This article proposes a holistic localisation framework for underwater robotic swarms to dynamically fuse multiple position estimates of an autonomous underwater vehicle while using fuzzy decision support system. A number of underwater localisation methods have been proposed in the literature for wireless sensor networks. The proposed navigation framework harnesses the established localisation methods in order to provide navigation aids in the absence of acoustic exteroceptive sensors navigation aid (i.e., ultra-short base line) and it can be extended to accommodate newly developed localisation methods by expanding the fuzzy rule base. Simplicity, flexibility, and scalability are the main three advantages that are inherent in the proposed localisation framework when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. A physics-based simulation platform that considers environment’s hydrodynamics, industrial grade inertial measurement unit, and underwater acoustic communications characteristics is implemented in order to validate the proposed localisation framework on a swarm size of 150 autonomous underwater vehicles. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation by 16.53% and 35.17%, respectively, when compared to the Extended Kalman Filter based localisation with round-robin scheduling.

中文翻译：

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水下机器人群的模糊合作定位框架

本文提出了一种用于水下机器人群的整体定位框架，以在使用模糊决策支持系统的同时动态融合自主水下航行器的多个位置估计。在文献中已经提出了许多用于无线传感器网络的水下定位方法。所提出的导航框架利用已建立的定位方法，以便在没有声学外感传感器的情况下提供导航辅助（即超短基线），并且可以通过扩展模糊规则库来扩展以适应新开发的定位方法。与其他传统的和普遍采用的水下本地化方法相比，简单性，灵活性和可伸缩性是所提议的本地化框架固有的三个主要优势，例如扩展卡尔曼滤波器。一个基于物理的仿真平台考虑了环境的流体动力学，工业级惯性测量单元和水下声通信特性，以验证在150个自动水下车辆群上提出的定位框架的有效性。与基于循环调度的基于扩展卡尔曼滤波器的定位相比，基于模糊的定位算法将整个群体平均定位误差和标准差分别提高了16.53％和35.17％。实施水下声通信特性，以在150个自主水下航行器群上验证所提出的定位框架。与基于循环调度的基于扩展卡尔曼滤波器的定位相比，基于模糊的定位算法将整个群体平均定位误差和标准差分别提高了16.53％和35.17％。实施水下声通信特性，以在150个自主水下航行器群上验证所提出的定位框架。与基于循环调度的基于扩展卡尔曼滤波器的定位相比，基于模糊的定位算法将整个群体平均定位误差和标准差分别提高了16.53％和35.17％。