Reinforcement-learning robotic sailboats: simulator and preliminary results
This work addresses the problem of developing RL-based navigation algorithms for robotic sailboats, but it is incremental as it focuses on simulator creation without reporting concrete performance gains.
The authors tackled the challenge of creating a virtual oceanic environment for Unmanned Surface Vehicles (USV) digital twins to develop Reinforcement Learning (RL) agents for autonomous navigation, presenting modeling and implementation steps for a functional digital twin based on a real robotic sailing vessel.
This work focuses on the main challenges and problems in developing a virtual oceanic environment reproducing real experiments using Unmanned Surface Vehicles (USV) digital twins. We introduce the key features for building virtual worlds, considering using Reinforcement Learning (RL) agents for autonomous navigation and control. With this in mind, the main problems concern the definition of the simulation equations (physics and mathematics), their effective implementation, and how to include strategies for simulated control and perception (sensors) to be used with RL. We present the modeling, implementation steps, and challenges required to create a functional digital twin based on a real robotic sailing vessel. The application is immediate for developing navigation algorithms based on RL to be applied on real boats.