Vessel Behavior Decision-making and Planning for Autonomous Surface Vessels in Inland Waterway

For collision avoidance in waterway channels, Autonomous surface vessels (ASVs) need a collision-avoidance planning algorithm, but increasing sailing efficiency while ensuring safety is challenging. Because collision avoidance is a process involving numerous behaviors, achieving optimum trajectory outcomes in all cases with a single algorithm is difficult. We present a vessel behavior decision and motion planning framework for ASVs that considers impediments in this study's waterway. Our study uses a combination of state machines (SM) and model predictive control (MPC) to develop behavior and motion plans in real-time that meet kinodynamic and collision constraints in waterway channels. The suggested planning algorithm aims to maximize sailing efficiency while maintaining safety. The suggested algorithm makes use of each approach to achieve optimality and real-time performance by merging two methods in a hierarchical structure. The SM method identifies maneuver states of vessel behavior that can provide safety and establishes the best obstacles and limitations in each state using the SM. The MPC devises an ideal path that takes into account forecast uncertainty, safety, and efficiency. Simulation results indicate the vessel is more successful in regulation-compliance avoidance of impediments. The best trajectory was developed in a multivessel scenario while assuring superior safety to the system. The autonomous decision and planning system for vessels increases control performance while boosting traffic organization capacity and efficiency.