Closed‐loop one‐way‐travel‐time navigation using low‐grade odometry for autonomous underwater vehicles. Issue 4 (7th September 2017)

• Record Type: Journal Article
• Authors: Claus, Brian; Kepper, James H.; Suman, Stefano; Kinsey, James C.
• Is Part Of: Journal of field robotics. Volume 35:Issue 4(2018)
• Page Start: 421
• Page End: 434
• Publication Date: 2017-09-07
• Subjects: acoustic -- autonomous underwater vehicles -- low grade odometry -- navigation -- one way travel time; Robots, Industrial -- Periodicals; Automatic control -- Periodicals; 629.892
• DOI: 10.1002/rob.21746
• Languages: English
• ISSNs: 1556-4959

Abstract

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.