We present a formal mathematical multi-agent modeling framework for autonomously combating a wildland fire with unmanned aerial vehicles. The problem is formulated as a collaboration between a drone and a helicopter equipped with a tanker. The modeling solutions are designed to capture the communication between agents and the information processes between the agents and their environment. The drone is used to make partial observations and use them to update probability distributions over the uncertain state of the world. We design a parameterized direct lookahead approximation policy to guide the drone through the region and promote active learning. The helicopter is used to extinguish the fire. We design a helicopter policy which anticipates the zones which will burn through the belief modeling and extinguish them. We simulate the modeling and policy solutions using a wildland fire simulation in an area of Northern California.
Cite
@article{arxiv.2301.07013,
title = {An Information-Collecting Drone Management Problem for Wildfire Mitigation},
author = {Lawrence Thul and Warren B Powell},
journal= {arXiv preprint arXiv:2301.07013},
year = {2023}
}