Researchers at IIT-Madras have developed SEANAV (Sea Navigation), an advanced real-time digital twin platform designed to test, validate and train autonomous maritime navigation systems within highly realistic virtual environments.

Developed by the Department of Ocean Engineering under the leadership of Associate Professor Suresh Rajendran, the platform represents a significant step towards accelerating the deployment of autonomous vessels while reducing the risks and costs associated with conventional sea trials.

The innovation addresses a fundamental challenge confronting the global shipping industry. Maritime transport carries more than 80 per cent of global trade, yet testing autonomous navigation technologies in real-world waters remains expensive, operationally complex and potentially hazardous.

SEANAV has been created to overcome these obstacles by providing a highly realistic virtual maritime environment where vessels and navigation systems can be evaluated safely before deployment at sea.

Rather than relying exclusively on open-water testing, researchers can use the platform to conduct large-scale simulations involving vessels, sensor systems, ocean conditions and maritime traffic in real time.

This approach significantly reduces development costs while accelerating the validation process for emerging autonomous shipping technologies.

Built on the Unity3D engine, SEANAV creates a digital twin of the maritime environment capable of accurately reproducing ocean waves, vessel dynamics and navigation scenarios.

The simulator has been calibrated using a benchmark tanker vessel measuring approximately 320 metres in length, allowing the behaviour observed in the virtual environment to closely mirror actual commercial shipping operations.

One of the platform’s most significant capabilities is its support for multi-agent artificial intelligence. Multiple autonomous vessels can operate simultaneously within the same virtual space, enabling researchers to study vessel interactions in congested waterways and develop intelligent navigation systems capable of functioning safely under realistic traffic conditions.

The system has already demonstrated autonomous path-following and AI-driven collision avoidance under representative sensing and wave conditions. This capability is particularly important as future autonomous vessels will be required to navigate complex maritime routes while complying with internationally recognised navigation regulations.

SEANAV also incorporates sophisticated sensor modelling far beyond what is available in many conventional simulators.

Researchers can evaluate the performance of navigation systems using combinations of LiDAR, marine radar and stereo vision cameras while accounting for real-world complications such as signal noise, sensor delays and data fusion challenges.

The platform is capable of simulating difficult operating conditions including rain, fog and other adverse weather scenarios. As a result, engineers can assess the reliability of autonomous navigation technologies under circumstances that are often dangerous or impractical to reproduce through physical trials.

To improve environmental awareness and target detection, the research team has advanced multi-sensor fusion techniques that combine the strengths of different sensing technologies. The project includes the StereoLiD framework, which integrates stereo vision systems with LiDAR-generated information to enhance depth estimation and target localisation. A Kalman filtering approach is used to combine information from multiple sensors and improve accuracy in complex marine environments.

According to the researchers, these techniques help overcome challenges commonly faced by optical sensors, including poor visibility, variable lighting and dense fog. This creates a more dependable navigation environment for future autonomous vessels operating on busy shipping routes.

The research team has also worked on strengthening maritime computer vision capabilities. Existing maritime datasets have limitations in terms of viewing angles and weather diversity. To address these shortcomings, the team developed a new vessel image dataset known as DetectShips, containing approximately 14,000 ship images, which was combined with existing maritime datasets to improve the performance of vessel-recognition algorithms.

Another major feature of SEANAV is its virtual reality integration. Through Meta Quest headsets, researchers, trainees and maritime professionals can experience operations from a ship’s bridge perspective. This immersive environment allows users to practise vessel manoeuvring, port approaches, navigation procedures and emergency-response situations without exposing personnel or equipment to real-world risks.

The virtual bridge environment creates opportunities for both technological development and professional training. Human operators can gain experience within realistic maritime conditions while autonomous systems are tested alongside them, providing a valuable pathway towards future human-machine collaboration at sea.

Highlighting the significance of the platform, Associate Professor Suresh Rajendran stated: “SEANAV provides a realistic and scalable platform for developing and validating autonomous maritime navigation technologies.

By combining digital twins, AI, advanced sensor fusion, and virtual reality, we are creating an environment where both autonomous systems and human operators can be trained safely and efficiently before deployment in real-world maritime settings.”

He further noted: “The platform can also strengthen safety at major Indian ports, including Chennai, Mumbai, Visakhapatnam and Kochi, by supporting the development of AI-assisted navigation systems capable of reducing collision and grounding risks. Smarter navigation strategies can further help reduce fuel consumption, lower carbon emissions and minimise the risk of environmental accidents such as oil spills.”

Beyond autonomous shipping, the platform offers wider strategic applications. Its ability to simulate multiple vessels simultaneously makes it suitable for coastal surveillance missions, search-and-rescue operations and maritime defence applications. These capabilities could support both civilian maritime authorities and national security organisations tasked with monitoring busy coastal regions.

The development is also closely aligned with India's maritime modernisation ambitions. SEANAV supports the objectives of the Sagarmala Program and Maritime India Vision 2030, both of which emphasise smart ports, digital technologies, operational efficiency and autonomous maritime systems. The platform therefore contributes not only to technological innovation but also to broader national efforts aimed at transforming maritime infrastructure and logistics.

The project has already gained academic recognition. Research findings associated with SEANAV have been published in the Journal of Offshore Mechanics and Arctic Engineering and presented at the ASME International Conference on Ocean, Offshore and Arctic Engineering in Vancouver and Tokyo, highlighting international interest in the technology and its potential contribution to the future of autonomous shipping.

As global shipping moves steadily towards greater automation, digital twin environments such as SEANAV are expected to play an increasingly important role.

By combining artificial intelligence, advanced sensing technologies, virtual reality and realistic maritime simulation, IIT-Madras has created a platform capable of bridging the gap between laboratory research and real-world maritime deployment while helping improve safety, efficiency and sustainability across the shipping sector.

IDN (With Agency Inputs)