Coordinated Control of UAVs for Human-Centered Active Sensing of Wildfires

TL;DR

This paper presents a distributed UAV control framework for active wildfire sensing, enabling real-time fire monitoring and safer firefighting operations through improved uncertainty reduction and firefront coverage.

Contribution

It introduces a novel dual-criterion objective function combining Kalman residuals and consensus protocols for coordinated wildfire monitoring by UAVs.

Findings

Significant reduction in environmental uncertainty residuals (over 10^2 to 10^5 times).

Enhanced firefront coverage performance compared to prior methods.

Successful demonstration on physical robots in firefighting scenarios.

Abstract

Fighting wildfires is a precarious task, imperiling the lives of engaging firefighters and those who reside in the fire's path. Firefighters need online and dynamic observation of the firefront to anticipate a wildfire's unknown characteristics, such as size, scale, and propagation velocity, and to plan accordingly. In this paper, we propose a distributed control framework to coordinate a team of unmanned aerial vehicles (UAVs) for a human-centered active sensing of wildfires. We develop a dual-criterion objective function based on Kalman uncertainty residual propagation and weighted multi-agent consensus protocol, which enables the UAVs to actively infer the wildfire dynamics and parameters, track and monitor the fire transition, and safely manage human firefighters on the ground using acquired information. We evaluate our approach relative to prior work, showing significant improvements by reducing the environment's cumulative uncertainty residual by more than $ 10^2 $ and $ 10^5 $ times in firefront coverage performance to support human-robot teaming for firefighting. We also demonstrate our method on physical robots in a mock firefighting exercise.