Importance of data richness in identification of ship maneuvering hydrodynamic models

For autonomous surface vessels (ASVs), accurate modeling of vessel dynamics is critical for the safe and reliable design of guidance and control systems. Unlike conventional vessels with human oversight, ASVs must independently handle challenging scenarios such as dynamic collision avoidance, pursuit–evasion, interception, and close-quarter maneuvering. These tasks inherently involve sharp maneuvers, where the vessel exhibits strongly nonlinear behavior and higher-order hydrodynamic effects play a significant role in determining motion. These nonlinear effects are modeled in simulations through a set of coefficients known as hydrodynamic coefficients. Estimating hydrodynamic coefficients is challenging and has traditionally been performed using experimental Planar Motion Mechanism (PMM) tests or computational fluid dynamics (CFD). However PMM tests require specialized and expensive facilities while CFD methods are time consuming. In the recent years a new approach of estimating hydrodynamic coefficients from free running data is gaining popularity. However most studies exploring this approach rely on only standard maneuvers such as turning circles or zig-zag maneuvers. This study demonstrates that datasets created by such standard maneuvers is not sufficiently rich enough to capture the nonlinear interactions and hence fall short on estimating higher order hydrodynamic coefficients. This study addresses a structured way to quantify data richness and proposes a new maneuver that can provide a rich dataset that can estimate linear and higher order hydrodynamic coefficients accurately.