Exploring feasibility of UAV-Based Situational Awareness Support for Maritime Autonomous Surface Ships through Scenario-Based Sea Trial

Abstract

This study investigates the feasibility of employing unmanned aerial vehicle (UAV) to enhance the situational awareness capabilities of Maritime Autonomous Surface Ships (MASS), thereby improving the reliability of their operations. However, fixed situational awareness sensors on MASS, including cameras, LiDAR, and radar, are subject to inherent physical limitations due to their static installation, reducing their effectiveness in dynamic and complex navigational environments. To address these limitations, data collected from scenario-based real-sea trials were applied in simulations to evaluate the effectiveness of UAV assisted situational awareness. For this purpose, we designed and conducted trials using the sea-trial testbed vessel HaeyangNuri. The representative operational scenario was set in a geographically constrained channel. For the simulation, the vessel’s motion was modeled using the Velocity Obstacle method, and a weighted exponential model, incorporating the distance and time to the closest point of approach (DCPA and TCPA), was developed to quantitatively assess collision risk. The results demonstrated reduced collision risk and improved operational stability when UAV were employed. These findings highlight the potential of UAV to support MASS navigation, and future work will focus on developing a robust MASS？UAV cooperative framework for broad operational applications to further enhance the operational reliability of MASS.