机译:本文提出了一种用于水下机器人(AUV)的生物启发地磁导航的多目标进化算法。受生物导航行为的启发,该解决方案的提出无需使用先验信息,只需通过磁力趋向搜索即可。然而,地磁异常的存在对地磁导航系统有重大影响,这常常会破坏地磁场的分布。极值区域可能容易出现在异常区域中,这会导致AUV在导航阶段丢失。本文提出了一种改进的具有行为约束的生物启发算法,以使AUV逃离异常区域。首先,将导航问题视为优化问题。其次,引入环境监测算子,以确定算法是否落入地磁异常区域。然后,使用行为约束算子摆脱异常区域。最后,终止条件被触发。与现有技术相比,所提出的方法有效地克服了地磁异常的干扰。仿真结果证明了该方法在复杂环境下的可靠性和可行性。

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