Taiwan's Ocean Affairs Council and National Cheng Kung University Complete 20-Drone Swarm Simulation Using MARL for Maritime Surveillance
Taiwan's Ocean Affairs Council and National Cheng Kung University have completed a simulation of a 20-drone swarm using Multi-Agent Reinforcement Learning to enhance the Coast Guard Administration's autonomous maritime monitoring capabilities.
According to TechNews, the Taiwan Ocean Affairs Council is collaborating with National Cheng Kung University (NCKU) to develop an autonomous drone swarm system in response to China's grey-zone tactics around Taiwan. The project has successfully completed simulations involving two swarms totaling 20 drones, aiming to use AI for autonomous task allocation, route planning, and flight coordination to expand the Coast Guard Administration's surveillance reach.
Executed by Associate Professor 闕志哲's team from NCKU's Department of Aeronautics and Astronautics, the research integrates artificial intelligence, flight mechanics, and cooperative control technologies. The team implemented Multi-Agent Reinforcement Learning (MARL) to enable the drones to self-organize and reduce the need for individual manual control. To ensure formation safety, the control algorithms incorporate three-dimensional distance constraints with a minimum vertical separation of 100 feet (approx. 30 meters), along with dynamic obstacle avoidance capabilities.
The simulations focus on three VTOL fixed-wing drone models: JUMP 20, V-BAT, and Flexrotor. Notably, the V-BAT, developed by US defense tech firm Shield AI, was recently delivered to the Coast Guard Administration; it features over 12 hours of endurance and is suitable for long-duration maritime patrols. While the simulation phase is complete, the system remains in the verification stage. Future steps include Hardware-in-the-Loop (HIL) testing, prototype integration, and actual maritime flight trials to confirm reliability under various sea states and weather conditions.