AUV navigation through turbulent ocean environments supported by onboard H-ADCP

Frequently, autonomous underwater vehicles (AUVs) must operate in turbulent ocean environments with complex spatio-temporal variability. Energetic flows, instabilities and currents can strongly perturb safety conditions and development of AUVs operations. A priori knowledge of this ocean variability would allow to adequately plan AUV missions, minimizing the possible negative effects of the environment on its operation. Unfortunately, present oceanographic technology is only capable to forecast large and slow components of the ocean variability, being fast and small scales of variability still unpredictable. This partial knowledge about future environmental conditions degrades AUV robustness, reducing its autonomy and safety. In this article, we propose a class of real-time algorithm to support AUVs navigation in strong turbulent ocean environments characterized by unpredictable small scale variability. The algorithm infers the two-dimensional structure of the current field in a limited region in front of the AUV, using the one-dimensional information obtained from an horizontal acoustic doppler current profiler (H-ADCP). Then, a planner locally optimizes the performance of the AUV in the inferred field. In this work reducing travelling time was of interest. The proposed procedure has been tested on simulated turbulent environments. Results indicate that, although sub-optimals, solutions provided by the algorithm required significantly less travelling time than straight line paths. An example where the proposed methodology increases AUV safety and autonomy is also provided