Poster
in
Workshop: 6th Robot Learning Workshop: Pretraining, Fine-Tuning, and Generalization with Large Scale Models
Reinforcement-learning robotic sailboats: simulator and preliminary results
Eduardo Vasconcellos · Ronald M. Sampaio · ANDRE PAULO ARAUJO · Esteban CLUA · philippe preux · Luiz Marcos Garcia Goncalves · Luis MartÃ
Keywords: [ Gazebo ] [ Sailing boat ] [ Digital Twin ] [ Nvidia Omniverse ] [ Reinforcement Learning ]
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, thinking about 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 and 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.