Wildfire detection and suppression using multiple UAVs based on improved ant colony algorithm

With global climate change, the severity and frequency of forest fires are increasing, posing a significant threat to ecosystems and human settlements. Fire spots are often dispersed across multiple locations, and factors such as the presence of combustible materials, prevailing climate and weather conditions, and topographical features contribute to the rapid spread of fires. Addressing the challenge of effectively extinguishing forest fires, this study proposes an advanced solution utilising unmanned aerial vehicles (UAVs) for both detection and suppression. The core of this solution is a novel multi-UAV coordination mechanism that leverages swarm intelligence to detect and suppress forest fires. Specifically, this paper proposes an improved ant colony algorithm that incorporates principles of competition and cooperation by introducing repulsive pheromones and attraction information, thereby optimising the strategy for UAVs searching for fire spots. Additionally, to better align with real-world scenarios and enhance the practical applicability of UAVs, this paper improves the wildfire spread model by incorporating dynamic parameters, and introduces a practical UAV flight model that includes advanced obstacle avoidance functionality and considers the relationship between flight speed and water load. Simulation experiments under different environments are conducted to evaluate the performance of UAVs in detecting and suppressing wildfire spread. The experimental results demonstrate that the proposed scheme exhibits greater effectiveness and robustness in extinguishing forest fires with a shorter convergence time and a broader coverage of the search area. These improvements collectively contribute a valuable tool for forest fire management and rapid response.