Abstract
AbstractThis 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.
Highlights
Accurate absolute positioning is fundamental to all robots—both to ensure realtime closed-loop control and to provide position measurements co-located with other observations
Those methods are in contrast to strap down dead-reckoning or odometry methods such as those provided by Doppler velocity logs (DVLs) and inertial navigation systems (INSs) which accumulate error over time.[4,5]
In the Extended Kalman Filter (EKF) solution, global positioning system (GPS), Doppler velocity log (DVL), model velocity and range measurements are fused into the EKF's estimates as available
Summary
Accurate absolute positioning is fundamental to all robots—both to ensure realtime closed-loop control and to provide position measurements co-located with other observations. Other works have investigated the use of multiple cooperating vehicles which share varying amounts of information to further constrain their navigation.[20,21,22] A significant challenge in using intervehicle ranges for position estimates is preventing overconfidence in the solution through the sharing of correlated covariance information or double counting.[23] Prior work has largely dealt with this challenge by preserving prior measurement information and extending the state with each new measurement which has limited scalability and suitability for real-time processing Approximations to this approach have been presented which compress prior measurements[24] or which avoid transmitting the correlation terms of the covariance matrix.[25].
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