Yara: An Ocean Virtual Environment for Research and Development of Autonomous Sailing Robots and Other Unmanned Surface Vessels

Abstract

Overall, a big challenge in building a sailboat USV relies on the development of an autonomous system for guidance, navigation, and control (GNC) because both sail and rudder angle must be cooperatively adjusted to correct the navigation direction — traditional propelled boats can be more easily controlled with a straightforward control task to set the rudder angle. Moreover, sailing upwind requires special maneuvers to reach a given target in that unfeasible direction. Reinforcement learning emerges as a promising technique for building autonomous GNCs for sailing robots, but training the neural network with a real sailboat is impractical due to long periods of training and safety reasons. Even traditional control-based approaches are mainly tested in simulated environments due to the difficulties in building and operating a real sailboat. The issue that arises is the fidelity of these simulated environments. In this context, we propose Yara, an oceanic virtual environment with a reliable physics simulation for developing, training, and evaluating autonomous agents to operate digital twins of sailing robots in reinforcement learning and other paradigms. An autonomous sailing robot digital twin is available within the virtual environment, with the foil dynamics constructed based on a real sailing robot. We coupled these foil dynamics in Gazebo’s physics engine to compute the lift and drag forces acting on the sail, rudder, and keel. The simulated world feeds sensors such as cameras, wind sensors, and GPS. The Robot Operating System communicates these sensors’ data through topics, facilitating users’ implementation and testing of new GNC solutions. Yara provides a reliable solution for foil dynamic simulated physics that achieves a simulation speedup of 300 times on an i7 laptop with 8 GB of RAM, powered by a Nvidia RTX 3060 and running Ubuntu 20.04. With this speedup, it is possible to complete a million time steps of deep reinforcement learning training in approximately eight hours. Evaluation scenarios were presented to highlight specific features of the simulator, like the maneuverability of the sailing robot digital twin and applications to train, evaluate, and compare reinforcement learning agents and other control solutions.