Obstacle Avoidance for Autonomous Sailboats via Reinforcement Learning with Coarsetofine Strategy Obstacle avoidance of autonomous sailboats is a complicated task due to big inertia, highly nonlinear motion of sailboat and uncertain disturbance from wind and water current. To deal with the obstacle avoidance problem of autonomous sailboats, we promote a novel method based on reinforcement learning with coarsetofine strategy. In this strategy, coarse stage is used to detect the autonomous sailboat roughly in the test environment. Then fine stage is applied to localize the autonomous sailboat accurately. Hereby, the avoidance performance is improved by the transition from coarse to fine. We have verified our algorithms both in simulation and real experiments. With our method, the sailboat avoids the obstacle in higher precision than the method without the coarsetofine strategy. The final success rate of obstacle avoidance is near 83% and the rate of reaching goal is 70%. Both simulation results and experiments show that our method is effective.

An Adaptive Position Keeping Algorithm For Autonomous Sailboats Autonomous sailboats are potential solutions for insitu ocean measurement thanks to their high energy efficiency. Indeed, the ability for them to maintain their position is crucial for ocean data collection. Previous study has demonstrated a position keeping method for an autonomous sailboat and has theoretically proved the stability. As this method is ineffective against wind disturbance, we propose a more feasible algorithm for real environment in this paper. A retrofitted model sailboat is developed to evaluate the performance of our approach in an artificial wind field. Simulation and experiment results under several different cases show that our proposed algorithm is robust.