Automatic Sail: AI-Driven Optimization for Sustainable Maritime Navigation and Wind-Assisted Propulsion – AutoSail

Shipping accounts for 2.9% of global CO2 emissions, with a potential increase of 44% by 2050. To address this issue, wind-assisted propulsion solutions like Norsepower's rotor sails and Michelin's inflatable sails are being explored. However, there is limited research on real-time adaptive control strategies for sail propulsion. Our project leverages data science and AI to optimize sail adaptation and autonomous navigation for maritime transport.

The first objective is to develop feedback control methods for sail and rudder operations based on environmental data (wind direction, sail deformation, ship dynamics). The intrinsic complexity of flow dynamics will be addressed by guiding AI with system analysis in the infinite-dimensional framework. By integrating AI algorithms, such as reinforcement learning and neural networks, with real-time data from sensors and conservation laws from physics, we aim to dynamically adjust the sails to maximize propulsion efficiency and minimize energy consumption. Methods exploiting Navier-Stokes equations will be used to optimize sail trim in varying wind conditions, ensuring optimal lift and drag forces for propulsion.

The second objective is to enhance navigation and path planning for autonomous surface vehicles. Using real-time data from GPS, LiDAR, cameras, and ocean models, we will develop intelligent guidance, navigation, and control systems. Path planning methods will be developped to find optimal, energy-efficient routes that adapt to wind, waves, and ocean currents. AI-based control strategies, such as model-free reinforcement learning, will help the vessel adapt to changing conditions and avoid obstacles in complex marine environments.

By integrating AI, data science, and control theory, our project aims to improve the energy efficiency of sailing ships and reduce emissions. The algorithms will be tested on a sailboat funded and equipped by the project, allowing for an experimental evaluation of the methods.