2 research outputs found
Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and Prediction
Small unmanned aircraft can help firefighters combat wildfires by providing
real-time surveillance of the growing fires. However, guiding the aircraft
autonomously given only wildfire images is a challenging problem. This work
models noisy images obtained from on-board cameras and proposes two approaches
to filtering the wildfire images. The first approach uses a simple Kalman
filter to reduce noise and update a belief map in observed areas. The second
approach uses a particle filter to predict wildfire growth and uses
observations to estimate uncertainties relating to wildfire expansion. The
belief maps are used to train a deep reinforcement learning controller, which
learns a policy to navigate the aircraft to survey the wildfire while avoiding
flight directly over the fire. Simulation results show that the proposed
controllers precisely guide the aircraft and accurately estimate wildfire
growth, and a study of observation noise demonstrates the robustness of the
particle filter approach
Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning
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