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Digital-physical testbed for ship autonomy studies in the Marine Cybernetics Laboratory basin

Emir Cem Gezer, Mael Korentin Ivan Moreau, Anders Sandneseng Høgden, Dong Trong Nguyen, Roger Skjetne, Asgeir Sørensen

TL;DR

The paper tackles the challenge of validating Maritime Autonomous Surface Ships (MASS) algorithms given cost and safety constraints. It proposes a digital-physical testbed that merges high-fidelity simulations, reduced-order models, and a Unity-based digital twin, linked to a fleet of model-scale vessels, to form a complete design–verify–validate pipeline. The system uses ROS 2, Python/C++, and a CI/CD workflow, and releases hydrodynamic datasets to enable benchmarking and reproducibility. The approach supports progressive testing from simulation to physical basins and points toward semi-fullscale and full-scale validation with R/V milliAmpere1 and R/V Gunnerus.

Abstract

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 with high-fidelity simulation models of each model vessel, to the model-scale testing in the laboratory basin, allowing possibilities for moving towards semi-fullscale 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 guidance, navigation, and control (GNC), including autonomy.

Digital-physical testbed for ship autonomy studies in the Marine Cybernetics Laboratory basin

TL;DR

The paper tackles the challenge of validating Maritime Autonomous Surface Ships (MASS) algorithms given cost and safety constraints. It proposes a digital-physical testbed that merges high-fidelity simulations, reduced-order models, and a Unity-based digital twin, linked to a fleet of model-scale vessels, to form a complete design–verify–validate pipeline. The system uses ROS 2, Python/C++, and a CI/CD workflow, and releases hydrodynamic datasets to enable benchmarking and reproducibility. The approach supports progressive testing from simulation to physical basins and points toward semi-fullscale and full-scale validation with R/V milliAmpere1 and R/V Gunnerus.

Abstract

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 with high-fidelity simulation models of each model vessel, to the model-scale testing in the laboratory basin, allowing possibilities for moving towards semi-fullscale 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 guidance, navigation, and control (GNC), including autonomy.
Paper Structure (18 sections, 10 equations, 5 figures, 1 table)

This paper contains 18 sections, 10 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Infrastructure
  3. Overview
  4. Digital infrastructure
  5. Simulation environment
  6. High-fidelity simulation model
  7. Reduced-order model simulation
  8. Simplified dynamics with sensor models
  9. Digital Twin
  10. Software
  11. Core modules
  12. Control and estimation modules
  13. Available data
  14. Physical infrastructure
  15. Hardware
  16. ...and 3 more sections

Figures (5)

  • Figure 1: Overview of Marine Cybernetics Laboratory components.
  • Figure 2: C/S Voyager (on the left) and C/S Enterprise I (on the right) in the Stonefish simulation environment.
  • Figure 3: Control architecture of the testbed environment showing the closed-loop system: sensors feed an EKF observer that drives controllers, whose outputs route through thrust allocation to actuators. A multiplexer allows switching between control modules.
  • Figure 4: Four corner tests for C/S Voyager in simulation (left) and physical basin (right).
  • Figure 5: Shared hardware architecture across the C/S fleet.