Integrated Intelligent Guidance and Motion Control of USVs With Anticipatory Collision Avoidance Decision-Making

In crowded waters, multiple vessel encounter situations increase the collision risks (CRs) of unmanned surface vehicles (USVs) and hence the frequent collision avoidance (COLAV) maneuvers of USVs increase their actual sailing distances. This paper innovatively proposes a risk-prediction-based deep reinforcement learning (RPDRL) approach for the integrated intelligent guidance and motion control of USVs with anticipatory COLAV decision-making. The data sizes of detected vessels' motion states are different due to the uncertainties in the number of vessels detected by the navigation systems of a USV. To address this problem, these data are, for the first time, converted into the corresponding same-sized raster data as the states in the RPDRL approach. A new CR assessment model of the USV collisions with all the detected vessels is built to calculate the rewards in the RPDRL approach. Furthermore, actor and critic deep convolutional neural networks are created to make the anticipatory COLAV decisions which are the engine command and rudder command. Simulations and simulation comparison results on a USV demonstrate that the USV sails along a shorter path with a lower CR under the anticipatory COLAV decisions from our proposed RPDRL approach compared with a velocity obstacle method and a model predictive control method, and hence the economy and safety of USVs' autonomous navigation are enhanced.