CAREER: Toward Robust Multi-Vehicle Multi-Scalar Underwater Robotic Navigation - A Career Development Plan

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

The objective of this career development plan is to investigate new probabilistic methods for simultaneous localization and mapping (SLAM) that will improve the precision and scale of robotic mapping in ocean science. The proposed methodology will scale to multiple heterogeneous vehicles, allow for extended exploration over long time durations and over multiple spatial scales, and be robust to the challenging limitations of the underwater environment. Many land/air SLAM methods are largely inapplicable underwater because of (a) a lack of point features of unstructured seafloor, and (b) the rapid attenuation of electromagnetic, optical, and acoustic radiation in comparison to land, air, and space. The proposed multi-vehicle multi-scalar SLAM navigation framework overcomes these challenges by fusing information from three disparate technologies: 1) real-time vision-based seafloor navigation, 2) inertial navigation systems, and 3) acoustic modem-based communication, to create a flexible navigation framework that allows for inter-nodal ranging and data sharing among heterogeneous nodes. By leveraging the perception and localization capability of neighboring vehicles via a (low-bandwidth) distributed estimation framework, the proposed methodology will provide a cooperative navigation framework that will yield improved precision and scale in underwater robotic mapping for ocean science.