# A Distributed Control Framework for a Team of Unmanned Aerial Vehicles for Dynamic Wildfire Tracking

arXiv: https://arxiv.org/abs/1704.02630 · HTML: https://mlsanity.com/paper/1704.02630/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2017.

## Built on by (most cited first, 4 of 4)

- [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)
- [Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees](https://mlsanity.com/paper/2206.10544/) (2022)
- [Decentralized Consensus Inference-based Hierarchical Reinforcement Learning for Multi-Constrained UAV Pursuit-Evasion Game](https://mlsanity.com/paper/2506.18126/) (2025)
- [Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter](https://mlsanity.com/paper/2606.28441/) (2026)

## Abstract (arXiv)

Wildland fire fighting is a very dangerous job, and the lack of information of the fire front is one of main reasons that causes many accidents. Using unmanned aerial vehicle (UAV) to cover wildfire is promising because it can replace human in hazardous fire tracking and save operation costs significantly. In this paper we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collision as well as cooperate well with other neighbors. Experimental results are conducted to demonstrate the capabilites of the UAV team in covering a spreading wildfire.