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

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

The paper presents the creation of an integrated digital-physical testbed aimed at advancing research in ship autonomy, specifically focusing on Maritime Autonomous Surface Ships (MASS). Developed by researchers from the Norwegian University of Science and Technology (NTNU), this testbed facilitates a comprehensive process that ranges from algorithm development to validation in real-world scenarios, emphasizing a streamlined approach to testing which balances simulations with physical experiments. Herein, an examination of the infrastructure and methodologies used within the testbed is provided, along with a discussion on the implications of these efforts for the maritime industry.

Testbed Infrastructure

The testbed infrastructure encompasses a multi-layered approach that provides high-fidelity simulations, reduced-order model simulations, and hybrid setups with sensor integrations to create a robust environment for algorithm development. The high-fidelity simulations utilize Python-based frameworks incorporating wave-excitation models and hydrodynamic coefficients, ensuring realistic portrayals of vessel interactions with sea states. Complementary reduced-order model simulations offer efficient alternatives with lower computational overhead, focusing on rapid testing and interface integration. Finally, simplified dynamics with sensor models help in evaluating algorithms for localization and object detection, further bridging the gap between conceptual models and real-world applications.

Incorporating a digital twin environment within Unity also aids visualization and control, creating a remote interface for operational command and observation of vessel dynamics. This facilitates the examination of control algorithms under diverse conditions, providing essential insights into performance metrics.

Practical and Theoretical Implications

The practical implications of this research are significant, providing a platform for validating autonomous navigation algorithms which hold promises for future innovations in maritime logistics and operations. By offering accessible model-scale tests paired with simulation environments, the testbed lowers entry barriers and mitigates risks while significantly cutting costs. This fosters both academic and industry collaboration, potentially hastening commercial deployments of autonomous vessels and contributing to increased safety, reduced human error, and lower emissions in shipping.

From a theoretical perspective, the testbed offers extensive data, including publicly accessible hydrodynamic datasets that include wave-excitation models and response amplitude operators (RAOs) from vessel-based simulations. These datasets advance academic study by enabling researchers to investigate vessel behavior under different conditions and refine theoretical models.

Future Developments and Challenges

Although the testbed provides a comprehensive and advanced environment for research and testing, scalability remains a challenge due to resource requirements. The transition from model-scale to full-scale validation—despite its importance—is limited by real-world complexity, which small-scale models may not fully replicate.

Future efforts might include extending simulations to include forward-speed hydrodynamic data, refining the scalability of the platform, and facilitating collaborations that leverage data for commercial autonomous vessel operations. Plans for expanding test capabilities with more advanced facilities such as the Hydro-Cybernetics Laboratory are underway, promising broader research applicability and further advancements in marine cybernetics.

Conclusion

This paper encapsulates an ambitious step in maritime autonomy research by detailing an integrated testbed that addresses algorithm validation from theoretical conception to practical deployment. The testbed bridges the gap between simulations and physical experiments, promising safer and more economic marine operations through the incorporation of autonomous technologies. By laying the groundwork for advanced research and offering robust experimental possibilities, this work contributes valuably to the ongoing evolution of autonomous maritime systems and heralds future prospects in this growing field of study.