I-Corps: Translation Potential of Artificial Intelligence (AI)-Driven Wave Predictions for Sustainable Shipping and Fuel Efficiency

This I-Corps project is based on the development of an intelligent maritime navigation system that helps ships avoid fuel-wasting ocean conditions by optimizing routes in real time. The global shipping industry spends over $150 billion annually on fuel, with more than 20% lost due to inefficient routing through encountering waves. This technology offers a solution using autonomous, low-cost ocean sensors to collect real-time wave data and deliver smart route guidance through an intuitive software application. The system enables up to 6–10% fuel savings per trip, potentially cutting $10–14 billion in costs annually and reducing global emissions by 3%. This saving is equivalent to removing 12 million cars from the road. The technology may provide real-time marine data that may benefit cargo shipping companies, marine tour operators and soil barge operators as well as smaller operators such as yacht and tour boat companies. Additionally, scientists, the offshore wind industry, and the ocean wave power sector, may benefit from using these predictions and models.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a real-time, artificial intelligence (AI)-powered ocean forecasting and routing system for maritime navigation. Despite advances in ocean modeling, limited real-time wave data and sparse satellite coverage continue to hinder routing accuracy and fuel efficiency. This technology integrates ocean data acquisition with machine learning-based forecasting to overcome these limitations. At its core is a fleet of autonomous ocean drones (sailbots) powered by wind, wave, and solar energy. These drones collect high-resolution wave, wind, and ocean health data across wide areas and operate as a low-cost, self-powered mesh network for real-time data sharing and distributed learning. The sailbot’s affordability and modularity support economical multipoint deployment at scale. The system uses AI to accelerate complex wave simulations without sacrificing accuracy, enabling predictive routing optimized for safety, fuel use, and comfort. Users benefit from improved operational efficiency and enhanced safety. Beyond navigation, this high-resolution, distributed data platform has applications in offshore wind forecasting, weather pattern modeling, and autonomous maritime logistics, which are sectors that increasingly rely on localized, real-time ocean intelligence for informed decision-making and resilient operations.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Award Number: 2533913
Principal Investigator: Reza Alam
Funds Obligated: $50,000
State: CA