Drone Swarm Coordination Using Reinforcement Learning for Efficient Wildfires Fighting

Natural events, such as wildfires, pose a serious threat to the human population and cause significant environmental and economic damage. As climate change increases the frequency and intensity of extreme natural events, more efficient solutions are required to mitigate their impacts. One proposed solution is the usage of a swarm of drones and unmanned ground vehicles to extinguish wildfires. Controlling a swarm of drones is a challenging task that requires a complex control system capable of providing a good coordination between multiple vehicles. Artificial intelligence, particularly reinforcement learning, has been proposed as a solution for the autonomous control of a swarm. In this paper, we propose a conceptual system based on reinforcement learning and swarm robotics to efficiently combat wildfires. Our system consists of an overwatch drone and multiple payload drones which are responsible for carrying fire suppressants. The goal of our research is to guide a swarm of payload drones to a wildfire while preserving a tight swarm grouping. Each drone is individually controlled and aware of the position of the goal and the positions of other agents. To compare different algorithms, we tested a DQN, Rainbow DQN and FQF using a simulation and four AirSim scenarios. With our approach, we achieve impressive results in controlling a swarm of drones towards a common goal. Our research demonstrates the possibility of utilizing reinforcement learning to control a swarm of drones for wildfire fighting. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.