Model Predictive Control for Autonomous Ship Navigation with COLREG Compliance and Chart-Based Path Planning

Autonomous ship navigation systems must ensure safe and efficient route planning while complying with the International Regulations for Preventing Collisions at Sea (COLREGs). This paper presents an integrated navigation framework that combines chart-based global path planning with a Model Predictive Control (MPC) approach for local trajectory track-ing and COLREG-compliant collision avoidance. The method generates feasible reference routes using maritime charts and predefined waypoints, while the MPC controller ensures precise path following and dynamic re-planning in response to nearby vessels and coastal obstacles. Coastal features and shorelines are modeled using Global Self-consistent, Hier-archical, High-resolution Geography (GSHHG) data, enabling the MPC to treat land-masses as static obstacles. Other vessels are represented as dynamic obstacles with vary-ing speeds and headings, and COLREG rules are embedded within the MPC framework to enable rule-compliant maneuvering during encounters. To address real-time computa-tional constraints, a simplified MPC formulation is introduced, balancing predictive ac-curacy with computational efficiency, making the approach suitable for embedded im-plementations. The navigation framework is implemented in a MATLAB-based simula-tion with real-time visualization supporting multi-vessel scenarios and COLREG-aware vessel interactions. Simulation results demonstrate robust performance across diverse maritime scenarios—including complex multi-ship encounters and constrained coastal navigation—while maintaining the shortest safe routes. By seamlessly integrating chart-aware path planning with COLREG-compliant, MPC-based collision avoidance, the proposed framework offers an effective, scalable, and robust solution for autonomous maritime navigation.