Autonomous Underwater Vehicle Motion Response: A Nonacoustic Tool for Blue Water Navigation

Abstract

A B S T R A C TAutonomous underwater vehicles (AUVs) use secondary velocity over ground measurements to aid the Inertial Navigation System (INS) to avoid unbounded drift in the point-to-point navigation solution. When operating in deep open ocean (i.e., in blue water-beyond the frequency-specific instrument range), the velocity measurements are either based on water column velocities or completely unavailable. In such scenarios, the velocity-relative-to-water measurements from an acoustic Doppler current profiler (ADCP) are often used for INS aiding. ADCPs have a blanking distance (typically ranging between 0.5 and 5 m) in proximity to the device in which the flow velocity data are undetectable. Hence, water velocities used to aid the INS solution can be significantly different from that near the vehicle and are subjected to significant noise. Previously, the authors introduced a nonacoustic method to calculate the water velocity components of a turbulent water column within the ADCP dead zone using the AUV motion response (referred to as the WVAM method). The current study analyzes the feasibility of incorporating the WVAM method within the INS by investigating the accuracy of it at different turbulence levels of the water column. Findings of this work demonstrate that the threshold limits of the method can be improved in the nonlinear ranges (i.e., at low and high levels of energy); however, by estimating a more accurate representation of vehicle hydrodynamic coefficients, this method has proven robust in a range of tidally induced flow conditions. The WVAM method, in its current state, offers significant potential to make a key contribution to blue water navigation when integrated within the vehicle's INS.

Determining the Horizontal and Vertical Water Velocity Components of a Turbulent Water Column Using the Motion Response of an Autonomous Underwater Vehicle

“…The estimated hydrodynamic coefficients of the simulation model using the system identification method could be less accurate for large angles of incidence of the vehicle, where the hydrodynamic forces and moments are in their non-linear ranges. Therefore, as the yaw and pitch angle fluctuations become larger, the accuracy of the simulation model decreases; adversely affecting the WVAM velocity prediction [23]. The disparity at peaks of the velocity components is due to the hydrodynamic coefficients exceeding their linear ranges causing a reduction in the accuracy of the simulation model.…”

“…Also, the WVAM method assumes that the velocity components along the y and z axes are acting uniformly along the length of the AUV; i.e., the WVAM method estimates the mean velocity variation along the length of Both WVAM and vehicle on-board ADCP measurements use the same Earth-referenced AUV velocities. Therefore, in order to further validate the WVAM method Randeni, Forrest, Cossu, Leong, King and Ranmuthugala [23] compared the WVAM estimations with those obtained from a stationary ADCP moored to the seabed. When the AUV was flying over the location of the stationary ADCP, it maintained an altitude of 11 m; hence, the ADCP water column velocities recorded from the bins at 11 m altitude were used for the comparison.…”

A high‐resolution AUV navigation framework with integrated communication and tracking for under‐ice deployments

“…Wind and currents can cause the surface ice to translate and rotate, making the upward‐looking DVL method susceptible to larger drifts (McPhail et al, 2009). Both the bottom‐track and the ice‐track methods are restricted by the maximum range of the sensor, roughly 30 and 200 m for 1200 and 300 kHz, respectively (Hegrenaes & Hallingstad, 2011; Randeni et al, 2016). Correlation velocity logs (CVL) can provide a higher range with a slight compromise in navigation accuracy, but are limited to around 300 m in range (Blanford et al, 2018; Dillon, 2015).…”