Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles

摘要

To effectively examine ocean processes, sampling campaigns require persistent autonomous underwater vehicles that are able to spend a majority of their deployment time maneuvering and gathering data underwater. Current navigation techniques rely either on high-powered sensors (e.g. Doppler Velocity Loggers) resulting in decreased deployment time, or dead reckoning (compass and IMU) with motion models resulting in poor navigational accuracy due to unbounded sensor drift. Recent work has shown that terrain-based navigation can augment existing navigation methods to bound sensor drift and reduce error in an energy-efficient manner. In this paper, we investigate the augmentation of terrain-based navigation with in situ science data to further increase navigation and localization accuracy. The motivation for this arises from the need for underwater vehicles to navigate within a spatiotemporally dynamic environment and gather data of high scientific value. We investigate a method to create a terrain map with maximum variability across the range of data available. These data combined with local bathymetry create a terrain that enables underwater vehicles to navigate and localize 1) relative to interesting water properties, and 2) globally based on the terrain and traditional methods. We examine a dataset of bathymetry and multiple science parameters gathered at the ocean surface at Big Fisherman's Cove on Santa Catalina Island and present a weighting for each parameter. We present efficient algorithms to obtain a convex combination of science and bathymetry parameters for unique trajectories generation.