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

arXiv: https://arxiv.org/abs/2006.07969 · HTML: https://mlsanity.com/paper/2006.07969/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management](https://mlsanity.com/paper/2401.02456/) (2024)
- [Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming](https://mlsanity.com/paper/2201.08484/) (2022)
- [Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees](https://mlsanity.com/paper/2206.10544/) (2022)
- [WIT-UAS: A Wildland-fire Infrared Thermal Dataset to Detect Crew Assets From Aerial Views](https://mlsanity.com/paper/2312.09159/) (2023)
- [Towards Sample-efficient Apprenticeship Learning from Suboptimal Demonstration](https://mlsanity.com/paper/2110.04347/) (2021)
- [ARDDQN: Attention Recurrent Double Deep Q-Network for UAV Coverage Path Planning and Data Harvesting](https://mlsanity.com/paper/2405.11013/) (2024)
- [Towards In-Vehicle Multi-Task Facial Attribute Recognition: Investigating Synthetic Data and Vision Foundation Models](https://mlsanity.com/paper/2403.06088/) (2024)
- [Realtime Dynamic Gaze Target Tracking and Depth-Level Estimation](https://mlsanity.com/paper/2406.18595/) (2024)

## Abstract (arXiv)

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  102  and  105  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.