UW Applied Physics Laboratory (APL)

Autonomous Algorithm Development for Exploring Deep-Sea Hydrothermal Plumes

The Autonomous Vent Finder project utilizes Gaussian regression, machine learning, and computer vision to locate deep-sea hydrothermal vents using an autonomous underwater vehicle. These vents discharge plumes that spread thousands of kilometers. They sustain distinctive deep-sea ecosystems and have large impacts on global ocean biogeochemistry. As the AUV traverses the plume, it collects plume-related data using onboard sensors and estimates the vent location. By training the algorithm to adapt to the changing conditions of the plume, the AUV can re-align itself to locate the vent more efficiently than traditional methods, which rely on pre-programmed paths and post-dive data analysis.