Adaptive coverage sampling of thermocline with an autonomous underwater vehicle

The thermocline, which is common in the ocean, has an important influence on marine fisheries, underwater communication, and submarine activities. This study proposes a method for detecting and tracking a thermocline using an autonomous underwater vehicle (AUV), which follows theSense→Plan→Actcontrol methodology. The adaptive control of the vehicle ensures that the AUV can cover the target thermocline as it evolves over time and space. Additionally, this method achieves coverage observation of a highly dynamic water column containing multiple thermoclines through the online evaluation of the vertical thermocline distribution. Computer simulations are conducted to illustrate the advantages of this algorithm. In the field tests, an AUV is deployed in the South China Sea under the guidance of the satellite remote sensing data. And the algorithm is run in real time to track the thermoclines with different structures in various environments. At the same time, a series of ship-based conductivity, temperature, and depth (CTD) stations are built along the predetermined trajectory of the AUV as the cross-validation of this method. The simulation and field test results demonstrate that the method is effective, practical, and can better perceive variations of a thermocline.