موقعیت یابی وسایل نقلیه زیرآبی خودمختار
Abstract
I. Introduction
II. Mathematical Models and Theoretical Basis
III. The Cooperative Positioning Algorithm Based on Factor Graph and Maximum Correntropy
IV. Simulation
V. Real Tests and Results
Authors
Figures
References
Abstract
Cooperative positioning (CP) is considered as a promising positioning method for multiple autonomous underwater vehicles (multi-AUVs), because CP is characterized by low cost and high precision. In this research, a novel autonomous underwater vehicle (AUV) CP algorithm is proposed to enhance the global localization accuracy of the follower AUV. However, in traditional CP algorithm, the positioning error is large under the condition that the outlier data exists in the observation, which happens commonly. So in this research, a novel CP algorithm based on the factor graph and maximum correntropy(FGMC) for AUV is proposed to enhance the global localization accuracy of the AUV. Different from the traditional algorithms, this presented FGMC-based CP algorithm implements mathematically the Bayes filter by converting the global function estimation problem into the local one. And furthermore, the maximum correntropy is used as the cost function in the factor graph to estimation problem, this can reduce the influence of outliers on positioning accuracy. FGMC based cooperative positioning algorithm is established to mathematically implement the Bayes filter by converting the global function estimation problem into local function estimation problem. Furthermore, the maximum correntropy is used as the cost function in the factor graph to estimate the variables. To demonstrate and verify the proposed algorithm, simulation and real tests in different scenarios are performed in this research. Compared with the traditional CP algorithms, the positioning error of the proposed FGMC cooperative positioning algorithm is obviously smaller than that of the other algorithms.
Introduction
The primary issue in ensuring the successful and efficient execution of autonomous underwater vehicles (AUVs) and other marine robots is accurate positioning [1]–[3]. Common methods based on inertial measurement units (IMUs) have irreplaceable merit with respect to independencies, however, the accumulated error prevents high-accuracy localization in large-scale environments. Compared with using advanced IMUs, incorporating external information is a more feasible solution. In most terrestrial environments, the Global Navigation Satellite System (GNSS) or Wi-Fi can be used to locate an autonomous vehicle [4]–[۷]. However, for AUV, GNSS cannot be used due to the strong attenuation of electromagnetic fields under water [8]. In the harsh underwater environment, high-precision navigation has become an urgent and arduous challenge for AUV [9], [10]. Without an external reference, such as GNSS, the vehicle must rely on proprioceptive information obtained through a compass, a Doppler Velocity Logger (DVL) or an Inertial Navigation System (INS) [11]–[13]. The advantage of using compass and DVL for positioning is low cost, and Dead-Reckoning(DR) method is usually used for positioning in this case. But the positioning error based on DR information grows without bound. In the range of a few hundred meters from the sea floor, the positioning error is generally 0.5%− ۲% of the mileage, so the DVL will be locked when working under the sea [14].