Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning
This work addresses the complex coordination challenge for autonomous aircraft in wildfire monitoring, offering a scalable solution for firefighters, though it is incremental as it builds on existing deep reinforcement learning methods.
The paper tackles the problem of controlling multiple autonomous fixed-wing aircraft for wildfire surveillance by developing two deep reinforcement learning approaches for decentralized controllers, which in simulations accurately track wildfire expansions and outperform an online receding horizon controller.
Teams of autonomous unmanned aircraft can be used to monitor wildfires, enabling firefighters to make informed decisions. However, controlling multiple autonomous fixed-wing aircraft to maximize forest fire coverage is a complex problem. The state space is high dimensional, the fire propagates stochastically, the sensor information is imperfect, and the aircraft must coordinate with each other to accomplish their mission. This work presents two deep reinforcement learning approaches for training decentralized controllers that accommodate the high dimensionality and uncertainty inherent in the problem. The first approach controls the aircraft using immediate observations of the individual aircraft. The second approach allows aircraft to collaborate on a map of the wildfire's state and maintain a time history of locations visited, which are used as inputs to the controller. Simulation results show that both approaches allow the aircraft to accurately track wildfire expansions and outperform an online receding horizon controller. Additional simulations demonstrate that the approach scales with different numbers of aircraft and generalizes to different wildfire shapes.