A ship autonomous navigation decision-making method based on the OODA loop theory

Abstract

Ship autonomous navigation plays a vital role in ensuring the safety and efficiency of maritime transportation. However, existing methods are limited in handling complex ship encounter scenarios, and their output collision avoidance decisions suffer from insufficient rationality and practical applicability. To address these challenges, this study proposes a ship autonomous navigation decision-making method based on the Observe–Orient–Decide–Act (OODA) loop theory. This framework establishes a complete closed-loop navigation logic, covering situational awareness, risk assessment, decision generation, and motion control. In the observation module, a quaternion ship domain model is used to determine the dynamic safety boundary of ships, and a ship encounter recognition method based on relative orientation is constructed. A risk assessment module is developed that quantifies collision risk levels and multi-vessel avoidance priorities by integrating ship maneuvering constraints and dynamic maneuvering intervals. In the decision-making module, a feasible ship maneuvering interval model and a ship collision avoidance decision model are constructed by combining a three-degree-of-freedom ship motion model and an improved velocity obstacle algorithm to obtain a safe and feasible maneuvering decision scheme that meets the requirements. Finally, the Act module is constructed to execute decision instructions through the ship control system. Through simulation verification on the OpenCPN platform, the proposed method realizes safe collision avoidance of all target vessels in complex encounter scenarios. The minimum relative distance between the own ship and target ships exceeds the safety distance throughout avoidance, and DCPA is substantially larger than the safety threshold even when TCPA approaches zero. By tuning course and speed, this method yields COLREGs-compliant safe collision avoidance strategies and provides feasible technical support for the practical implementation of autonomous navigation systems.