Assessment of Maneuvering Influence on the Fine Alignment of Autonomous Underwater Vehicle

Abstract

Autonomous underwater vehicles (AUVs) are specialized robots used to accomplish important field operations such as inspection of oil and gas pipelines, marine wildlife monitoring, imaging of river and sea beds, nondestructive testing of ship hulls, and so on. Before the start of an AUV mission, its navigation system, which is generally comprised of a doppler velocity log (DVL)/pressure sensor (PS)-aided inertial navigation system (INS) needs to be initialized. After a brief coarse stage of initialization, the AUV attitude is generally refined (as well as some inertial measurement unit (IMU)/aiding sensor systematic error parameters are corrected for) in a Kalman filter (KF)-based estimation process known as fine alignment, which is usually performed in open sea conditions. When the latter is conducted before the submerged phase of the AUV, a Global Navigation Satellite System (GNSS) receiver may provide additional aiding information to the refinement process. As the excitation of the degrees of freedom of the AUV is known to interfere with the performance of the KF fine alignment, this study exploits Baram and Kailath's concept of estimability to assess what kind of deliberate AUV maneuver is able to deliver the best estimation results. As the main contribution, we show that among the tested AUV motion profiles, the lawn mower is the maneuver that, except for the IMU/DVL misalignment around the AUV longitudinal axis, decreases the estimation uncertainties of all remaining INS/GNSS/DVL/PS fine alignment states. Results from simulated and experimental tests confirm the adequacy of the outlined verifications.