Autonomous sailing with sim-to-real reinforcement learning

Autonomous sailing offers a sustainable alternative for reducing greenhouse gas emissions in maritime transport, aligning with global environmental targets. This study explores the application of reinforcement learning (RL) to autonomous sailing, addressing challenges in handling dynamic and unpredictable environmental conditions.Leveraging a sim-to-real transfer methodology, RL agents were trained in a simulation environment with the domain randomization technique to enhance adaptability and robustness, and tested in real-world scenarios using a robotic sailboat in the Offshore Basin at MARIN. The study quantified the reality gap between simulation and real-world environments, identifying key discrepancies in actuator latency and simulation modeling accuracy.Results demonstrate that RL agents trained with domain randomization achieve comparable success rates to conventional controllers while showcasing enhanced sailing capabilities like roll tacking and recovery from wind-stalled conditions. This work advances the understanding of autonomous sailing control and highlights pathways to bridge the reality gap, contributing to the broader adoption of RL in dynamic real-world applications.