Critical Factors Associated with Transition Between Manual and Autonomous Modes of Operation for Autonomous Functions on Marine Vessels
Drs. S. Rathinam, H. Kang, and P. Pagilla and students Anthony Saaiby, Mayur Patil, Nataraj Sudharsan, and Jiachang Xing at Texas A&M are working with a team of Subject Matter Experts at ABS led by Jin Wang, Director of Technology – Energy Transition, on Critical Factors Associated with the “Transition between Manual and Autonomous Modes of Operation for Autonomous Functions on Marine Vessels.”
The maritime industry is rapidly adopting autonomous technologies, exemplified by the development of Maritime Autonomous Surface Ships (MASS). These advancements promise significant improvements in safety and operational efficiency. However, the implementation of autonomous systems poses substantial challenges, including high development costs, logistical complexities, and the inherent risks of real-world testing. Replicating rare or extreme conditions—such as severe weather, equipment malfunctions, or collision scenarios—safely and economically adds to these difficulties.
To address these challenges, this work presents a modular, high-fidelity simulation framework designed to provide a controlled and cost-effective virtual environment for the testing and certification of autonomous systems. Developed using MATLAB and Unity, this framework enables the simulation of complex maritime scenarios, including adverse weather conditions, equipment failures, and near-miss events, which are often hazardous or impractical to replicate in real-world settings. The framework integrates advanced physics, realistic sensor modeling, and dynamic environmental factors, ensuring a high degree of fidelity in testing and validation.
A comprehensive set of Performance Indicators (PIs) and customizable test scenarios are developed and embedded within the framework to evaluate the safety, reliability, and compliance of autonomous systems with maritime standards.
A key aspect of the project is its emphasis on modularity, which has been integrated into both the simulated environment and the system components. This modularity ensures flexibility, scalability, and adaptability, making the simulation framework suitable for a wide range of maritime applications and use cases. By allowing users to customize and test individual components—either independently or as part of a complete system—the framework supports efficient development, testing, and optimization of autonomous systems.
The framework includes modular sensor plugins that enable users to simulate and test various sensor characteristics, such as range, resolution, noise levels, and sampling rates. This flexibility allows companies to evaluate how their autonomous systems perform under different sensor configurations and optimize sensor selection and integration for specific operational needs. Adjusting sensor properties also allows users to replicate realistic conditions or stress-test systems in extreme scenarios—such as poor visibility or high traffic density—ensuring reliable performance across a range of situations.
Modularity also applies to vessel models, allowing users to integrate custom Computer-Aided Design (CAD) vessel designs or use predefined models included within the framework. This supports testing across a wide variety of vessel types, from small unmanned surface vehicles (USVs) to large cargo ships.
By customizing vessel geometry and dynamics, companies can evaluate system performance in various operations such as harbor navigation, offshore activities, or open-sea missions. This adaptability helps identify system-specific challenges and ensures each system is suited to its intended use case.
The project’s communication framework has been successfully developed, with modular headings defined for key areas—Planning, Guidance, Perception, and Motion Planning—within the algorithm division. This structure lays a strong foundation for systematically addressing each core component in the next development phase, leading to a comprehensive autonomous navigation system. In parallel, the dynamics division is advancing work on hydrostatics, hydrodynamics, and a dynamic 6 Degrees of Freedom (6DOF) model—critical elements for accurately simulating the physical forces acting on vessels and ensuring the realism of the simulation environment.
Testing scenarios will be designed to assess the system’s adaptability and resilience under varying degrees of complexity, inspired by real-world maritime challenges. These scenarios will include environmental factors such as wave heights, currents, wind forces, and lighting conditions. For instance, simulations of low-light or nighttime operations will evaluate system performance with reduced camera dependency, highlighting the importance of integrating sensors like Light Detection And Ranging (LiDAR) and radar. Similarly, testing in rough seas or high-traffic conditions will validate the system’s robustness, ensuring reliable performance in demanding operational environments. This high level of environmental fidelity will help refine autonomous capabilities and improve system readiness for real-world deployment.
Ultimately, the goal is to develop a versatile and scalable simulation platform that accelerates the testing and validation of autonomous maritime systems. The integration of modular libraries, advanced simulation environments, and embedded performance indicators ensures the platform serves as a comprehensive tool for the marine industry to develop and test cutting-edge navigation technologies. With its high degree of customization and realistic environmental modeling, the framework will remain adaptable to the evolving demands of the maritime sector—fostering safer and more efficient autonomous operations.