2025/05/10 by Emir Cem Gezer, Mael Korentin Ivan Moreau, Gezer, Emir Cem +10 · 1 citation
Engineering · Environmental Science · #Automation #Command and control #Maritime Navigation and Safety #Maritime Transport Emissions and Efficiency #Model validation #Operability #Pipeline (software) #Set (abstract data type) #Ship Hydrodynamics and Maneuverability #Testbed #Verification and validation
paper · pdf · doi:10.48550/arxiv.2505.06787
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
The algorithms developed for Maritime Autonomous Surface Ships (MASS) are often challenging to test on actual vessels due to high operational costs and safety considerations. Simulations offer a cost-effective alternative and eliminate risks, but they may not accurately represent real-world dynamics for the given tasks. Utilizing small-scale model ships and robotic vessels in conjunction with a laboratory basin provides an accessible testing environment for the early stages of validation processes. However, designing and developing a model vessel for a single test can be costly and cumbersome, and researchers often lack access to such infrastructure. To address these challenges and enable streamlined testing, we have developed an in-house testbed that facilitates the development, testing, verification, and validation of MASS algorithms in a digital-physical laboratory. This infrastructure includes a set of small-scale model vessels, a simulation environment for each vessel, a comprehensive testbed environment, and a digital twin in Unity. With this, we aim to establish a full design and verification pipeline that starts from low-fidelity and moves up to high-fidelity simulation models of each vessel, and thereby to the model-scale testing of the vessel in the laboratory basin. Further advancement allows moving towards semi-full-scale validation with R/V milliAmpere1 and full-scale validation with R/V Gunnerus. In this work, we present our progress on the development of this testbed environment and its components, demonstrating its effectiveness in enabling ship autonomy guidance, navigation, and control (GNC) algorithms.