Development of the Saemangeum Digital Test-Bed (K-URSim) for Unmanned Underwater Vehicles
Yeongjun Lee, Jong-Boo Han, Sangsu Kim, Hojun Kang, Kihun Kim
AI summary
Problem
Validating unmanned marine systems in real oceans is costly, risky, and difficult to repeat, while existing simulators rely on synthetic environments that fail to capture real-world dynamics.
Approach
The authors developed K-URSim, a modular ROS2-based simulation platform tightly coupled with the Saemangeum enclosed-sea test site, using real-time in-situ ocean data to drive realistic digital-physical hybrid testing.
Key results
- Modular ROS2-based simulation architecture (KRISO Extensions) for marine robotics
- Real-time integration of in-situ ocean data (currents, waves, bathymetry) into simulation
- Support for reinforcement learning autonomy and synthetic data generation
- Digital-physical hybrid testing framework coupled with the Saemangeum test site
Why it matters
Provides researchers and engineers with a reliable, cost-effective pipeline for pre-validating and transferring autonomous marine algorithms from simulation to real-world deployment.
Abstract
This paper presents K-URSim (KRISO Under- water Robot Simulator), a ROS2-based modular simulation platform that serves as the backbone of the Saemangeum Digital Marine Testbed for unmanned marine systems (UMS). Unlike conventional simulators, K-URSim is tightly coupled with the real inshore test site, integrating in-situ ocean data such as currents, waves, and bathymetry into the simulation loop for realistic environment reproduction and data-driven validation. The platform adopts a modular architecture (KRISO Exten- sions) supporting vehicle modeling, physics-based dynamics, sensing, planning, control, and external interfaces within a unified ROS2 framework. By bridging simulation and real- world experiments, K-URSim enables pre-validation of control algorithms and mission scenarios prior to deployment, reducing cost and risk. It also supports reinforcement learning-based autonomy and synthetic data generation for sim-to-real transfer. The system can also integrate with NVIDIA Omniverse for digital–physical hybrid testing and sim-to-real validation.