A parallel hypothesis method of autonomous underwater vehicle navigation

Abstract

This research presents a parallel hypothesis method for autonomous underwater vehicle navigation that enables a vehicle to expand the operating envelope of existing long baseline acoustic navigation systems by incorporating information that is not normally used. The parallel hypothesis method allows the in-situ identification of acoustic multipath time-of-flight measurements between a vehicle and an external transponder and uses them in real-time to augment the navigation algorithm during periods when direct-path time-of-flight measurements are not available. A proof of concept was conducted using real-world data obtained by the Woods Hole Oceanographic Institution Deep Submergence Lab's Autonomous Benthic Explorer (ABE) and Sentry autonomous underwater vehicles during operations on the Juan de Fuca Ridge. This algorithm uses a nested architecture to break the navigation solution down into basic building blocks for each type of available external information. The algorithm classifies external information as either line of position or gridded observations. For any line of position observation, the algorithm generates a multi-modal block of parallel position estimate hypotheses. The multimodal hypotheses are input into an arbiter which produces a single unimodal output. If a priori maps of gridded information are available, they are used within the arbiter structure to aid in the elimination of false hypotheses.

(cont.) For the proof of concept, this research uses ranges from a single external acoustic transponder in the hypothesis generation process and grids of low-resolution bathymetric data from a ship-based multibeam sonar in the arbitration process. The major contributions of this research include the 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.