Copyright Interesting Engineering
The Korea Institute of Machinery and Materials (KIMM) has developed a next-generation autonomous fire suppression system that can detect and extinguish oil fires aboard naval vessels even under rough sea conditions. The AI-driven system independently verifies the authenticity of a fire, activates only when one is confirmed, and directs its suppression precisely at the source, much like a human firefighter. The system, developed by Senior Researcher Hyuk Lee and his team at KIMM’s AX Convergence Research Center, completed successful trials aboard a real naval vessel. It is an advanced version of the team’s earlier autonomous firefighting research, now adapted for the oil fires most common on naval ships. Unlike traditional firefighting systems that release extinguishing agents throughout an entire compartment, KIMM’s technology targets only the fire source. This prevents unnecessary damage during false alarms. By using AI-based detection and reinforcement learning, the system adapts to ship movement and sea conditions to ensure accurate discharge. The technology includes sensors, fire monitors, and a control unit with AI-based fire verification and location estimation capabilities. It achieved a fire detection accuracy rate of more than 98 percent and can discharge foam up to 24 meters. Tests also confirmed stable operation even in sea states of 3 or higher. Tested for real-world conditions Before shipboard testing, the team verified performance using a full-scale simulation facility measuring 25 by 5 by 5 meters. The facility replicated real ship compartments, including lighting and color conditions. Researchers recreated various fire and non-fire situations, such as lighters, welding sparks, and electric heaters, to train the AI for accurate fire identification. The system successfully handled both open-area and shielded oil fires, including those likely to occur on aircraft carriers. During testing, it extinguished a 4.5-square-meter open fire and a shielded fire beneath a helicopter-sized structure. These results proved its ability to respond to complex fire conditions at sea. Real-ship tests were later conducted aboard the ROKS Ilchulbong, an LST-II class amphibious assault ship. There, the system accurately targeted an oil fire 18 meters away, even in one-meter-high waves. To maintain precision, KIMM developed a reinforcement learning algorithm that continuously adjusts the nozzle’s aiming angle using six degrees of freedom acceleration data to compensate for wave and hull movement. Expanding safety beyond naval use “This newly developed initial suppression firefighting system for shipboard oil fires is the world’s first technology to complete step-by-step verification from land-based simulation facilities to actual shipboard environments,” said Senior Researcher Hyuk Lee of KIMM. “It can autonomously respond to the most dangerous oil fires on ships in both open and shielded conditions, marking a groundbreaking turning point for crew safety and preserving the ship’s combat effectiveness.” He added that the system’s applications extend well beyond naval use. “This technology is applicable not only to various naval vessels but also to ammunition depots, military supply warehouses, aircraft hangars, and offshore plants,” he said. “Its future expansion to civilian ships and petrochemical facilities will significantly enhance fire safety at sea and in industrial settings.” With its combination of AI precision, adaptive learning, and successful real-world testing, the KIMM system represents a significant advancement toward autonomous firefighting technologies for maritime and industrial safety.
