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Development and Online Validation of an UKF-based Navigation Algorithm for AUVs

机译：基于UKF的AUV导航算法的开发和在线验证

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The development of precise and robust navigation strategies for Autonomous Underwater Vehicles (AUVs) is fundamental to reach the high level of performance required by complex underwater tasks, often including more than one AUV. One of the main factors affecting the accuracy of AUVs navigation systems is the algorithm used to estimate the vehicle motion, usually based on kinematic vehicle models and linear estimators. In this paper, the authors present a navigation strategy specifically designed for AUVs, based on the Unscented Kalman Filter (UKF). The algorithm proves to be effective if applied to this class of vehicles and allows to achieve a satisfying accuracy improvement compared to standard navigation algorithms. The proposed strategy has been experimentally validated in suitable sea tests performed near the Cala Minnola wreck (Levanzo, Aegadian Islands, Sicily, Italy). The vehicles involved are the Typhoon AUVs, developed and built by the Department of Industrial Engineering of the University of Florence during the THESAURUS Tuscany Region project and the European ARROWS project for exploration and surveillance of underwater archaeological sites. The proposed algorithm has been implemented online on the AUVs and tested. The validation of the proposed strategy provided accurate results in estimating the vehicle dynamic behavior, better than those obtained through standard navigation algorithms.
机译：对于自动水下航行器（AUV）而言，精确而鲁棒的导航策略的发展对于达到复杂的水下任务（通常包括多个AUV）所要求的高水平性能至关重要。影响AUV导航系统准确性的主要因素之一是用于估计车辆运动的算法，通常基于运动学车辆模型和线性估计器。在本文中，作者提出了一种基于无味卡尔曼滤波器（UKF）专门为AUV设计的导航策略。该算法被证明适用于此类车辆，并且与标准导航算法相比，可以实现令人满意的精度改进。拟议的策略已在Cala Minnola残骸（意大利西西里岛的阿加迪亚群岛的莱万佐）附近进行的适当海试中得到了实验验证。涉及的车辆是台风AUV，它是由佛罗伦萨大学工业工程系在THESAURUS托斯卡纳大区项目和欧洲ARROWS项目期间开发和制造的，用于勘探和监视水下考古遗址。所提出的算法已在AUV上在线实现并经过测试。与通过标准导航算法获得的结果相比，所提出策略的验证在估计车辆动态行为方面提供了准确的结果。

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