Optimal Route Generation and Route-Following Control for Autonomous Vessel

In this study, basic research was conducted regarding the era of autonomous vessels and artificial intelligence (deep learning, big data, etc.). When a vessel is navigating autonomously, it must determine the optimal route by itself and accurately follow the designated route using route-following control technology. First, the optimal route should be generated in a manner that ensures safety and reduces fuel consumption by the vessel. To satisfy safety requirements, sea depth, under-keel clearance, and navigation charts are used; algorithms capable of determining and shortening the distance of travel and removing unnecessary waypoints are used to satisfy the requirements for reducing fuel consumption. In this study, a reinforcement-learning algorithm-based machine learning technique was used to generate an optimal route while satisfying these two sets of requirements. Second, when an optimal route is generated, the vessel must have a route-following controller that can accurately follow the set route without deviation. To accurately follow the route, a velocity-type fuzzy proportional-integral-derivative (PID) controller was established. This controller can prevent deviation from the route because overshoot rarely occurs, compared with a proportional derivative (PD) controller. Additionally, because the change in rudder angle is smooth, energy loss by the vessel can be reduced. Here, a method for determining the presence of environmental disturbance using the characteristics of the Kalman filter innovation process and estimating environmental disturbance with a fuzzy disturbance estimator is presented, which allows the route to be accurately maintained even under conditions involving environmental disturbance. The proposed approach can automatically set the vessel's optimal route and accurately follow the route without human intervention, which is useful and can contribute to maritime safety and efficiency improvement.