AUV Navigation Research Abstract
This paper extends the progress of single beacon one-way-travel-time (OWTT) range measurements for constraining XY position for autonomous underwater vehicles (AUV).
Traditional navigation algorithms have used OWTT measurements to constrain an inertial navigation system aided by a Doppler Velocity Log (DVL). These methodologies limit AUV applications to where DVL bottom-lock is available as well as the necessity for expensive strap-down sensors, such as the DVL. Thus, deep water, mid-water column research has mostly been left untouched, and vehicles that need expensive strap-down sensors restrict the possibility of using multiple AUVs to explore a certain area.
This work presents a solution for accurate navigation and localization using a vehicle’s odometry determined by its dynamic model velocity and constrained by OWTT range measurements from a topside source beacon as well as other AUVs operating in proximity.
We present a comparison of two navigation algorithms: an Extended Kalman Filter (EKF) and a Particle Filter(PF). Both of these algorithms also incorporate a water velocity bias estimator that further enhances the navigation accuracy and localization. Closed-loop online field results on local waters as well as a real-time implementation of two days field trials operating in Monterey Bay, California during the Keck Institute for Space Studies oceanographic research project prove the accuracy of this methodology with a root mean square error on the order of tens of meters compared to GPS position over a distance traveled of multiple kilometers.