DMD-Based Distributed Flow Sensing for Bio-Inspired Autonomous Underwater Robots

Abstract

This paper presents a novel flow sensing method for autonomous underwater robots using distributed pressure measurements. The proposed flow sensing method harnesses a Bayesian filter and a dynamic mode decomposition (DMD)-based reduced-order flow model to estimate the dynamic flow environments. This data-driven estimation method does not rely on any analytical flow models and is applicable to many and various dynamic flow fields for arbitrarily shaped underwater robots. To demonstrate the effectiveness of the proposed distributed flow sensing approach, a simulation study with a Joukowski-foil-shaped underwater robot is presented.