Monitoring wildfires using multiple unmanned aerial vehicles (UAVs) is essential for timely intervention and management of the fire while minimising risks to human lives. A research gap was identified for practical UAV solutions integrating critical features, such as localisation, fire safety and battery management. This thesis presents a novel rule-based algorithm designed for effective wildfire monitoring using cooperative UAVs. A simulation environment with a wildfire spread model was developed to test and evaluate various strategies under dynamic conditions. The proposed algorithm operates in two phases: first, locating the fire and then monitoring its progression. Key features include a heat avoidance mechanism to maintain UAV safety and recharging schedules to maximise operational uptime. A fire detection map is centrally created using the infrared data from the UAVs. The convex hull around the fire serves as the basis for path planning to track the fire perimeter. The best strategy is determined and optimised through a series of experiments in the simulation environment. The overall best-performing algorithm makes navigational decisions based on the importance of different sections of the fire perimeter. The importance of the perimeter is constantly updated based on the fire spread, visit times, and distances of the UAVs. The assignment of various UAV tasks was reviewed to enhance UAV cooperation, which showed performance benefits across different scenarios. Other key findings
include the recommendation of a small fleet size of up to six UAVs, with a flight speed of 10 m/s at an altitude of 100 meters, balancing costs, performance and safety. The algorithm demonstrated performance equal to state-of-the-art reinforcement learning techniques
while offering advantages in explainability. Additionally, the algorithm has been successfully validated in a lab environment, demonstrating its potential as a practical and cost-effective solution for wildfire monitoring. This work brings a fire monitoring system closer to real-world implementation and will possibly help fight wildfires effectively.