Implementation of a Hydrodynamic Model-Based Navigation System for a Low-Cost AUV Fleet

Abstract

This work implements a hydrodynamic model-based localization and navigation system for low-cost autonomous underwater vehicles (AUVs) that are limited to a micro-electro mechanical system (MEMS) inertial measurement unit (IMU). The hydrodynamic model of this work is uniquely developed to directly determine the linear velocities of the vehicle using the measured vehicle angular rates and propeller speed as inputs. The proposed system was tested in the field using a fleet of low-cost Bluefin SandShark AUVs. Implementation of the model-based localization system and fusing of the solution into the vehicle navigation loop was conducted using backseat computers of the AUV fleet that run mission orientated operating suite -interval programming (MOOS-IvP). With the model-based navigation system, the maximum localization error (i.e., in comparison to a long baseline (LBL) based ground-truth position) was limited to 15 m and 30 m for two 650-second and 1070-second long missions. Extrapolation of the position drift shows that the model-based localization system is able to limit the position uncertainty to less than 100 m by the end of hour-long mission; whereas, the drift in the default IMU-based localization solution was over 1 km per hour. This is a considerable improvement by only using a MEMS IMU that generally costs less than $100. Furthermore, this work is a step towards generalizing and automating the process of hydrodynamic modeling, model parameter estimation and data fusion (i.e., fusing the localization solution with those from other available aiding sensors and feeding to the navigation loop) so that a model-based localization system can be implemented in any AUV that has backseat computing capability.

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

“…The vehicle depth response illustrated in Figure 7c shows that the AUV was altering the vehicle depth during these time periods; that is, the vehicle pitch angle was not around zero. A vehicle flight dynamic model is most accurate within the motion range where the model parameters are valid (Randeni et al, 2020, 2018). For example, if the model parameters were estimated for a surge speed range of 1–2 ms −1 , the motion response predictions at 2.5 ms −1 may not be as accurate as those at 1.6 ms −1 .…”

“…The velocity and position estimated by the flight dynamic model are relative to the water column (i.e., $${\nu }_{(\text{auv}| \text{water})}^{model}$$ and $${x}_{(\text{auv}| \text{water})}^{model}$$) since the model excludes water currents. Therefore, the error sources of the model‐based velocity and position estimates include the drift due to water currents and the uncertainty of the model (Randeni et al, 2020, 2018), which are counteracted by the self‐adaptation of the flight dynamic model…”

Underwater and out of sight: Towards ubiquity in underwater robotics

“…In general, this function is nonlinear and can be made as complex as desired to capture the evolution of vehicle state over time -it can incorporate factors such as the physical properties of the vehicle (size, shape, mass, inertia), its interaction with the environment (lift, drag, thrust), as well as environmental forces acting upon it (currents, buoyancy). As expected, deriving a complex equation of motion that accounts for a large number of parameters is exceedingly difficult, but can provide better predictive power [176], [177], [178], [74] [179] [180]. Regardless of its complexity, no model can perfectly capture the dynamics of the vehicle operating in a real-world environment, and so an additional noise term is added to account for these uncertainties:…”

“…• Improved dead-reckoning via hydrodynamic modeling: The current piUSBL system makes use of a very simple constant velocity model to perform the filter prediction step using vehicle attitude and speed from propeller RPM. Randeni [180] recently developed a hydrodynamic model of the SandShark vehicle using LBL position data of the AUV as well as IMU data, which was demonstrated to significantly outperform the simple constant velocity model in dead-reckoning. Integrating this improved hydrodynamic model within the prediction step of the filter is the subject of ongoing work, and could potentially improve the navigational ability of the SandShark fleet.…”

Advances in passive acoustic detection, localization, and tracking applied to unmanned underwater vehicles

“…Part of our state estimation implementation is transforming RPM measurements to the underway speed of the vehicle. We incorporate the vehicle dynamic model estimation technique, developed in Randeni et al [144], which used system identification to obtain a dynamic model of the Sandshark UUV for the purpose of developing a hydrodynamic modelaided navigation system. In order to implement this technique for the tracking application, the dynamic model estimator uses the on-board UUV navigation data from a previous experiment as inputs.…”