Mode-Aware Adaptive Navigation for Autonomous Underwater Vehicles Using Multimodal Sensing and Optical Feedback in Simulation Environment

Autonomous underwater vehicles require the integration of inertial, velocity, optical, and acoustic measurements for reliable underwater navigation where GPS signals are unavailable or intermittent. During a mission, the utility of these modalities may vary due to changes in environmental visibility, Doppler velocity log tracking, acoustic geometries, or available infrastructure. While conventional approaches account for these differences with fixed measurement-level adaptations, they are insufficient to address the need to adjust the navigation modality used. We introduce a mode-aware multimodal navigation system that determines its current capabilities based on available sensor and estimator evidence and chooses from several distinct optical, DVL/inertial, LBL, USBL, relative dead-reckoning, and terminal navigation modes. We evaluated our proposed architecture via closed-loop simulation against a well-performing fixed configuration using both pre-determined testing scenarios and novel dynamically-changing test scenarios. In 400 tests involving dynamic scenarios, our approach reduced the horizontal root-mean-square error by 0.4262 m (−0.6052 m to −0.2608 m at 95% confidence interval) over the pre-selected configuration while selecting an appropriate viable mode 70.75% of the time during instances of sustained adaptation. Our system also demonstrated better transitions, which supports continuity and safety, but did not exhibit any measurable advantage in localization performance if the preselected fixed configuration was already suitable. Simulation results indicate that capability-aware navigation can enhance autonomous underwater vehicle performance when the best sensing configuration becomes unavailable after deployment. We also highlight the importance of switching selectivity as an engineering design consideration.