Underwater navigation methodology based on intelligent velocity model for standard AUV

Abstract This paper addresses a state-of-the-art navigation system aided by an intelligent velocity model for autonomous underwater vehicle (AUV). It's critical to obtain a precise navigation for the safety and effectiveness of AUV missions. The reliability of AUV navigation-related sensors becomes particularly important, while the accuracy of sensors, especially the Doppler velocity log (DVL), which is easy to be interfered, affects navigation accuracy directly. However, standard AUV does not have redundant velocity sensors. Once DVL is invalid caused by fish movement, sound scattering, or DVL overrange, it will have a catastrophic effect on AUV missions. To improve navigation robustness and avoid the effects of DVL dropouts, an intelligent velocity model is proposed to assist navigation by using optimally pruned extreme learning machine. The performance of model-aided navigation system is evaluated on data from our research platform Sailfish 210 AUV during sea trials on the high seas of yellow sea, which achieve 0.5% navigation accuracy. In addition, the intelligent velocity model without modifying the model parameters is more flexible in model acquisition than the traditional vehicle dynamic model when AUV sensor distribution and AUV mass change. Results show that it is possible to improve accuracy and robustness of navigation as a redundant velocity information for standard AUV.