Dynamic Fire Spread-Based Cooperative Path Planning for UAV Swarms in Forest Fire Suppression

The rapid spread and dynamic behavior of forest fires in complex environments challenge multi-UAV firefighting systems, as traditional isolated strategies often fail to balance global fire boundary prioritization with real-time adaptive path planning, leading to delays and inefficiency. This study proposes a hybrid framework integrating a genetic algorithm (GA) to dynamically identify high-priority fire boundaries based on spread velocity and an ant colony optimization (ACO) algorithm to generate collision-free suppression paths. The model focuses on minimizing extinguishing time and total fire spread area, tested under scenarios simulating wind-driven spread and irregular boundary expansion. Results demonstrate the hybrid strategy ensures timely intervention and effective containment of fire spread. Future work will involve simulating complex forest fire scenarios, taking into account factors such as terrain slope, heterogeneous vegetation flammability, and dynamic wind patterns. This will aid in studying more realistic phenomena of forest fire spread.