RL-based path planning for an over-actuated floating vehicle under disturbances

This paper investigates the use of reinforcement learning for the path planning of an autonomous triangular marine platform in unknown environments under various environmental disturbances. The marine platform is over-actuated, i.e. it has more control inputs than degrees of freedom. The proposed approach uses a high-level online least-squared policy iteration scheme for value function approximation in order to estimate sub-optimal policy. The chosen action is considered as the desired input to a fast and efficient low-level velocity controller. We evaluate our approach in a simulated environment, including the dynamic model of the platform, the dynamics and limitations of the actuators, and the presence of wind, wave, and sea current disturbances. Simulation results are presented that demonstrate the performance of the proposed approach. Despite the model dynamics, the actuation dynamics and constrains, and the environmental disturbances, the presented results are promising.