A Parallel Hypothesis Method of Autonomous Underwater Vehicle Navigation

Cara LaPointe, Ph.D., 2009
Dana Yoerger, Advisor

This thesis presents a parallel hypothesis method for autonomous underwater vehicle navigation that expands the operating envelope of existing long baseline acoustic navigation systems by incorporating information that is not normally used. The proof of concept was done using real-world data obtained by the Autonomous Benthic Explorer (ABE) and Sentry vehicles during operations on the Juan de Fuca Ridge. This algorithm uses a nested architecture that breaks the navigation solution down into basic building blocks for different types of available external information. A proof of concept was conducted using acoustic time-of-flight measurements in a hypothesis generation process and a priori low-resolution bathymetric data in a grid arbitration process.

The major contributions of this research include in-situ identification of acoustic multipath time-of-flight measurements, the multiscale utilization of a priori low-resolution bathymetric data in a high-resolution navigation algorithm, and the design of a navigation algorithm with a flexible architecture. This flexible architecture allows the incorporation of multimodal beliefs without requiring a complex mechanism for real-time hypothesis generation and culling, and it allows the real-time incorporation of multiple types of external information as they become available in situ into the overall navigation solution.